Wavelet reduced order observer-based adaptive tracking control for a class of uncertain delayed non-linear systems subjected to actuator saturation using actor-critic architecture
by Manish Sharma; Ajay Verma
International Journal of Automation and Control (IJAAC), Vol. 7, No. 4, 2013

Abstract: This paper investigates the mean to design the reduced order observer-controller strategy for a class of uncertain delayed non-linear system subject to actuator saturation using actor-critic architecture. A new design approach of wavelet-based adaptive reduced order observer is proposed. The task of the proposed wavelet adaptive reduced order observer is to identify the unknown system dynamics and to reconstruct the states of the system. Wavelet neural network (WNN) is implemented to approximate the uncertainties present in the system and to compensate the non-linearities introduced due to actuator saturation. Reinforcement learning is applied through actor-critic architecture where a separate structure is for both perception (critic) and action (actor). Reinforcement learning is used via two wavelet neural networks (WNNs), critic WNN and action WNN. By Lyapunov-Krasovskii approach, the closed-loop tracking error is proved to be uniformly ultimate bounded. A numerical example is provided to verify the effectiveness of theoretical development.

Online publication date: Sat, 12-Jul-2014

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