Chaos Synchronization Using Adaptive Dynamic Neural NetworkController with Variable Learning Rates

Abstract

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

Chaos suppression for a four-dimensional fundamental power system model using adaptive feedback control

“…The rapid development of modern control theory has provided many advanced control methods for chaos control, such as finite time control (Wei et al, 2014a), feedback control (Chen and Han, 2003), fuzzy control (Vembarasan and Balasubramaniam, 2013), backstepping control (Zhang and Mu, 2014), adaptive control (Wei et al, 2014b), sliding mode control (Aghababa and Feizi, 2012), projective synchronization (Wang and He, 2008), passive control (Wei and Luo, 2007a), neural network control (Kao et al, 2011), optimal control (Chavarette et al, 2009), etc. Some of them have been applied to design a power system controller, such as Thyristor Controlled Series Compensation (TCSC) (Jiang et al, 2012), Static Var Compensator (SVC) (Ginarsa et al, 2013), Power System Stabilizer (PSS) (Farhang and Mazlumi, 2013) and Unified Power Flow Controller (UPFC) (Jiang et al, 2006).…”