Controlling Chaos in Neuron Based on Lasalle Invariance Principle

Abstract

A new control law is proposed to asymptotically stabilize the chaotic neuron system based on LaSalle invariant principle. The control technique does not require analytical knowledge of the system dynamics and operates without an explicit knowledge of the desired steady-state position. The well-known modified Hodgkin-Huxley (MHH) and Hindmarsh-Rose (HR) model neurons are taken as examples to verify the implementation of our method. Simulation results show the proposed control law is effective. The outcome of this study is significant since it is helpful to understand the learning process of a human brain towards the information processing, memory and abnormal discharge of the brain neurons.

Fast Synergetic Control for Chaotic Oscillation in the Power System Based on Input-Output Feedback Linearization

“…In recent years, scholars have studied how to effectively control chaos in the power system and obtained rich research results [6][7][8]. At present, the focus of research is to analyze and control chaotic oscillation in a three-bus power system [8][9][10][11][12][13][14][15]. Different from controlling chaos in some simple chaotic systems, the chaos control problem of the power system has obvious physical background.…”

“…Among numerous power system control devices, flexible alternating current transmission system (FACTS) devices have superior performance and can provide many superior options [16,17]. However, the existing chaos control methods for the power system always ignore the use of the FACTS device to control chaotic oscillation in the power system [9][10][11][12][13][14]. Among the members of FACTS devices, unified power flow controller (UPFC) device has rich control functions and the most excellent performance, and it can adjust active power and reactive power at the same time.…”