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The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach. [ABSTRACT FROM AUTHOR]

This paper deals with the H∞ synchronization problem of switched chaotic systems accompanied by a time-driven switching law and its application to secure communications. Based on the Lyapunov stability theory and linear matrix inequality (LMI) and linear matrix equality (LME) optimization techniques, an output feedback controller that guarantees the synchronization of switched master-slave chaotic systems is designed. A chaotic encryption technique that uses synchronization is proposed for securely transmitting a message over public channels. Numerical simulations of both analog and digital security communication systems are conducted to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the global asymptotic stability (GAS) for a class of nonlinear neural networks with multiple delays. Based on Lyapunov stability theory and the linear matrix inequality (LMI) technique, a less conservative delay-dependent stability criterion is derived. The present result is shown to be less conservative than those given in the literature. [Copyright &y& Elsevier]

Abstract: In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein. [Copyright &y& Elsevier]

Abstract: This paper addresses chaos anti-synchronization of uncertain unified chaotic systems with dead-zone input nonlinearity. Using the sliding mode control technique and Lyapunov stability theory, a proportional–integral (PI) switching surface is proposed to ensure the stability of the closed-loop error system in sliding mode. Then a sliding mode controller (SMC) is proposed to guarantee the hitting of the switching surface even with uncertainties and the control input containing dead-zone nonlinearity. Some simulation results are included to demonstrate the effectiveness and feasibility of the proposed synchronization scheme. [Copyright &y& Elsevier]

Abstract: This article aims to present a decentralized synchronization scheme for a class of delayed neural networks subject to dead-zone nonlinearity in control input. On the basis of the drive–response concept and the Lyapunov stability theory, a decentralized control law is proposed for guaranteeing global synchronization even when input nonlinearity is present. The proposed controller is suitable for application in delayed cellular neural networks (DCNNs) and delayed Hopfield neural networks (DHNNs) with no restriction on the derivative of the time-varying delays. Finally, computer simulations are provided to demonstrate the effectiveness of the proposed synchronization scheme. [Copyright &y& Elsevier]