approximation based adaptive fuzzy tracking control for a class of nonstrict feedback stochastic nonlinear time delay systems this paper focuses on the problem of approximation based adaptive fuzzy tracking control for a class of stochastic nonlinear time delay systems with a nonstrict feedback structure a variable separation approach is introduced to overcome the design difficulty from the nonstrict feedback structure mamdani type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique it is shown that the proposed controller guarantees that all signals in the closed loop system are fourth moment semiglobally uniformly ultimately bounded and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value simulation results are provided to demonstrate the effectiveness of our results further developments will consider how to generalize the proposed strategy to nonstrict feedback nonlinear systems with input nonlinearities

on the additive structure of the inverses of banded matrices the additive structure of the inverses of banded matrices is investigated under certain conditions the inverse of a 2k 1 diagonal symmetric banded matrix can be expressed as a sum of k symmetric matrices belonging to the class of inverses of symmetric irreducible tridiagonal matrices in the nonsymmetric case a more complicated structure is obtained applications are mentioned for the resolution of constant coefficient banded linear systems in vlsi models

adaptive fuzzy tracking control for a class of pure feedback nonlinear systems with time varying delay and unknown dead zone abstract this paper investigates the problem of adaptive fuzzy tracking control for a category of pure feedback nonlinear systems with time varying delay and unknown dead zone fuzzy logic systems are used to identify unknown functions existing in systems mean value theorem is introduced to overcome the difficulty arising from the pure feedback structure in designing controller based on the information of dead zone slopes dead zone is handled only one adaptive parameter needs updating online by considering the norm of membership function vectors rather than all sub vectors moreover utilizing the adaptive backstepping technique a novel adaptive fuzzy tracking control scheme is developed to guarantee all signals of the closed loop systems are semi globally uniformly ultimately bounded and the tracking error can be regulated to the origin with a small neighborhood the stability of the closed loop system is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach

fuzzy control turns 50 in 2015 we celebrate the 50th anniversary of fuzzy sets ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in fss see 1 ten years is at the same time a long period and short time thinking to the inner dynamics of research this paper presented for these 50 years of fuzzy sets is taking into account both thoughts a first part presents a quick recap of the history of fuzzy control from model free design based on human reasoning to quasi lpv linear parameter varying model based control design via some milestones and key applications the second part shows where we arrived and what the improvements are since the milestone of the first 40 years a last part is devoted to discussion and possible future research topics

inverses of quasi tridiagonal matrices abstract discretizations in various types of problems lead to quasi tridiagonal matrices in this paper the inverse of a nonsingular quasi tridiagonal matrix is obtained in addition a necessary and sufficient condition for a block matrix to have a quasi tridiagonal inverse is derived

adaptive fuzzy backstepping tracking control for strict feedback systems with input delay this paper investigates the problem of adaptive fuzzy tracking control for nonlinear strict feedback systems with input delay and output constraint input delay is handled based on the information of pade approximation and output constraint problem is solved by barrier lypaunov function some adaptive parameters of the controller need to be updated online through considering the norm of membership function vector instead of all sub vectors a novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed loop systems are semiglobally uniformly ultimately bounded and the tracking error can be adjusted around the origin with a small neighborhood the stability of the closed loop systems is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach

observer based adaptive fuzzy tracking control of nonlinear systems with time delay and input saturation this paper studies the problem of adaptive output tracking control for a class of nonlinear systems subject to unknown time delay and input saturation to address the delay and the input saturation some reasonable assumptions and an auxiliary system are introduced the fuzzy logic approach is employed to approximate the unknown functions in the system an observer is designed to estimate the unavailable system states the dynamic surface control dsc technique is utilized to avoid the problem of explosion of complexity by combining the backstepping and dsc methods a bounded control input is designed to ensure that all signals of the closed loop system are bounded and the tracking error can fluctuate around the origin within a small neighborhood finally two examples are presented to demonstrate the effectiveness and potential of the proposed new design techniques

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

adaptive fuzzy asymptotic control of mimo systems with unknown input coefficients via a robust nussbaum gain based approach this paper proposes an adaptive fuzzy asymptotic control method for multiple input multiple output mimo nonlinear systems with unknown input coefficients with a focus on handling unknown input nonlinearities and control directions for all the existing nussbaum gain based approaches it is difficult to investigate unknown input coefficients problem since multiple time varying coefficients and disturbances coexist and should be simultaneously tackled in the stability analysis to overcome the above difficulty we propose a robust nussbaum gain based approach for the adaptive fuzzy asymptotic control of mimo nonlinear systems benefiting from the proposed nussbaum gain based approach bounded disturbances including unmodeled system dynamics and universal approximation errors are handled furthermore the proposed approach helps extend the bounded fuzzy control result to the asymptotic convergence hence both the control robustness and control accuracy are prompted within the frame of the developed nussbaum gain approach finally a simulation example is carried out to illustrate the effectiveness of the proposed control method

observer based fuzzy control for nonlinear networked systems under unmeasurable premise variables the problem of fuzzy observer based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties the nonlinear networked control systems with parameter uncertainties are modeled through an interval type 2 it2 takagi sugeno t s model in which the uncertainties are handled via lower and upper membership functions the measurement loss occurs randomly both in the sensor to observer and the controller to actuator communication links specially a novel data compensation strategy is adopted in the controller to actuator channel the observer is designed under the unmeasurable premise variables case and then the controller is designed with the estimated states moreover the conditions for the existence of the controller can ensure that the resulting closed loop system is stochastically stable with the predefined disturbance attenuation performance two examples are provided to illustrate the effectiveness of the proposed method

a motion analysis marker based method of determining centre of pressure during two legged hopping the fixed position of force plates has led researchers to pursue alternative methods of determining centre of pressure cop location to date errors reported using alternative methods to the force plate during dynamic tasks have been high the aim of this study was to investigate the accuracy of a motion analysis markerbased system to determine cop during a two legged hopping task five markers were attached to the left and right feet of eight healthy adults 5 females 3 males age 25 072 8 years height 1 7570 07 m mass 71 3711 3 kg multivariate forward stepwise and forced entry linear regression was used with data from five participants to determine cop position during quiet standing and hopping at various frequencies maximum standard error of the estimate of cop position was 12 mm in the anteroposterior direction and 8 mm in the mediolateral cross validation was performed using the remaining 3 participants maximum root mean square difference between the force plate and marker method was 14 mm for mediolateral cop and 20 mm for anteroposterior cop during 1 5 hz hopping differences reduced to a maximum of 7 mm mediolateral and 14 mm anteroposterior for the other frequencies the smallest difference in calculated sagittal plane ankle moment and timing of maximum moment was during 3 0 hz hopping and largest at 1 5 hz results indicate the marker based method of determining cop may be a suitable alternative to a force plate to determine cop position during a two legged hopping task at frequencies greater than 1 5 hz 2014 elsevier ltd all rights reserved

describing gait as a sequence of states traditionally gait analysis has been based on normalizing the stride time to a percentage and then averaging several strides measured under the same conditions this procedure relies on the questionable assumptions that gait is a cyclic movement with superimposed noise and that there is no variability in the timing of activation or in the angles within the stride so no rescaling occurs during the percentage conversion however there is a fluctuation in the timings at which the peak values occur a typical hallmark of this time rescaling is the increase of the joint angle standard deviation when the angular velocity increases the goal of this paper is to present a description of gait to avoid averaging without distorting the original curves in addition it allows the analysis of the fluctuation between consecutive strides in this method it is assumed that gait is quasi periodic the key point is the representation of gait by a state vector that evolves in time this state vector can be used to calculate the instantaneous period and provides a measure of the time fluctuations between strides the sequence of states method describes a quasi periodic movement like gait with a continuous estimate of cycle time and provides measure of the deviations between cycles

the method of polarized traces for the 2d helmholtz equation we present a solver for the 2d high frequency helmholtz equation in heterogeneous acoustic media with online parallel complexity that scales optimally as o n l where n is the number of volume unknowns and l is the number of processors as long as l grows at most like a small fractional power of n the solver decomposes the domain into layers and uses transmission conditions in boundary integral form to explicitly define polarized traces i e up and down going waves sampled at interfaces local direct solvers are used in each layer to precompute traces of local green s functions in an embarrassingly parallel way the offline part and incomplete green s formulas are used to propagate interface data in a sweeping fashion as a preconditioner inside a gmres loop the online part adaptive low rank partitioning of the integral kernels is used to speed up their application to interface data the method uses second order finite differences the complexity scalings are empirical but motivated by an analysis of ranks of off diagonal blocks of oscillatory integrals they continue to hold in the context of standard geophysical community models such as bp and marmousi 2 where convergence occurs in 5 to 10 gmres iterations while the parallelism in this paper stems from decomposing the domain we do not explore the alternative of parallelizing the systems solves with distributed linear algebra routines

technical communique sliding mode control with bounded l 2 gain performance of markovian jump singular time delay systems in this paper we investigate the problem of sliding mode control smc of markovian jump singular time delay systems the aim is to consider the bounded 2 gain performance in the analysis of sliding mode dynamics thus to improve the transient performance of the smc system firstly a delay dependent bounded real lemma is proposed for the underlying system to be stochastically admissible while achieving the prescribed bounded 2 gain performance condition an integral type switching surface function is designed by taking the singular matrix into account thus the resulting sliding mode dynamics is a full order singular markovian jump time delay system then the sliding mode dynamics is analyzed and the solvability condition for the desired switching surface function is derived moreover an smc law is synthesized to drive the system trajectories onto the predefined switching surface in a finite time finally a numerical example is provided to illustrate the effectiveness of the proposed techniques

parallel solution of block tridiagonal linear systems abstract the explicit structure of the inverse of block tridiagonal matrices is presented in terms of blocks defined by linear recurrence relations parallel algorithms are shown which solve block second order linear recurrences without using commutativity moreover we investigate the parallel solution of the associated block tridiagonal linear system using this theoretical background the implementation of the algorithms is analyzed both on a small number of processors and on a hypercube the resulting complexity is given in terms of parallel steps each consisting of block operations and the cost due to interprocessor communications is taken into account too

adaptive fuzzy controller for multivariable nonlinear state time varying delay systems subject to input nonlinearities in this paper an adaptive fuzzy variable structure controller is investigated for a class of uncertain multi input multi output mimo nonlinear time delay systems with both sector nonlinearities and dead zones a decomposition property of the control gain matrix is fully exploited in the controller design and the stability analysis the unknown time varying delay uncertainties are compensated for using an appropriate lyapunov krasovskii functional the boundedness of all signals of the closed loop system as well as the exponential convergence of the underlying tracking errors to an adjustable region are established the effectiveness of the proposed fuzzy adaptive controller is illustrated throughout simulation results

observer based adaptive neural network controller for uncertain nonlinear systems with unknown control directions subject to input time delay and saturation abstract this paper addresses the design of an observer based adaptive neural controller for a class of strict feedback nonlinear uncertain systems subject to input delay saturation and unknown direction the input delay has been handled using an integral compensator term in the controller design a neural network observer has been developed to estimate the unmeasured states in the observer design the lipschitz condition has been relaxed to solve the problem of unknown control directions the nussbaum gain function has been applied in the backstepping controller design the explosion of complexity occurred in the traditional backstepping technique has been avoided utilizing the dynamic surface control dsc technique and the designed controller is singularity free it has been shown that all closed loop signals are semi globally uniformly ultimately bounded sguub and the output tracking error converges to a small neighborhood of the origin by choosing the design parameters appropriately the numerical examples illustrate the effectiveness of the proposed control scheme

a novel error compensation control for a class of high order nonlinear systems with input delay a novel tracking error compensation based adaptive neural control scheme is proposed for a class of high order nonlinear systems with completely unknown nonlinearities and input delay in the tracking errors of existing papers there exist the following difficulties first output curve always lags behind the desired trajectory second some big peak errors cause a decrease in tracking precision and third a big initial value of the modified tracking error can make the closed loop system unstable to tackle them three corresponding error compensation terms are constructed including a prediction and compensation term an auxiliary signal produced by the constructed auxiliary system and a damping term however inequality amplification caused by high order will weaken the effectiveness of the proposed error compensation scheme and the control precision will decrease under an assumption that the lower bounds of the unknown control coefficients should be exactly known to overcome aforementioned difficulties in the derivation of the first virtual control law the radial basis function neural network is used to approximate a hybrid term online constructed by unknown nonlinearities a lumped control coefficient achieved by state transformation and the dynamic of the proposed error compensation terms and desired signal meanwhile input delay is coped with a robust compensation signal constructed based on a finite integral of the past control values finally it is proven that all the closed loop signals are semiglobally uniformly ultimately bounded simulation results demonstrate the effectiveness of the proposed method

brief paper novel adaptive neural control design for nonlinear mimo time delay systems in this paper we address the problem of adaptive neural control for a class of multi input multi output mimo nonlinear time delay systems in block triangular form based on a neural network nn online approximation model a novel adaptive neural controller is obtained by constructing a novel quadratic type lyapunov krasovskii functional which not only efficiently avoids the controller singularity but also relaxes the restriction on unknown virtual control coefficients the merit of the suggested controller design scheme is that the number of online adapted parameters is independent of the number of nodes of the neural networks which reduces the number of the online adaptive learning laws considerably the proposed controller guarantees that all closed loop signals remain bounded while the output tracking error dynamics converges to a neighborhood of the origin a simulation example is given to illustrate the design procedure and performance of the proposed method

a new result on h_ infty state estimation of delayed static neural networks this brief presents a new guaranteed h infty performance state estimation criterion for delayed static neural networks to facilitate the use of the slope information about activation function the estimation error of activation function is separated into two parts for the first time then a novel lyapunov krasovskii functional lkf is constructed which has fully captured the slope information of the activation based on the new lkf a less conservative design criterion of estimator is derived to ensure the asymptotic stability of estimation error system with h infty performance the desired estimator gain matrices and the performance index are obtained by solving a convex optimization problem the simulation results show that the proposed method has much better performance than the most recent results

fuzzy model based sampled data control of chaotic systems a fuzzy time dependent lyapunov krasovskii functional approach this paper addresses the sampled data stabilization problem for chaotic systems represented by takagi sugeno t s fuzzy models if the upper bounds for the time derivative of membership functions are available combining the fuzzy blending for some quadratic functions together with the introduction of some new useful terms a novel fuzzy time dependent lyapunov krasovskii functional lkf is proposed to fully capture the available characteristics of the actual sampling pattern and membership functions simultaneously based on the proposed lkf a new criterion dependent on the upper bounds for the time derivative of membership functions is presented to guarantee the asymptotic stability of the whole closed loop system moreover a stability criterion independent of the upper bounds is also provided based on the corresponding common time dependent lkf then the designed fuzzy sampled data controller can be synthesized by analyzing the corresponding stabilization conditions moreover a search algorithm is provided to find the optimal tuning parameters finally one practical example of the lorenz system is given to illustrate that much less conservativeness can be achieved compared with the earlier results by using the corresponding common lkf and the results can be further improved when adopting the fuzzy time dependent lkf within large upper bounds

fuzzy adaptive output feedback control of mimo nonlinear systems with partial tracking errors constrained in this paper a partial tracking error constrained fuzzy output feedback dynamic surface control dsc scheme is proposed for a class of uncertain multi input and multi output mimo nonlinear systems the considered mimo nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design with the help of fuzzy logic systems identifying the mimo unknown nonlinear systems a fuzzy adaptive observer is established to estimate the unmeasured states by transforming the tracking errors into new virtual error variables and based on the dsc backstepping recursive design technique a new adaptive fuzzy output feedback control method is developed it is proved that the proposed control approach can guarantee that all the signals of the resulting closed loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds the simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme

switched fuzzy output feedback control and its application to a mass spring damping system this paper investigates the dynamic output feedback control problem for interval type 2 it2 fuzzy systems a switched output feedback controller which depends on the values of membership functions is constructed the membership functions of it2 fuzzy systems contain parameter uncertainties and are different from the type 1 takagi sugeno fuzzy systems based on the it2 fuzzy set theory the parameter uncertainties can be effectively obtained by upper and lower membership functions a novel it2 switched output feedback controller is designed to ensure that the closed loop system is asymptotically stable with an h infty performance finally a mass spring damping system is proposed to show the feasibility and the merit of the proposed scheme over the other existing ones

decentralized adaptive fuzzy control for nonlinear large scale systems with random packet dropouts sensor delays and nonlinearities abstract this paper investigates the adaptive fuzzy decentralized control problem for a class of uncertain nonlinear large scale systems different from the conventional backstepping based adaptive output feedback control technique the output measurements are imperfect and considered to suffer random packet dropouts rpds random sensor delays rsds and random sensor nonlinearities rsns which result typically from a network environment such as sensor networks a novel sensor model is established for describing the random phenomena within a unified representation by introducing a multi markovian variable based on this sensor model two main difficulties arise first is that the system output cannot be used directly for controller design due to the randomly occurring phenomena second is how to deal with the complex nonlinear stochastic terms with unknown interconnections unknown time varying delays and unknown sensor nonlinearities entangled together with the help of new coordinate transformations and mean value theorem by using appropriate lyapunov krasovskii functionals a new adaptive decentralized memoryless output feedback controller is designed it is proved that the constructed controller ensures the boundedness in probability of all the closed loop signals in presence of rpds rsds and rsns the simulation results are presented to show the effectiveness of the proposed scheme

adaptive indirect fuzzy sliding mode controller for networked control systems subject to time varying network induced time delay two major challenges in networked control systems are the time varying networked induced delays and the packet losses to alleviate these problems this study presents a novel fuzzy sliding mode controller where a fuzzy system is used to estimate the nonlinear dynamical system online and the networked induced delay is handled by pade approximation the problem of packet losses is handled by viewing them as large time varying delays in the system the sliding mode based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time varying networked induced time delays using an appropriate lyapunov function it is proved that the tracking error converges to the neighborhood of zero asymptotically furthermore since the adaptation laws of the parameters are derived by using of the lyapunov function these laws are also found to be stable simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network which is subject to bounded external disturbances time varying network induced delays and packet losses with adequate performance

adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on a small gain approach in this paper an adaptive fuzzy robust output feedback control problem is considered for a class of single input and single output nonlinear systems in a strict feedback form the considered systems possess the unstructured uncertainties unknown dead zone and the dynamics uncertainties and they do not assume the states being available for the controller design in the controller design fuzzy logic systems are first used to approximate the unstructured uncertainties and by utilizing the information of the bounds of the dead zone slopes and treating the time varying inputs coefficients as a system uncertainty a fuzzy state observer is designed to estimate the unmeasured states by combining a backstepping technique with a nonlinear small gain approach a new adaptive fuzzy robust output feedback control has been developed it is proved that the proposed fuzzy adaptive control approach can guarantee the semiglobal uniform ultimate boundedness for all the solutions of the closed loop systems simulation studies and comparisons with previous methods are included to illustrate the effectiveness of the proposed approach

dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation in this paper a dynamic surface control dsc scheme is proposed for a class of uncertain strict feedback nonlinear systems in the presence of input saturation and unknown external disturbance the radial basis function neural network rbfnn is employed to approximate the unknown system function to efficiently tackle the unknown external disturbance a nonlinear disturbance observer ndo is developed the developed ndo can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set using ndo and rbfnn the dsc scheme is developed for uncertain nonlinear systems based on a backstepping method using a dsc technique the problem of explosion of complexity inherent in the conventional backstepping method is avoided which is specially important for designs using neural network approximations under the proposed dsc scheme the ultimately bounded convergence of all closed loop signals is guaranteed via lyapunov analysis simulation results are given to show the effectiveness of the proposed dsc design using ndo and rbfnn

multi instant observer design of discrete time fuzzy systems a ranking based switching approach this paper generalizes recent results on multi instant observer design for discrete time takagi sugeno fuzzy systems through a valid ranking based switching approach the approach hereby develops a concentrated subdivision of spanning space composed of normalized fuzzy weighting functions and then substantially produces a new ranking based switching mechanism by taking advantage of this ranking based switching mechanism a class of new fuzzy multi instant observers are achieved and more relaxed design conditions with respect to the recent work can be obtained for ensuring the asymptotically stability of the developed state estimation error system two illustrative examples are provided to validate the effectiveness of the result given in this study

on band matrices and their inverses abstract structural properties of the inverses of band matrices are discussed the definition of semiseparable matrices is given and the theorem is proved that the inverse of a strict band matrix is a semiseparable matrix and vice versa finally a recurrence algorithm is recommended for computing the blocks of the inverses of strict band matrices

prediction of ground reaction forces in level and incline decline walking from a multistage analysis of plantar pressure data knowing the ground reaction forces grfs during walking has various biomechanical applications in injury prevention gait analysis as well as prosthetic and footwear design the current study presents a method for predicting the grfs in level and incline decline walking that may be used in various outdoor biomechanics studies geared towards the above applications the method was developed to predict the complete set of grfs at walking inclinations of 0 5 10 15 and 20 plantar pressure insoles were used to obtain inclination specific linear regression models based on three periods of gait stance phase and the model determined grfs were compared with those measured from a forceplate the three periods were determined based on the observed shifting of load bearing insole sensors from heel to forefoot during walking i e heel strike midstance and toe off six subjects wearing minimalist shoes fitted with plantar pressure insoles containing 99 pressure sensors performed ten walking trials at each of the aforementioned inclinations on an adjustable ramp with an embedded forceplate data from contact of the instrumented shoes with the forceplate were used to create linear regressions to transform insole pressure data into a complete set of grfs the root mean square error rmse over peak recorded values were on average 10 3 21 for level walking and 11 4 23 for ramp walking in the respective anteroposterior vertical and mediolateral directions the multistage linear regression model developed in the current study may be an acceptable option for estimating grfs during walking in various environments without the restraint of a forceplate

adaptive neural control for output feedback nonlinear systems using a barrier lyapunov function in this brief adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions the unknown functions are handled via on line neural network nn control using only output measurements a barrier lyapunov function blf is introduced to address two open and challenging problems in the neuro control area 1 for any initial compact set how to determine a priori the compact superset on which nn approximation is valid and 2 how to ensure that the arguments of the unknown functions remain within the specified compact superset by ensuring boundedness of the blf we actively constrain the argument of the unknown functions to remain within a compact superset such that the nn approximation conditions hold the semiglobal boundedness of all closed loop signals is ensured and the tracking error converges to a neighborhood of zero simulation results demonstrate the effectiveness of the proposed approach

hybrid fuzzy adaptive output feedback control design for uncertain mimo nonlinear systems with time varying delays and input saturation in this paper a hybrid fuzzy adaptive output feedback control design approach is proposed for a class of multiinput and multioutput strict feedback nonlinear systems with unknown time varying delays unmeasured states and input saturation first fuzzy logic systems are employed to approximate unknown nonlinear functions in the system next a smooth function is used to approximate the input saturation and an adaptive fuzzy state observer is constructed to solve the problem of unmeasured states based on the designed adaptive fuzzy state observer a serial parallel estimation model is established by applying adaptive fuzzy dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial parallel estimation model a new fuzzy controller with the composite parameters adaptive laws is developed based on lyapunov krasovskii functional it is proved that all variables of the closed loop system are bounded and the system outputs can follow the given bounded reference signals as close as possible a simulation example is provided to further show the effectiveness of this novel control scheme

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

storage compression of inverses of band matrices abstract two algorithms are presented which reduce the total amount of storage locations needed by the inverse of a band matrix the first compresses the existing inverse of a general band matrix the second computes a compressed form of the inverse of tridiagonal block matrix both algorithms have been numerically tested over significant classes of matrices

decentralized adaptive delay dependent neural network control for a class of large scale interconnected nonlinear systems this paper investigates the problem of adaptive decentralized control for a class of large scale interconnected nonlinear systems with unknown time delays and the unmeasured states compared with the existing results the delay parameters are estimated by utilizing mean value theorem and adaptive mechanism with the help of the delay estimations a delay dependent state observer is designed to make the states available based on lyapunov stability theorem and the backstepping technique the novel adaptive neural network memory output feedback controllers are developed it is proved that the proposed control scheme can guarantee that all the signals in the closed loop system are bounded and the tracking error converges to the adjustable neighborhood of the origin the effectiveness of the proposed control scheme is illustrated by the simulation results

fuzzy model based reliable static output feedback mathscr h _ infty control of nonlinear hyperbolic pde systems this paper investigates the problem of output feedback robust mathscr h infty control for a class of nonlinear spatially distributed systems described by first order hyperbolic partial differential equations pdes with markovian jumping actuator faults the nonlinear hyperbolic pde systems are first expressed by takagi sugeno fuzzy models with parameter uncertainties and then the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed loop system with certain mathscr h infty disturbance attenuation performance based on a markovian lyapunov functional combined with some matrix inequality convexification techniques two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy pde systems it is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite difference method in space finally two examples are presented to demonstrate the effectiveness of the proposed methods

use of pressure insoles to calculate the complete ground reaction forces a method to calculate the complete ground reaction force grf components from the vertical grf measured with pressure insoles is presented and validated with this approach it is possible to measure several consecutive steps without any constraint on foot placement and compute a standard inverse dynamics analysis with the estimated grf

stability and stabilization of nonlinear switched systems under average dwell time in this paper the problems of stability and stabilization of nonlinear discrete time switched systems is investigated firstly in order to implement the lower bound of minimum average dwell time adt of discrete time switched nonlinear systems the open chain and quasi cyclic switching signals are introduced secondly the problem of these underlying nonlinear discrete time switched systems are solved by using the interval type 2 it2 fuzzy modeling approach thirdly a novel delayed it2 fuzzy controller is devised to guarantee the asymptotically stable of the resulting systems finally a numerical simulation example is given to show the merit and effectiveness of the proposed approach

dissipativity analysis and synthesis for discrete time t s fuzzy stochastic systemswith time varying delay this paper is concerned with the problems of dissipativity analysis and synthesis for discrete time takagi sugeno fuzzy systems with stochastic perturbation and time varying delay first a novel model transformation method is introduced to pull the time varying delay uncertainty out of the original system consequently the transformed model is composed of a linear time invariant system and a norm bounded uncertain subsystem by using this model transformation method combined with the lyapunov krasovskii technique sufficient conditions of the dissipativity are established then a fuzzy controller is designed to guarantee the dissipative performance of the closed loop system finally three examples are presented one shows the effectiveness of model transformation method the second performs the comparison with alternative approaches and the third illustrates the applicability of the proposed dissipative control methods

on periodic block tridiagonal matrices abstract periodic block tridiagonal matrices are defined and conditions are given for factorizing their characteristic polynomial by means of the zeros of chebyshev polynomials of the second kind these conditions are expressed by the block centrosymmetry of certain submatrices along the main diagonal the main theorems can be considered as generalizations of an earlier result published by elsner and redheffer and by rozsa

estimating the complete ground reaction forces with pressure insoles in walking this study presented a method to estimate the complete ground reaction forces from pressure insoles in walking five male subjects performed 10 walking trials in a laboratory the complete ground reaction forces were collected during a right foot stride by a force plate at 1000 hz simultaneous plantar pressure data were collected at 100 hz by a pressure insole system with 99 sensors covering the whole plantar area stepwise linear regressions were performed to individually reconstruct the complete ground reaction forces in three directions from the 99 individual pressure data until redundancy among the predictors occurred an additional linear regression was performed to reconstruct the vertical ground reaction force by the sum of the value of the 99 pressure sensors five other subjects performed the same walking test for validation estimated ground reaction forces in three directions were calculated with the developed regression models and were compared with the real data recorded from force plate accuracy was represented by the correlation coefficient and the root mean square error results showed very good correlation in anterior posterior 0 928 and vertical 0 989 directions and reasonable correlation in medial lateral direction 0 719 the root mean square error was about 12 5 and 28 of the peak recorded value future studies should aim to generalize the methods or to establish specific methods to other subjects patients motions footwear and floor conditions the method gives an extra option to study an estimation of the complete ground reaction forces in any environment without the constraints from the number and location of force plates

barrier lyapunov functions for the control of output constrained nonlinear systems in this paper we present control designs for single input single output siso nonlinear systems in strict feedback form with an output constraint to prevent constraint violation we employ a barrier lyapunov function which grows to infinity when its arguments approach some limits by ensuring boundedness of the barrier lyapunov function in the closed loop we ensure that those limits are not transgressed besides the nominal case where full knowledge of the plant is available we also tackle scenarios wherein parametric uncertainties are present asymptotic tracking is achieved without violation of the constraint and all closed loop signals remain bounded under a mild condition on the initial output furthermore we explore the use of an asymmetric barrier lyapunov function as a generalized approach that relaxes the requirements on the initial conditions we also compare our control with one that is based on a quadratic lyapunov function and we show that our control requires less restrictive initial conditions a numerical example is provided to illustrate the performance of the proposed control