output feedback nn control for two classes of discrete time systems with unknown control directions in a unified approach in this paper output feedback adaptive neural network nn controls are investigated for two classes of nonlinear discrete time systems with unknown control directions 1 nonlinear pure feedback systems and 2 nonlinear autoregressive moving average with exogenous inputs narmax systems to overcome the noncausal problem which has been known to be a major obstacle in the discrete time control design both systems are transformed to a predictor for output feedback control design implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input the problem of lacking a priori knowledge on the control directions is solved by using discrete nussbaum gain the high order neural network honn is employed to approximate the unknown control the closed loop system achieves semiglobal uniformly ultimately bounded sguub stability and the output tracking error is made within a neighborhood around zero simulation results are presented to demonstrate the effectiveness of the proposed control

asymptotic fuzzy tracking control for a class of stochastic strict feedback systems this paper presents an asymptotic tracking control design method for stochastic strict feedback systems via fuzzy logic systems in the existing results stochastic controls are usually limited to be bounded in probability however how to realize the asymptotic tracking control for stochastic strict feedback systems remains a control dilemma this paper achieves the asymptotic tracking control by proposing a novel gain suppressing inequality approach specifically the three part construction is performed to achieve such control objective for stochastic systems first the novel gain suppressing inequality technique is developed to lay the foundation for carrying out the lyapunov stability analysis second the developed inequality technique is integrated with each step of the backstepping based adaptive control design procedure third analyses are provided to realize the asymptotic tracking control of stochastic strict feedback systems

moving horizon estimation for markov jump systems in this paper we study the state estimation problem for a class of nonlinear markov jump systems with the moving horizon estimation algorithm which is an optimization based filtering method the optimal estimate is obtained by minimizing a quadratic estimation cost function defined on fixed sliding horizon measurements for this purpose the quadratic estimation cost function is formulated from the negative logarithm of the joint states distribution whose particular factorization is represented by a bayesian network by analyzing the corresponding full information estimation as a basic problem for nonlinear markov jump systems the formulation of the moving horizon estimation is developed an example is presented to compare the proposed new estimation technique with classical interacting multiple model particle filtering and show the effectiveness and advantages of the new estimation scheme

on the design of mimo block fading channels with feedback link capacity constraint in this paper we propose a combined adaptive power control and beamforming framework for optimizing multiple input multiple output mimo link capacity in the presence of feedback link capacity constraint the feedback channel is used to carry channel state information only it is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block we show that the hybrid design could achieve the optimal mimo link capacity and we derive a computationally efficient algorithm to search for the optimal design under a specific average power constraint finally we shall illustrate that a minimum mean square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal capacity we found that feedback effectively enhances the forward channel capacity for all signal to noise ratio snr values when the number of transmit antennas n sub t is larger than the number of receive antennas n sub r the snr gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling the former factor contributed at most 10log sub 10 n sub t n sub r db snr gain when n sub t n sub r while the latter factor s snr gain is significant only for low snr values

approximation based control of nonlinear mimo time delay systems approximation based control is presented for a class of multi input multi output mimo nonlinear systems in block triangular form with unknown state delays neural networks nns are utilized to approximate and compensate for unknown functions in the system dynamics including the unknown bounds of the functions of delayed states the use of a separation technique removes the need for any assumption on the function of delayed states and allows the handling of multiple delays in each function of delayed states by combining the use of lyapunov krasovskii functionals and adaptive nn backstepping the proposed control guarantees that all closed loop signals remain bounded while the outputs converge to a neighborhood of the desired trajectories simulation results demonstrate the effectiveness of the proposed scheme

adaptive fuzzy control of nonlinear systems in pure feedback form based on input to state stability using mean value theorem and backstepping technique a robust adaptive fuzzy control scheme is proposed for a class of pure feedback nonlinear systems with unknown dead zone and disturbances via input to state stability takagi sugeno t s type fuzzy logic systems are used to approximate the uncertain nonlinear functions and fewer learning parameters need to be adjusted online based on small gain theorem the closed loop control system is proven to be semiglobally uniformly ultimately bounded and the tracking error converges to a neighborhood of zero by choosing appropriate parameters simulation results demonstrate the effectiveness of the control scheme

robust h control for linear markovian jump systems with unknown nonlinearities this paper studies the problem of stochastic stability and disturbance attenuation for a class of linear continuous time uncertain systems with markovian jumping parameters the uncertainties are assumed to be nonlinear and state control and external disturbance dependent a sufficient condition is provided to solve the above problem an h controller is designed such that the resulting closed loop system is stochastically stable and has a disturbance attenuation for all admissible uncertainties it is shown that the control law is in terms of the solutions of a set of coupled riccati inequalities a numerical example is included to demonstrate the potential of the proposed technique

robust h sup spl infin filtering for uncertain markovian jump systems with mode dependent time delays this paper considers the problem of robust h sup spl infin filtering for uncertain markovian jump linear systems with time delays which are time varying and depend on the system mode the parameter uncertainties are time varying norm bounded the aim of this problem is to design a markovian jump linear filter that ensures robust exponential mean square stability of the filtering error system and a prescribed l sub 2 induced gain from the noise signals to the estimation error for all admissible uncertainties a sufficient condition for the solvability of this problem is obtained the desired filter can be constructed by solving a set of linear matrix inequalities an illustrative numerical example is provided to demonstrate the effectiveness of the proposed approach

locally trivial principal homogeneous spaces and conjugacy theorems for lie algebras abstract we link locally trivial principal homogeneous spaces over spec r to the question of conjugacy of maximal abelian diagonalizable subalgebras of g r

adaptive fuzzy identification and control for a class of nonlinear pure feedback mimo systems with unknown dead zones the adaptive fuzzy identification and control problems are considered for a class of multi input multi output nonlinear systems with unknown functions and unknown dead zone inputs the main characteristics of the considered systems are that 1 they are composed of n subsystems and each subsystem is in nested lower triangular form 2 dead zone inputs are in nonsymmetric nonlinear form and 3 dead zone inputs appear nonlinearly in the systems and their parameters are not required to be known the controller design for this class of systems is a difficult and complicated task because of the existences of unknown functions the couplings among the nested subsystems and the dead zone inputs in the controller design the fuzzy logic systems are employed to approximate the unknown functions and the differential mean value theorem is used to separate dead zone inputs to compensate for dead zone inputs the compensative terms are designed in the controllers the stability of the closed loop system is proved via the lyapunov stability theorem a simulation example is provided to validate the feasibility of the approach

global asymptotic stability of recurrent neural networks with multiple time varying delays in this paper several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time varying delays the lyapunov krasovskii stability theory for functional differential equations and the linear matrix inequality lmi approach are employed in our investigation the results are shown to be generalizations of some previously published results and are less conservative than existing results the present results are also applied to recurrent neural networks with constant time delays

adaptive quantization on a grassmann manifold for limited feedback beamforming systems in this paper we examine delay limited adaptive quantization on the grassmann manifold of 1 dimensional subspaces in n dimensional space due to strict delay limits vector quantization over multiple time instants cannot be applied to exploit the temporal correlation of the source signal instead a vector predictive quantizer is proposed that combines prediction and differential quantization algorithms to achieve an efficient quantization of the correlated grassmannian source the proposed predictor is based on adaptive finite impulse response filters to adjust to the temporal statistics of the source signal it is shown that the prediction error in the tangent space associated with the grassmann manifold behaves approximately gaussian provided its norm is sufficiently small the proposed quantization algorithm is applied to channel state information quantization in multi user beamforming wireless communication systems large throughput gains are demonstrated in comparison to memoryless quantization due to reduced multi user interference

adaptive fuzzy control for a class of nonlinear discrete time systems with backlash an adaptive fuzzy controller design is studied for uncertain nonlinear systems in this paper the considered systems are of the discrete time form in a triangular structure and include the backlash and the external disturbance by using the prediction function of future states the systems are transformed into an n step ahead predictor the fuzzy logic systems flss are used to approximate the unknown functions unknown backlash and backlash inversion respectively a discrete time tuning algorithm is developed to estimate the optimal fuzzy parameters compared with the previous works for the discrete time systems with backlash the main contributions of the paper are that 1 the rigorous restriction for the functional estimation error is removed and 2 the external disturbance is bounded but the bound is not required to be known a novel controller and the adaptation laws are constructed by using the discrete taylor series expansion and the difference lyapunov analysis and thus those limitations in the previous works are overcome it is proven that all the signals in the closed loop system are bounded and that the system output can be to follow the reference signal to a bounded compact set a simulation example is provided to illustrate the effectiveness of the proposed approach

robust backstepping control for a class of time delayed systems in this note the problem of robust output feedback control for a class of nonlinear time delayed systems is considered the systems considered are in strict feedback form state observer is first designed then based on the observed states the controller is designed via backstepping method both the designed observer and controller are independent of the time delays based on lyapunov stability theory we prove that the constructed controller can render the closed loop system asymptotically stable simulation results further verify the effectiveness of the proposed approach

fuzzy adaptive control with state observer for a class of nonlinear discrete time systems with input constraint in this paper an adaptive fuzzy controller is constructed for a class of nonlinear discrete time systems with unknown functions and bounded disturbances the main characteristics of the systems are that they take into account the effect of discrete time dead zone and the system states are not required to be measurable the stability problem of this class of systems is for the first time to be addressed in this paper due to the unavailability of the states and the presence of the discrete time dead zone the controller design becomes more difficult to stabilize the uncertain nonlinear discrete time systems the fuzzy logic systems are used to approximate the unknown functions a fuzzy state observer is designed to estimate the immeasurable states and the effect caused by discrete time dead zone can be solved via establishing an adaptation auxiliary signal based on the lyapunov approach it is proved that all the signals of the closed loop system are the semiglobal uniformly ultimately bounded and the tracking error is made within a small neighborhood around zero the feasibility of the developed control scheme is verified via two simulation examples

the capacity region of the gaussian multiple input multiple output broadcast channel the gaussian multiple input multiple output mimo broadcast channel bc is considered the dirty paper coding dpc rate region is shown to coincide with the capacity region to that end a new notion of an enhanced broadcast channel is introduced and is used jointly with the entropy power inequality to show that a superposition of gaussian codes is optimal for the degraded vector broadcast channel and that dpc is optimal for the nondegraded case furthermore the capacity region is characterized under a wide range of input constraints accounting as special cases for the total power and the per antenna power constraints

novel neural control for a class of uncertain pure feedback systems this paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure feedback nonlinear systems using the implicit function theorem and backstepping technique a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters in contrast to conventional lyapunov based design techniques an alternative lyapunov function is constructed for the development of control law and learning algorithms differing from the existing results in the literature the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures furthermore the scheme requires the desired trajectory and its first derivative rather than its first n derivatives in addition the useful property of the basis function of the radial basis function which will be used in control design is explored simulation results illustrate the effectiveness of the proposed techniques

adaptive neural network tracking control of mimo nonlinear systems with unknown dead zones and control directions in this paper adaptive neural network nn tracking control is investigated for a class of uncertain multiple input multiple output mimo nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions the design is based on the principle of sliding mode control and the use of nussbaum type functions in solving the problem of the completely unknown control directions it is shown that the dead zone output can be represented as a simple linear system with a static time varying gain and bounded disturbance by introducing characteristic function by utilizing the integral type lyapunov function and introducing an adaptive compensation term for the upper bound of the optimal approximation error and the dead zone disturbance the closed loop control system is proved to be semiglobally uniformly ultimately bounded with tracking errors converging to zero under the condition that the slopes of unknown dead zones are equal simulation results demonstrate the effectiveness of the approach

adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics in this paper the problems of stability and tracking control for a class of large scale nonlinear systems with unmodeled dynamics are addressed by designing the decentralized adaptive fuzzy output feedback approach because the dynamic surface control technique is introduced the designed controllers can avoid the issue of explosion of complexity which comes from the traditional backstepping design procedure that deals with large scale nonlinear systems with unmodeled dynamics in addition a reduced order observer is designed to estimate those immeasurable states based on the lyapunov stability method it is proven that all the signals in the closed loop system are bounded and the system outputs track the reference signals to a small neighborhood of the origin by choosing the design parameters appropriately the simulation examples are given to verify the effectiveness of the proposed techniques

brief paper sliding mode control for ito stochastic systems with markovian switching this paper deals with the problem of sliding mode control smc for a class of nonlinear uncertain stochastic systems with markovian switching by introducing some specified matrices the connections among the designed sliding surfaces corresponding to every mode are established furthermore the present sliding mode controller including the transition rates of modes can cope with the effect of markovian switching by means of linear matrix inequalities lmis with equality constraint sufficient conditions are derived such that the sliding motions on the specified sliding surfaces are stochastically stable with c disturbance attenuation level finally a numerical example is given to illustrate the applicability of the present method

adaptive nn controller design for a class of nonlinear mimo discrete time systems an adaptive neural network tracking control is studied for a class of multiple input multiple output mimo nonlinear systems the studied systems are in discrete time form and the discretized dead zone inputs are considered in addition the studied mimo systems are composed of n subsystems and each subsystem contains unknown functions and external disturbance due to the complicated framework of the discrete time systems the existence of the dead zone and the noncausal problem in discrete time it brings about difficulties for controlling such a class of systems to overcome the noncausal problem by defining the coordinate transformations the studied systems are transformed into a special form which is suitable for the backstepping design the radial basis functions nns are utilized to approximate the unknown functions of the systems the adaptation laws and the controllers are designed based on the transformed systems by using the lyapunov method it is proved that the closed loop system is stable in the sense that the semiglobally uniformly ultimately bounded of all the signals and the tracking errors converge to a bounded compact set the simulation examples and the comparisons with previous approaches are provided to illustrate the effectiveness of the proposed control algorithm

hybrid fuzzy model based control of nonholonomic systems a unified viewpoint this paper proposes a unified hybrid fuzzy model based control scheme for uncertain nonholonomic systems compared with typical hybrid fuzzy control the stability analysis is performed based on a new concept of constructing a semicommon lyapunov function and a new definition called as exponential like model following this advancement provides a strict stability analysis but results in relaxed gain conditions in detail a unified hybrid takagi sugeno fuzzy model is first introduced for representing well known nonholonomic systems with a momentum conservation constraint or a no slip constraint then the hybrid fuzzy controller is derived to ensure robust nonlinear model following control i e an asymptotic convergence with adjustable ultimate bound and arbitrary disturbance attenuation in an gain sense furthermore an iterative linear matrix inequality technique is proposed to guarantee the stability and avoid the need of a common positive definite matrix finally the applications are carried out on a hopping robot and a car like mobile robot numerical simulations and experiment results show the expected performances

adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators recently through the use of parameterized fuzzy approximators various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood an important class of parameterized fuzzy approximators is constructed using radial basis function rbf as a membership function however some tuneable parameters in rbf appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task in this paper we propose a new adaptive control method in an effort to tune all the rbf parameters thereby reducing the approximation error and improving control performance global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller simulations performed on a simple nonlinear system illustrate the approach

fuzzy adaptive output feedback fault tolerant tracking control of a class of uncertain nonlinear systems with nonaffine nonlinear faults this paper studies the fuzzy adaptive output feedback fault tolerant control ftc problem for a class of single input and single output uncertain nonlinear systems with time varying nonaffine nonlinear faults in strict feedback form in the design procedure filtered signals are adopted to circumvent algebraic loop problems on implementing the usual controllers by using fuzzy logic systems to approximate the unknown nonlinearity effects and changes in model dynamics due to faults a fuzzy state observer is first presented to estimate the unmeasured states based on the online estimating information from the adaptive mechanism an observer based dynamic output feedback fault tolerant controller is designed via the backstepping technique it is shown that the stability and tracking performances of the closed loop system can be achieved even in the presence of unknown nonlinear faults in comparison with the existing approaches the ftc scheme can handle the nonaffine nonlinear faults effectively finally a simulation example is included to validate the advantages of the proposed approaches

observer based tracking control for mimo pure feedback nonlinear systems with time delay and input quantisation abstractin addressing the adaptive neural backstepping control for multiple input and multiple output nonlinear systems in pure feedback form with time delay and input quantisation we construct a high gain state observer and an output feedback adaptive control scheme using backstepping method with neural networks to estimate the uncertain nonlinear functions then we propose an output feedback neural controller that ensures all the state trajectories in the time delay quantised nonlinear systems are ultimately bounded with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser an illustrative example is presented to show the applicability of the new control method developed

duality achievable rates and sum rate capacity of gaussian mimo broadcast channels we consider a multiuser multiple input multiple output mimo gaussian broadcast channel bc where the transmitter and receivers have multiple antennas since the mimo bc is in general a nondegraded bc its capacity region remains an unsolved problem we establish a duality between what is termed the dirty paper achievable region the caire shamai see proc ieee int symp information theory washington dc june 2001 p 322 achievable region for the mimo bc and the capacity region of the mimo multiple access channel mac which is easy to compute using this duality we greatly reduce the computational complexity required for obtaining the dirty paper achievable region for the mimo bc we also show that the dirty paper achievable region achieves the sum rate capacity of the mimo bc by establishing that the maximum sum rate of this region equals an upper bound on the sum rate of the mimo bc

technical communique discrete adaptive sliding mode control of a class of stochastic systems this paper presents a discrete adaptive sliding mode controller for a class of linear systems with coloured noise the systems are assumed to be minimum phase and the adaptive controller is developed using an input output model it is shown that the controller ensures system stability and that the mean square deviation from the sliding surface is minimized simulation results are presented to illustrate the features of the adaptive controller

fuzzy tracking adaptive control of discrete time switched nonlinear systems this paper is concerned with the problem of fuzzy tracking adaptive control for a class of discrete time switched uncertain nonlinear systems with arbitrary switching based on the common lyapunov function method and by utilizing the fuzzy logic systems to approximate the unknown nonlinear functions an adaptive fuzzy controller is firstly constructed for a class of uncertain nonlinear discrete time switched systems meanwhile the proposed adaptive control algorithm reduces the amount of online adjustable parameters based on the above techniques the constructed fuzzy controllers and the adaptive law can guarantee that all the signals are bounded and the system output can converge to a small neighborhood of the reference signal in the closed loop system an illustrative example is provided to demonstrate the effectiveness of the proposed approaches

how much does transmit correlation affect the sum rate scaling of mimo gaussian broadcast channels this paper considers the effect of spatial correlation between transmit antennas on the sum rate capacity of the mimo gaussian broadcast channel i e downlink of a cellular system specifically for a system with a large number of users n we analyze the scaling laws of the sum rate for the dirty paper coding and for different types of beamforming transmission schemes when the channel is i i d it has been shown that for large n the sum rate is equal to m log log n m log p m o 1 where m is the number of transmit antennas p is the average signal to noise ratio and o 1 refers to terms that go to zero as n rarr infin when the channel exhibits some spatial correlation with a covariance matrix r non singular with tr r m we prove that the sum rate of dirty paper coding is m log log n m log p m log det r o 1 we further show that the sum rate of various beamforming schemes achieves m log log n m log p m m log c o 1 where c les 1 depends on the type of beamforming we can in fact compute c for random beamforming proposed in and more generally for random beamforming with preceding in which beams are pre multiplied by a fixed matrix simulation results are presented at the end of the paper

observer based adaptive neural network control for nonlinear systems in nonstrict feedback form this paper focuses on the problem of adaptive neural network nn control for a class of nonlinear nonstrict feedback systems via output feedback a novel adaptive nn backstepping output feedback control approach is first proposed for nonlinear nonstrict feedback systems the monotonicity of system bounding functions and the structure character of radial basis function rbf nns are used to overcome the difficulties that arise from nonstrict feedback structure a state observer is constructed to estimate the immeasurable state variables by combining adaptive backstepping technique with approximation capability of radial basis function nns an output feedback adaptive nn controller is designed through backstepping approach it is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed loop systems two examples are used to illustrate the effectiveness of the proposed approach

stochastic adaptive backstepping controller design by introducing dynamic signal and changing supply function a more general class of stochastic non linear systems with unmodelled dynamics and uncertain non linear functions are considered in this paper with the concept of isps being extended to stochastic case by combining changing supply function technique with backstepping design technique an adaptive output feedback controller is proposed it is shown that all the solutions of the closed loop system are uniformly bounded in probability and the output can be regulated to an arbitrarily small neighbourhood of the origin in probability a simulation example demonstrates the control scheme

on conjugacy of cartan subalgebras in extended affine lie algebras abstract that finite dimensional simple lie algebras over the complex numbers can be classified by means of purely combinatorial and geometric objects such as coxeter dynkin diagrams and indecomposable irreducible root systems is arguably one of the most elegant results in mathematics the definition of the root system is done by fixing a cartan subalgebra of the given lie algebra the remarkable fact is that up to isomorphism this construction is independent of the choice of the cartan subalgebra the modern way of establishing this fact is by showing that all cartan subalgebras are conjugate for symmetrizable kac moody lie algebras with the appropriate definition of cartan subalgebra conjugacy has been established by peterson and kac an immediate consequence of this result is that the root systems and generalized cartan matrices are invariants of the kac moody lie algebras the purpose of this paper is to establish conjugacy of cartan subalgebras for extended affine lie algebras a natural class of lie algebras that generalizes the finite dimensional simple lie algebra and affine kac moody lie algebras

ensemble properties of rvq based limited feedback beamforming codebooks the ensemble properties of random vector quantization rvq codebooks for limited feedback beamforming in multiinput multioutput mimo systems are studied with the metrics of interest being the received ssr snr loss and mutual information loss both relative to a perfect channel state information csi benchmark the simplest case of unskewed codebooks is first studied in the correlated mimo setting and these loss metrics are computed as a function of the number of bits of feedback b transmit antenna dimension nt and spatial correlation in particular it is established that 1 the loss metrics are a product of two components a quantization component and a channel dependent component 2 the quantization component which is also common to analysis of channels with i i d fading decays as b increases at the rate 2 b nt 1 3 the channel dependent component reflects the condition number of the channel further the precise connection between the received ssr snr loss and the squared singular values of the channel is shown to be a schur convex majorization relationship finally the ensemble properties of skewed codebooks that are generated by skewing rvq codebooks with an appropriately designed fixed skewing matrix are studied based on an estimate of the loss expression for skewed codebooks it is established that the structure of a good skewing matrix is critically dependent on the condition numbers of the effective channel product of the true channel and the skewing matrix and the skewing matrix

adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems an adaptive fuzzy control approach is proposed for a class of multiple input multiple output mimo nonlinear systems with completely unknown nonaffine functions the mimo systems are composed of n subsystems and each of subsystems is in the nested lower triangular form it is difficult and complicated to control this class of systems due to the existence of unknown nonaffine functions and the couplings among the nested subsystems this difficulty is overcome by introducing some special type lyapunov functions and taking advantage of the mean value theorem the backstepping design method and the approximation property of the fuzzy systems the proposed control approach can guarantee that all the signals in the closed loop system are bounded a simulation experiment is utilized to verify the feasibility of the proposed approach

adaptive backstepping controller design using stochastic small gain theorem a more general class of stochastic nonlinear systems with unmodeled dynamics and uncertain nonlinear functions are considered in this paper with the concept of input to state practical stability isps and nonlinear small gain theorem being extended to stochastic case by combining stochastic small gain theorem with backstepping design technique an adaptive output feedback controller is proposed it is shown that the closed loop system is practically stable in probability a simulation example demonstrates the control scheme

control of markovian jump discrete time systems with norm bounded uncertainty and unknown delay this paper studies the problem of control for discrete time delay linear systems with markovian jump parameters the system under consideration is subjected to both time varying norm bounded parameter uncertainty and unknown time delay in the state and markovian jump parameters in all system matrices we address the problem of robust state feedback control in which both robust stochastic stability and a prescribed h sub spl infin performance are required to be achieved irrespective of the uncertainty and time delay it is shown that the above problem can be solved if a set of coupled linear matrix inequalities has a solution

reinforcement learning design based adaptive tracking control with less learning parameters for nonlinear discrete time mimo systems based on the neural network nn approximator an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output mimo nonlinear discrete time systems with unknown functions and disturbances in the design procedure two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function an optimal control signal and adaptation laws can be generated based on two nns in the previous approaches the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design consequently compared with the existing results the main contributions of this paper are 1 only two parameters are needed to be adjusted and thus the number of the adaptation laws is smaller than the previous results and 2 the updating parameters do not depend on the number of the subsystems for mimo systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks it is proven that the tracking errors the adaptation laws and the control inputs are uniformly bounded using lyapunov analysis method the simulation examples are employed to illustrate the effectiveness of the proposed algorithm

new delay dependent stability criteria for neural networks with time varying delay in this letter a new method is proposed for stability analysis of neural networks nns with a time varying delay some less conservative delay dependent stability criteria are established by considering the additional useful terms which were ignored in previous methods when estimating the upper bound of the derivative of lyapunov functionals and introducing the new free weighting matrices numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

memoryless state feedback controller design for time delay systems with matched uncertain nonlinearities the robust control problem for a class of uncertain time delay systems is investigated the systems include multiple time delays and uncertain nonlinear functions to our best knowledge no controller design method for the systems is reported in the literature except for sliding mode control smc however the sliding mode controller must use the delayed state of the system so that the precise time delays have to be known and also a large memory is needed for controller implementation to overcome the shortcomings we employ the backstepping method to design a memoryless state feedback controller by constructing a novel lyapunov krasovskii functional we show that the resulting closed loop system is asymptotically stable simulations are done to verify the effectiveness of the proposed method

neural network based near optimal control for a class of discrete time affine nonlinear systems with control constraints in this paper the near optimal control problem for a class of nonlinear discrete time systems with control constraints is solved by iterative adaptive dynamic programming algorithm first a novel nonquadratic performance functional is introduced to overcome the control constraints and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis in the present control scheme there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme

transmit antenna selection for multiple input multiple output spatial modulation systems the benefits of transmit antenna selection tas invoked for spatial modulation sm aided multiple input multiple output mimo systems are investigated specifically we commence with a brief review of the existing tas algorithms and focus on the recently proposed euclidean distance based tas ed tas schemes due to their high diversity gain then a pair of novel ed tas algorithms termed as the improved qr decomposition qrd based tas qrd tas and the error vector magnitude based tas evm tas are proposed which exhibit an attractive system performance at low complexity moreover the proposed ed tas algorithms are amalgamated with the low complexity yet efficient power allocation pa technique termed as tas pa for the sake of further improving the system s performance our simulation results show that the proposed tas pa algorithms achieve signal to noise ratio snr gains of up to 9 db over the conventional tas algorithms and up to 6 db over the tas pa algorithm designed for spatial multiplexing systems

fuzzy approximation based global adaptive control for uncertain strict feedback systems with a priori known tracking accuracy abstract in this study we propose a novel adaptive backstepping fuzzy control scheme for a class of uncertain strict feedback systems where the tracking accuracy is known a priori and we also introduce a multiswitching based adaptive fuzzy controller compared with the existing method for adaptive fuzzy control the advantages of the proposed scheme are as follows first the controller guarantees that all the closed loop signals are globally uniformly ultimately bounded which differs from most existing adaptive fuzzy control approaches where the semi global boundedness of the closed loop signals is ensured under a harsh assumption on the approximation domain of the fuzzy logic system second our controller ensures that the tracking error converges to an accuracy that is given a priori for the uncertain strict feedback system which cannot be achieved using existing adaptive fuzzy control methods third based on some nonnegative functions we analyze the convergence of the tracking error using barbalat s lemma fourth the main technical novelty is the construction of three new n th order continuously differentiable switching functions which are used to design the desired controller finally three simulation examples are provided that illustrate the effectiveness of the proposed control strategy

fuzzy approximation based adaptive control of nonlinear delayed systems with unknown dead zone this paper focuses on the problem of adaptive fuzzy control for a class of nonlinear systems without a strict feedback form a new adaptive fuzzy control scheme is proposed for nonlinear delayed systems with unknown dead zone nonlinearity control design is achieved by introducing a variable separation approach and employing the unique structural property of fuzzy logic systems the proposed adaptive fuzzy controller guarantees that all the closed loop signals are semiglobally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin two simulation examples illustrate the effectiveness of the proposed approach

adaptive fuzzy control for synchronization of nonlinear teleoperators with stochastic time varying communication delays in this paper adaptive fuzzy control is investigated for nonlinear teleoperators with time delays which ensures synchronization of positions and velocities of the master and slave manipulators and does not rely on the use of the scattering transformation compared with the previous passivity framework the communication delays are assumed to be stochastic time varying by feedback linearization the nonlinear dynamics of the teleoperation system is transformed into two subsystems local master slave position control with unmodeled dynamics and delayed motion synchronization then based on linear matrix inequalities lmi and markov jump linear systems adaptive fuzzy control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems it is proven that the master slave teleoperation system is stochastically stable in mean square under specific lmi conditions and all the signals of the resulting closed loop system are uniformly bounded finally the extensive simulations are performed to show the effectiveness of the proposed method

a vector perturbation technique for near capacity multiantenna multiuser communication part ii perturbation recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity we introduce a simple encoding algorithm that achieves near capacity at sum rates of tens of bits channel use the algorithm is a variation on channel inversion that regularizes the inverse and uses a sphere encoder to perturb the data to reduce the energy of the transmitted signal the paper is comprised of two parts in this second part we show that after the regularization of the channel inverse introduced in the first part a certain perturbation of the data using a sphere encoder can be chosen to further reduce the energy of the transmitted signal the performance difference with and without this perturbation is shown to be dramatic with the perturbation we achieve excellent performance at all signal to noise ratios the results of both uncoded and turbo coded simulations are presented

adaptive fuzzy control with guaranteed convergence of optimal approximation error with no a priori knowledge of plant boundary functions a novel direct adaptive fuzzy controller afc for a class of single input single output siso uncertain affine nonlinear systems is developed in this paper based on the theory of fuzzy logic systems flss with variable universes of discourse uds sufficient conditions that guarantee that the optimal fuzzy approximation error fae is locally convergent are given by the use of the output tracking error and its derivatives as input variables and by the selection of suitable adjusting parameters a variable ud fls with an optimal fae local convergence is constructed and its parameter adaptive law is derived by virtue of the lyapunov stability theorem under the assumption that the optimal fae is bounded it is proved that the closed loop system is asymptotically stable in the sense that all variables are uniformly ultimately bounded and that the tracking errors converge to zero the proposed approach eliminates the influence of the fae on the tracking errors by means of the inherent mechanism of the variable ud fls thus it has the potential to achieve high control performance without additional compensation under only a few fuzzy rules simulation studies demonstrate the superiority of the proposed afc in terms of the settling time tracking accuracy smoothness of the control input and robustness against external disturbances and parameter variations

zero forcing precoding and generalized inverses we consider the problem of linear zero forcing precoding design and discuss its relation to the theory of generalized inverses in linear algebra special attention is given to a specific generalized inverse known as the pseudo inverse we begin with the standard design under the assumption of a total power constraint and prove that precoders based on the pseudo inverse are optimal among the generalized inverses in this setting then we proceed to examine individual per antenna power constraints in this case the pseudo inverse is not necessarily the optimal inverse in fact finding the optimal matrix is nontrivial and depends on the specific performance measure we address two common criteria fairness and throughput and show that the optimal generalized inverses may be found using standard convex optimization methods we demonstrate the improved performance offered by our approach using computer simulations

a combined backstepping and stochastic small gain approach to robust adaptive fuzzy output feedback control in this paper an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict feedback systems without the requirement of states measurement the stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties unknown nonlinear functions and in the presence of unmodeled dynamics dynamics disturbances fuzzy logic systems are used to approximate the unstructured uncertainties and a fuzzy state observer is designed to estimate the unmeasured states by combining the backstepping design technique with the stochastic small gain approach a new adaptive fuzzy output feedback control approach is developed it is proved that the proposed control approach can guarantee that the closed loop system is input state practically stability isps in probability and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance in addition the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties

zero forcing methods for downlink spatial multiplexing in multiuser mimo channels the use of space division multiple access sdma in the downlink of a multiuser multiple input multiple output mimo wireless communications network can provide a substantial gain in system throughput the challenge in such multiuser systems is designing transmit vectors while considering the co channel interference of other users typical optimization problems of interest include the capacity problem maximizing the sum information rate subject to a power constraint or the power control problem minimizing transmitted power such that a certain quality of service metric for each user is met neither of these problems possess closed form solutions for the general multiuser mimo channel but the imposition of certain constraints can lead to closed form solutions this paper presents two such constrained solutions the first referred to as block diagonalization is a generalization of channel inversion when there are multiple antennas at each receiver it is easily adapted to optimize for either maximum transmission rate or minimum power and approaches the optimal solution at high snr the second known as successive optimization is an alternative method for solving the power minimization problem one user at a time and it yields superior results in some e g low snr situations both of these algorithms are limited to cases where the transmitter has more antennas than all receive antennas combined in order to accommodate more general scenarios we also propose a framework for coordinated transmitter receiver processing that generalizes the two algorithms to cases involving more receive than transmit antennas while the proposed algorithms are suboptimal they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity

on designing of sliding mode control for stochastic jump systems in this note we consider the problems of stochastic stability and sliding mode control for a class of linear continuous time systems with stochastic jumps in which the jumping parameters are modeled as a continuous time discrete state homogeneous markov process with right continuous trajectories taking values in a finite set by using linear matrix inequalities lmis approach sufficient conditions are proposed to guarantee the stochastic stability of the underlying system then a reaching motion controller is designed such that the resulting closed loop system can be driven onto the desired sliding surface in a limited time it has been shown that the sliding mode control problem for the markovian jump systems is solvable if a set of coupled lmis have solutions a numerical example is given to show the potential of the proposed techniques

fuzzy approximation based adaptive backstepping optimal control for a class of nonlinear discrete time systems with dead zone in this paper an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete time systems the controlled systems are in a strict feedback frame and contain unknown functions and nonsymmetric dead zone for this class of systems the control objective is to design a controller which not only guarantees the stability of the systems but achieves the optimal control performance as well this immediately brings about the difficulties in the controller design to this end the fuzzy logic systems are employed to approximate the unknown functions in the systems based on the utility functions and the critic designs and by applying the backsteppping design technique a reinforcement learning algorithm is used to develop an optimal control signal the adaptation auxiliary signal for unknown dead zone parameters is established to compensate for the effect of nonsymmetric dead zone on the control performance and the updating laws are obtained based on the gradient descent rule the stability of the control systems can be proved based on the difference lyapunov function method the feasibility of the proposed control approach is further demonstrated via two simulation examples

adaptive control of a class of nonlinear pure feedback systems using fuzzy backstepping approach a controller is proposed for the robust backstepping control of a class of nonlinear pure feedback systems using fuzzy logic the proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the usual controllers and the approximation errors can be efficiently counteracted by employing smooth robust compensators most importantly the uniform ultimate boundedness of all signals in the closed loop system can be guaranteed and a priori knowledge of the plant dynamics is no longer required furthermore the proposed method can be used for adaptive control of a large class of single input single output nonlinear systems in both strict feedback and pure feedback forms and has great potential in many diverse applications the performance of the proposed approach is demonstrated through three simulation examples including one nonlinear pure feedback and two nonlinear strict feedback systems

transmit beamforming in multiple antenna systems with finite rate feedback a vq based approach this paper investigates quantization methods for feeding back the channel information through a low rate feedback channel in the context of multiple input single output miso systems we propose a new quantizer design criterion for capacity maximization and develop the corresponding iterative vector quantization vq design algorithm the criterion is based on maximizing the mean squared weighted inner product mswip between the optimum and the quantized beamforming vector the performance of systems with quantized beamforming is analyzed for the independent fading case this requires finding the density of the squared inner product between the optimum and the quantized beamforming vector which is obtained by considering a simple approximation of the quantization cell the approximate density function is used to lower bound the capacity loss due to quantization the outage probability and the bit error probability the resulting expressions provide insight into the dependence of the performance of transmit beamforming miso systems on the number of transmit antennas and feedback rate computer simulations support the analytical results and indicate that the lower bounds are quite tight

adaptive tracking for stochastic nonlinear systems with markovian switching the problem of the adaptive tracking for a class of stochastic nonlinear systems with stationary markovian switching is considered in this note an ito formula is proposed for stochastic integral equations with an integral about martingale measure an adaptive backstepping controller is designed such that the closed loop system has a unique solution that is globally bounded in probability and l4 norm of the tracking error converges to an arbitrarily small neighborhood of zero a simulation example demonstrates the efficiency of the proposed scheme

adaptive backstepping controller design for stochastic jump systems in this technical note we improve the results in a paper by shi et al in which problems of stochastic stability and sliding mode control for a class of linear continuous time systems with stochastic jumps were considered however the system considered is switching stochastically between different subsystems the dynamics of the jump system can not stay on each sliding surface of subsystems forever therefore it is difficult to determine whether the closed loop system is stochastically stable in this technical note the backstepping techniques are adopted to overcome the problem in a paper by shi et al the resulting closed loop system is bounded in probability it has been shown that the adaptive control problem for the markovian jump systems is solvable if a set of coupled linear matrix inequalities lmis have solutions a numerical example is given to show the potential of the proposed techniques

grassmannian beamforming for multiple input multiple output wireless systems transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple input multiple output mimo wireless systems unfortunately optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector both are hard to realize in this article a quantized maximum signal to noise ratio snr beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter by using the distribution of the optimal beamforming vector in independent and identically distributed rayleigh fading matrix channels the codebook design problem is solved and related to the problem of grassmannian line packing the proposed design criterion is flexible enough to allow for side constraints on the codebook vectors bounds on the codebook size are derived to guarantee full diversity order results on the density of grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or snr loss monte carlo simulations are presented that compare the probability of error for different quantization strategies

cooperative adaptive fuzzy tracking control for networked unknown nonlinear multiagent systems with time varying actuator faults in this paper the cooperative adaptive fault tolerant fuzzy tracking control caftftc problem of networked high order multiagent with time varying actuator faults is studied and a novel caftftc scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults the leader node is modeled as a higher order nonautonomous nonlinear system it acts as a command generator giving commands only to a small portion of the networked group each follower is assumed to have nonidentical unknown nonlinear dynamics and the communication network is also assumed to be a weighted directed graph with a fixed topology a distributed robust adaptive fuzzy controller is designed for each follower node such that the tracking errors are cooperative uniform ultimate boundedness cuub moreover these controllers are distributed in the sense that the controller designed for each follower node only requires relative state information between itself and its neighbors the adaptive compensation term of the optimal approximation errors and external disturbances is adopted to reduce the effects of the errors and disturbances which removes the assumption that the upper bounds of unknown function approximation errors and disturbances should be known analysis of stability and parameter convergence of the proposed algorithm are conducted that are based on algebraic graph theory and lyapunov theory comparing with results in the literature the caftftc scheme can minimize the time delay between fault occurrence and accommodation and reduce its adverse effect on system performance in addition the ftc scheme requires no additional fault isolation model which is necessary in the traditional active ftc scheme finally an example is provided to validate the theoretical results

stable neural controller design for unknown nonlinear systems using backstepping we propose from an adaptive control perspective a neural controller for a class of unknown minimum phase feedback linearizable nonlinear system with known relative degree the control scheme is based on the backstepping design technique in conjunction with a linearly parametrized neural network structure the resulting controller however moves the complex mechanics involved in a typical backstepping design from off line to online with appropriate choice of the network size and neural basis functions the same controller can be trained online to control different nonlinear plants with the same relative degree with semi global stability as shown by the simple lyapunov analysis meanwhile the controller also preserves some of the performance properties of the standard backstepping controllers simulation results are shown to demonstrate these properties and to compare the neural controller with a standard backstepping controller

a unified approach to adaptive neural control for nonlinear discrete time systems with nonlinear dead zone input in this paper an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete time systems which contain unknown functions unknown dead zone input and unknown control direction different from linear dead zone the dead zone in this paper is a kind of nonlinear dead zone to overcome the noncausal problem which leads to the control scheme infeasible the systems can be transformed into a m step ahead predictor due to nonlinear dead zone appearance the transformed predictor still contains the nonaffine function in addition it is assumed that the gain function of dead zone input and the control direction are unknown these conditions bring about the difficulties and the complicacy in the controller design thus the implicit function theorem is applied to deal with nonaffine dead zone appearance the problem caused by the unknown control direction can be resolved through applying the discrete nussbaum gain and the neural networks are used to approximate the unknown function based on the lyapunov theory all the signals of the resulting closed loop system are proved to be semiglobal uniformly ultimately bounded moreover the tracking error is proved to be regulated to a small neighborhood around zero the feasibility of the proposed approach is demonstrated by a simulation example

neural network control based adaptive learning design for nonlinear systems with full state constraints in order to stabilize a class of uncertain nonlinear strict feedback systems with full state constraints an adaptive neural network control method is investigated in this paper the state constraints are frequently emerged in the real life plants and how to avoid the violation of state constraints is an important task by introducing a barrier lyapunov function blf to every step in a backstepping procedure a novel adaptive backstepping design is well developed to ensure that the full state constraints are not violated at the same time one remarkable feature is that the minimal learning parameters are employed in blf backstepping design by making use of lyapunov analysis we can prove that all the signals in the closed loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output finally a simulation is given to verify the effectiveness of the method

robust integral sliding mode control for uncertain stochastic systems with time varying delay this paper is concerned with sliding mode control for uncertain stochastic systems with time varying delay both time varying parameter uncertainties and an unknown nonlinear function may appear in the controlled system an integral sliding surface is first constructed then by means of linear matrix inequalities lmis a sufficient condition is derived to guarantee the global stochastic stability of the stochastic dynamics in the specified switching surface for all admissible uncertainties the synthesized sliding mode controller guarantees the reachability of the specified sliding surface finally a simulation example is presented to illustrate the proposed method

chaos synchronization of uncertain fractional order chaotic systems with time delay based on adaptive fuzzy sliding mode control this paper proposes an adaptive fuzzy sliding mode control afsmc to synchronize two different uncertain fractional order time delay chaotic systems which are infinite dimensional in nature and time delay is a source of instability because modeling the behavior of dynamical systems by fractional order differential equations has more advantages than integer order modeling the adaptive time delay fuzzy logic system is constructed to approximate the unknown fractional order time delay system functions by using lyapunov stability criterion the free parameters of the adaptive fuzzy controller can be tuned online by output feedback control law and adaptive law the sliding mode design procedure not only guarantees the stability and robustness of the proposed afsmc but it also guarantees that the external disturbance on the synchronization error can be attenuated the simulation example is included to confirm validity and synchronization performance of the advocated design methodology

multivariable gaussian evolving fuzzy modeling system this paper introduces a class of evolving fuzzy rule based system as an approach for multivariable gaussian adaptive fuzzy modeling the system is an evolving takagi sugeno ets functional fuzzy model whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning the fuzzy sets of the rule antecedents are multivariable gaussian membership functions which have been adopted to preserve information between input variable interactions the parameters of the membership functions are estimated by the clustering algorithm a weighted recursive least squares algorithm updates the parameters of the rule consequents experiments considering time series forecasting and nonlinear system identification are performed to evaluate the performance of the approach proposed the multivariable gaussian evolving fuzzy models are compared with alternative evolving fuzzy models and classic models with fixed structures the results suggest that multivariable gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling

infinite flag varieties and conjugacy theorems abstract we study the orbit of a highest weight vector in an integrable highest weight module of the group g associated to a kac moody algebra unk a we obtain applications to the geometric structure of the associated flag varieties and to the algebraic structure of unk a in particular we prove conjugacy theorems for cartan and borel subalgebras of unk a so that the cartan matrix a is an invariant of unk a

a combined direct indirect and variable structure method for robust adaptive control direct methods and indirect methods for the adaptive control of linear plants with unknown parameters are currently well known methods for combining direct and indirect methods have also been suggested in the paper the latter method is combined with the variable structure method new adaptive laws are suggested which overcome the principal drawbacks of the variable structure method while the direct and indirect components of the new method assure stability the variable structure component improves the transient response of the system simulation results are presented to show that the overall system is asymptotically stable in the ideal case and exhibits robustness and improved performance in the presence of perturbations

adaptive fuzzy control for a class of unknown nonlinear dynamical systems in this study we investigate an adaptive fuzzy controller design for a class of nonlinear multi input multi output mimo systems in interconnected form the systems considered comprise n subsystems and an unknown interconnection term is included in every equation for each subsystem the interconnection term is a function of all the states from the first to the n 1 th subsystems moreover the effects of dead zone models are considered in each subsystem of the systems these properties of the systems cause the difficulties and add further complexity to the design in order to overcome these difficulties we use the following methods 1 the fuzzy logic systems are employed to approximate the appropriate unknown functions of the systems 2 a novel backstepping design procedure is constructively designed and 3 compensative adaptation laws are provided to compensate for the effects of the dead zone inputs we show that all the signals in the closed loop system are bounded and that the outputs converge to a compact set by using the lyapunov analysis theorem simulated examples are presented that validate the effectiveness of the approach

universal fuzzy integral sliding mode controllers based on t s fuzzy models this paper addresses the universal fuzzy integral sliding mode controllers problem for continuous time multi input multi output nonlinear systems based on takagi sugeno t s fuzzy models by using the approximation capability of t s fuzzy models the nonlinear systems are expressed by uncertain t s fuzzy models with norm bounded approximation errors a novel fuzzy dynamic integral sliding mode control dismc scheme is then developed for the nonlinear systems based on their t s fuzzy approximation models one of the key features of the new dismc scheme is that the restrictive assumption that all local linear systems share a common input matrix which is required in most existing fuzzy integral sliding mode control ismc approaches is removed furthermore the results of universal fuzzy ismcs for two classes of nonlinear systems along with constructive procedures to obtain the universal fuzzy ismcs are provided respectively finally the advantages and effectiveness of the proposed approaches are illustrated via a numerical example

a new methodology for the online adaptation of fuzzy self structuring controllers in this study a novel fuzzy controller which is able to self design from scratch while working online is proposed the controller does not use the information regarding the differential equations that govern the plant s behavior or any of their bounds the algorithm presented is able to determine the most adequate topology for the fuzzy controller based on the data obtained during the system s normal operation therefore the controller can start operating with an empty set of fuzzy rules and needs no offline training the proposed methodology comprises two phases adaptation of the consequents for every selected topology and online addition of new membership functions mfs some of the main advantages of this method are its robustness under changes on the plant s dynamics good performance in noisy situations and the ability to perform variable selection among a group of candidate variables unlike other online methods the modification of the topology is based on the analysis of the whole operating region of the plant thus providing higher robustness several simulation examples are used to show these features

adaptive control of a class of discrete time mimo nonlinear systems with uncertain couplings in this article adaptive control is investigated for a class of discrete time multi input multi output nonlinear systems in block triangular form with uncertain couplings of delayed states among subsystems future states prediction is carried out to facilitate adaptive control design and auxiliary outputs are introduced to develop a novel compensation mechanism for the uncertain nonlinear couplings by using lyapunov method and ordering signals growth rate it is rigorously proved that all the signals in the whole closed loop systems are globally bounded and the output tracking errors asymptotically converge to zeros the effectiveness of the proposed control is demonstrated in the simulation study

design guidelines for limited feedback in the spatially correlated broadcast channel a linear beamformer design for the broadcast channel where the base station is equipped with n t antennas and signals to m 2 users each with a single antenna and where the vector channels are spatially correlated is considered this problem is of relevance in wireless standardization efforts where two users are simultaneously scheduled instead of the theoretically feasible n t user scheduling to minimize signaling overhead the users are assumed to have perfect channel state information csi whereas the base station has statistical information of the channels in the first part of this work the role of the relevance of feedback is studied via the quantification of the gap in ergodic sum rate between the perfect csi and statistics only extremes in this direction the importance of orthogonality of the dominant eigenmodes of the two users in maximizing the gap is established in the second part of this work in scenarios with a large gap limited feedback beamforming codebooks and a codeword selection metric are designed for the low rate feedback setting the main contribution here is the proposal of a generalized eigenvector codebook and feedback of the codeword index that maximizes an estimate of the signal to interference and noise ratio textsf sinr of each user extensions of this scheme are also proposed for the general n t antenna case with m users where m le n t it is shown via numerical studies that this scheme leads to significant performance improvement across a large family of channels over schemes such as grassmannian random vector quantization single user codebooks with the channel projection metric for codeword selection

adaptive fuzzy logic control of dynamic balance and motion for wheeled inverted pendulums in this paper adaptive fuzzy logic control of dynamic balance and motion is investigated for wheeled inverted pendulums with parametric and functional uncertainties the proposed adaptive fuzzy logic control based on physical properties of wheeled inverted pendulums makes use of a fuzzy logic engine and a systematic online adaptation mechanism to approximate the unknown dynamics based on lyapunov synthesis the fuzzy control ensures that the system outputs track the given bounded reference signals to within a small neighborhood of zero and guarantees semi global uniform boundedness of all closed loop signals the effectiveness of the proposed control is verified through extensive simulations

quantized multimode precoding in spatially correlated multiantenna channels multimode precoding where the number of independent data streams is adapted optimally can be used to maximize the achievable throughput in multiantenna communication systems motivated by standardization efforts embraced by the industry the focus of this work is on systematic precoder design with realistic assumptions on the spatial correlation channel state information csi at the transmitter and the receiver and implementation complexity for the spatial correlation of the channel matrix we assume a general channel model based on physical principles that has been verified by many recent measurement campaigns we also assume a coherent linear minimum mean square error mmse receiver and knowledge of the spatial statistics at the transmitter along with the presence of an ideal low rate feedback link from the receiver to the transmitter the reverse link is used for codebook index feedback and the goal of this work is to construct precoder codebooks adaptable in response to the statistical information such that the achievable throughput is significantly enhanced over that of a fixed nonadaptive independent and identically distributed i i d codebook design we illustrate how a codebook of semiunitary precoder matrices localized around some fixed center on the grassmann manifold can be skewed in response to the spatial correlation via low complexity maps that can rotate and scale submanifolds on the grassmann manifold the skewed codebook in combination with a low complexity statistical power allocation scheme is then shown to bridge the gap in performance between a perfect csi benchmark and an i i d codebook design

a cohomological proof of peterson kacʼs theorem on conjugacy of cartan subalgebras for affine kac moody lie algebras abstract this paper deals with the problem of conjugacy of cartan subalgebras for affine kac moody lie algebras unlike the methods used by peterson and kac our approach is entirely cohomological and geometric it is deeply rooted on the theory of reductive group schemes developed by demazure and grothendieck and on the work of j tits on buildings

on beamforming with finite rate feedback in multiple antenna systems we study a multiple antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations assuming that the transmitter uses the quantized information for beamforming we derive a universal lower bound on the outage probability for any finite set of beamformers the universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel using the bound it is shown that finite information systems approach the perfect information case as t 1 2 sup b t 1 where b is the number of feedback bits and t is the number of transmit antennas the geometrical bounding technique used in the proof of the lower bound also leads to a design criterion for good beamformers whose outage performance approaches the lower bound the design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook and is equivalent to the problem of designing unitary space time codes under certain conditions finally we show that good beamformers are good packings of two dimensional subspaces in a 2t dimensional real grassmannian manifold with chordal distance as the metric