Wavelet Neural Network Observer Based Adaptive Tracking Control for a Class of Uncertain Nonlinear Delayed Systems Using Reinforcement Learning

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Manish Sharma 1,* Ajay Verma 2

1. Dept. of Electronics and Instrumentation Engineering, Medicaps Institute of Technology and Management, Indore, India

2. Dept. of Electronics and Instrumentation Engineering, Institute of Engineering and Technology, Devi Ahilya University, Indore, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.02.03

Received: 23 Mar. 2011 / Revised: 20 Jul. 2011 / Accepted: 9 Oct. 2011 / Published: 8 Mar. 2012

Index Terms

Wavelet neural networks, adaptive control, optimal control, reinforcement learning, Lyapunov- Krasovskii functional


This paper is concerned with the observer designing problem for a class of uncertain delayed nonlinear systems using reinforcement learning. Reinforcement learning is used via two Wavelet Neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The “strategic” utility function is approximated by the critic WNN and is minimized by the action WNN. Adaptation laws are developed for the online tuning of wavelets parameters. By Lyapunov approach, the uniformly ultimate boundedness of the closed-loop tracking error is verified. Finally, a simulation example is shown to verify the effectiveness and performance of the proposed method.

Cite This Paper

Manish Sharma, Ajay Verma, "Wavelet Neural Network Observer Based Adaptive Tracking Control for a Class of Uncertain Nonlinear Delayed Systems Using Reinforcement Learning", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.2, pp.28-34, 2012. DOI:10.5815/ijisa.2012.02.03


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