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

the existence and global exponential stability of periodic solution for a neutral coupled system on networks with delays in this paper we establish sufficient conditions for the existence and global exponential stability of periodic solution to a type of neutral coupled system on networks with delays the key to prove the existence of periodic solutions is using the combined method of graph theory coincidence degree theory and lyapunov functional method and the sufficient conditions are easy to be checked finally a numerical simulation is carried out to show the correctness of our main results

brief paper output feedback adaptive control of a class of nonlinear discrete time systems with unknown control directions in this paper output feedback adaptive control is investigated for a class of nonlinear systems in output feedback form with unknown control gains to construct output feedback control the system is transformed into the form of the narma nonlinear auto regressive moving average model based on which future output prediction is carried out with employment of the predicted future output a constructive output feedback adaptive control is given with the discrete nussbaum gain exploited to overcome the difficulty due to unknown control directions under the global lipschitz condition of the system functions the boundedness of all the closed loop signals and asymptotical output tracking are achieved by the proposed control simulation results are presented to show the effectiveness of the proposed approach

periodic solutions of linear and quasilinear neutral functional differential equations abstract in this paper we will use the fourier expansions to generalize hale s alternative results for the existence of periodic solutions of linear retarded functional differential equations to neutral equations using some a priori estimation and the leray schauder degree theory we will obtain some existence theorems of periodic solutions for a class of quasilinear neutral equations the results also generalize the works of hatvani and krisztin on retarded equations

adaptive control of nonlinear systems using neural networks layered networks are used in a nonlinear adaptive control problem the plant is an unknown feedback linearizable discrete time system represented by an input output model a state space model of the plant is obtained to define the zero dynamics which are assumed to be stable a linearizing feedback control is derived in terms of some unknown nonlinear functions to identify these functions it is assumed that they can be modelled by layered neural networks the weights of the networks are updated and used to generate the control a local convergence result is given computer simulations verify the theoretical result

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

adaptive nn control for a class of strict feedback discrete time nonlinear systems in this paper both full state and output feedback adaptive neural network nn controllers are presented for a class of strict feedback discrete time nonlinear systems firstly lyapunov based full state adaptive nn control is presented via backstepping which avoids the possible controller singularity problem in adaptive nonlinear control and solves the noncausal problem in the discrete time backstepping design procedure after the strict feedback form is transformed into a cascade form another relatively simple lyapunov based direct output feedback control is developed the closed loop systems for both control schemes are proven to be semi globally uniformly ultimately bounded

many body theory of chemotactic cell cell interactions we consider an individual based stochastic model of cell movement mediated by chemical signaling fields this model is formulated using langevin dynamics which allows an analytic study using methods from statistical and many body physics in particular we construct a diagrammatic framework within which to study cell cell interactions in the mean field limit where statistical correlations between cells are neglected we recover the deterministic keller segel equations within exact perturbation theory in the chemotactic coupling epsilon statistical correlations are non negligible at large times and lead to a renormalization of the cell diffusion coefficient d r an effect that is absent at mean field level an alternative closure scheme based on the necklace approximation probes the strong coupling behavior of the system and predicts that d r is renormalized to zero at a critical value of epsilon indicating self localization of the cell stochastic simulations of the model give very satisfactory agreement with the perturbative result at higher values of the coupling simulations indicate that d r approximately epsilon 2 a result at odds with the necklace approximation we briefly discuss an extension of our model which incorporates the effects of short range interactions such as cell cell adhesion

control of nonlinear dynamical systems using neural networks ii observability identification and control for pt i see ibid vol 4 1993 this paper considers the problems of regulation and tracking of a dynamical system when the state variables of the dynamical system are not accessible the existence of the nonlinear maps describing the identifier and controller are first established and the implications for neural network realizations are described simulation results are included to complement the theoretical discussions

stability analysis on a predator prey system with distributed delays abstract this paper concerns the local and global dynamical properties of the nonnegative and positive equilibria of a lotka volterra predator prey system with distributed delays it is shown that while the positive equilibrium does not exist the nonnegative equilibrium is globally asymptotically stable or globally attractive as long as the delays are small enough if the positive equilibrium exists it is shown that it is locally asymptotically stable when the delays are suitably small furthermore an explicit asymptotic stability region for the positive equilibrium is also obtained based on a liapunov functional

adaptive nn control for a class of discrete time non linear systems in this paper adaptive neural network nn control is investigated for a class of single input single output siso discrete time unknown non linear systems with general relative degree in the presence of bounded disturbances firstly the systems are transformed into a causal state space description adaptive state feedback nn control is presented based on lyapunov s stability theory then by converting the systems into a causal input output representation adaptive output feedback nn control is given finally adaptive nn observer design and observer based adaptive control are presented under the assumption of persistent excitation pe all the control schemes avoid the so called controller singularity problem in adaptive control by suitably choosing the design parameters the closed loop systems are proven to be semi globally uniformly ultimately bounded sguub simulation studies show the effectiveness of the newly proposed schemes

experimental studies in nonlinear discrete time adaptive prediction and control this paper presents implementation results using recently introduced discrete time adaptive prediction and control techniques using online function approximators we consider a process control experiment as our test bed and develop a discrete time adaptive predictor for liquid volume and a discrete time adaptive controller for reference volume tracking we use takagi sugeno ts fuzzy systems as our function approximators and for both prediction and control we investigate the use of a least squares update of the fuzzy system s parameters

the existence uniqueness and global attractivity of periodic solution for a type of neutral functional differential system with delays in this article by applying the coincidence degree theory and constructing a suitable lyapunov functional the authors study a kind of neutral functional differential system with delays some sufficient conditions which guarantee the existence uniqueness and global attractivity of periodic solution for this system are obtained

stable adaptive control using fuzzy systems and neural networks stable direct and indirect adaptive controllers are presented which use takagi sugeno fuzzy systems conventional fuzzy systems or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous time nonlinear plants with poorly understood dynamics the indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics while the direct adaptive scheme allows for the incorporation of such a priori knowledge in specifying the controller we prove that with or without such knowledge both adaptive schemes can learn how to control the plant provide for bounded internal signals and achieve asymptotically stable tracking of a reference input in addition for the direct adaptive scheme a technique is presented in which linguistic knowledge of the inverse dynamics of the plant may be used to accelerate adaptation the performance of the indirect and direct adaptive schemes is demonstrated through the longitudinal control of an automobile within an automated lane

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

feedback linearization using neural networks abstract for a class of single input single output continuous time nonlinear systems a multilayer neural network based controller that feedback linearizes the system is presented control action is used to achieve tracking performance for a state feedback linearizable but unknown nonlinear system the control structure consists of a feedback linearization portion provided by two neural networks plus a robustifying portion that keeps the control magniture bounded a stability proof is given in the sense of lyapunov it is shown that all the signals in the closed loop system are uniformly ultimately bounded no off line learning phase is needed initialization of the network weights is straightforward

gaussian networks for direct adaptive control a direct adaptive tracking control architecture is proposed and evaluated for a class of continuous time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible the architecture uses a network of gaussian radial basis functions to adaptively compensate for the plant nonlinearities under mild assumptions about the degree of smoothness exhibit by the nonlinear functions the algorithm is proven to be globally stable with tracking errors converging to a neighborhood of zero a constructive procedure is detailed which directly translates the assumed smoothness properties of the nonlinearities involved into a specification of the network required to represent the plant to a chosen degree of accuracy a stable weight adjustment mechanism is determined using lyapunov theory the network construction and performance of the resulting controller are illustrated through simulations with example systems

on the existence of periodic solutions for neutral functional differential equation abstract by means of mawhin s continuation theorem existence criteria are established for the periodic solutions of neutral functional differential equation

robust adaptive control conditions for global stability an input output approach is presented for analyzing the global stability and robustness properties of adaptive controllers to unmodeled dynamics the concept of a tuned system is introduced i e the control system that could be obtained if the plant were known comparing the adaptive system to the tuned system results in the development of a generic adaptive error system passivity theory is used to derive conditions which guarantee global stability of the error system associated with the adaptive controller and ensure boundedness of the adaptive gains specific bounds are presented for certain significant signals in the control systems limitations of these global results are discussed particularly the requirement that a certain operator be strictly positive real spr a condition that is unlikely to hold due to unmodeled dynamics

adaptive neural control for strict feedback nonlinear systems without backstepping in this brief a new adaptive neurocontrol algorithm for a single input single output siso strict feedback nonlinear system is proposed most of the previous adaptive neural control algorithms for strict feedback nonlinear systems were based on the backstepping scheme which makes the control law and stability analysis very complicated the main contribution of the proposed method is that it demonstrates that the state feedback control of the strict feedback system can be viewed as the output feedback control problem of the system in the normal form as a result the proposed control algorithm is considerably simpler than the previous ones based on backstepping depending heavily on the universal approximation property of the neural network nn only one nn is employed to approximate the lumped uncertain system nonlinearity the lyapunov stability of the nn weights and filtered tracking error is guaranteed in the semiglobal sense