Jui-Sheng Lin

SYNCHRONIZATION CONTROL FOR A CLASS OF CHAOTIC SYSTEMS WITH UNCERTAINTIES

This paper investigates the chaos synchronization problem for a class of uncertain master-slave chaotic systems. Based on the variable structure control theory, a strategy is proposed to guarantee the occurrence of a sliding mode motion of error states when the proposed control law is applied. As expected, the error state is able to drive to zero with match external uncertainties or into a predictable neighborhood of zero with mismatch external uncertainties. Furthermore, a modified continuous sliding mode controller is also proposed to avoid the chattering. Examples of Lorenz system and Chuas circuit are presented to demonstrate the obtained results.

Robust Controller Design for Modified Projective Synchronization of Chen-Lee Chaotic Systems with Nonlinear Inputs

This study demonstrates the modified projective synchronization in Chen-Lee chaotic system. The variable structure control technology is used to design the synchronization controller with input nonlinearity. Based on Lyapunov stability theory, a nonlinear controller and some generic sufficient conditions can be obtained to guarantee the modified projective synchronization, including synchronization, antisynchronization, and projective synchronization in spite of the input nonlinearity. The numerical simulation results show that the synchronization and antisynchronization can coexist in Chen-Lee chaotic systems. It demonstrates the validity and feasibility of the proposed controller.

Adaptive Chaos Synchronization of FitzHugh-Nagumo Neurons

The remarkable system of FitzHugh-Nagumo (FHN) neurons in external electrical stimulation is studied from the view of chaos synchronization in this paper. An effective adaptive sliding mode controller is derived to achieve chaos synchronization even when the parameters of the drive and response FHN neurons are fully unknown. An illustrative example is presented for the purpose of verification and illustration.

SYNCHRONIZATION CONTROL OF NEURAL NETWORKS SUBJECT TO TIME-VARYING DELAYS AND INPUT NONLINEARITY

This paper investigates the synchronization problem for a particular class of neural networks subject to time-varying delays and input nonlinearity. Using the variable structure control technique, a memoryless decentralized control law is established which guarantees exponential synchronization even when input nonlinearity is present. The proposed controller is suitable for application in delayed cellular neural networks and Hopfield neural networks with no restriction on the derivative of the time-varying delays. A two-dimensional cellular neural network and a four-dimensional Hopfield neural network, both with time-varying delays, are presented as illustrative examples to demonstrate the effectiveness of the proposed synchronization scheme.

Decentralized Adaptive Control for Large-Scale Singular Systems with Series Nonlinearities

Abstract This paper concerns with the decentralized adaptive variable structure control for a class of perturbed large-scale singular systems with series input nonlinearities. Based on the Lyapunov stability theorem, a decentralized adaptive variable structure controller is developed. The limitation of knowing the upper bounds of the uncertainties is released due to the adaptive scheme in this paper. The proposed controller ensures the occurrence of the sliding manifold of the composite singular system even subjected to input nonlinearity. It shows that the uncertain nonlinear singular large-scale system also possesses the property of insensitivity to uncertainties and disturbances as a linear system does.