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Abstract
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Article Information:
Learning from Adaptive Neural Control of Electrically-Driven Mechanical Systems
Yu-Xiang Wu, Jing Zhang and Cong Wang
Corresponding Author: Yu-Xiang
Submitted: January 10, 2013
Accepted: January 31, 2013
Published: September 10, 2013 |
Abstract:
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This study presents deterministic learning from adaptive neural control of unknown electrically-driven mechanical systems. An adaptive neural network system and a high-gain observer are employed to derive the controller. The stable adaptive tuning laws of network weights are derived in the sense of the Lyapunov stability theory. It is rigorously shown that the convergence of partial network weights to their optimal values and locally accurate NN approximation of the unknown closed-loop system dynamics can be achieved in a stable control process because partial Persistent Excitation (PE) condition of some internal signals in the closed-loop system is satisfied. The learned knowledge stored as a set of constant neural weights can be used to improve the control performance and can also be reused in the same or similar control task. Numerical simulation is presented to show the effectiveness of the proposed control scheme.
Key words: Adaptive neural control, deterministic learning, electrically-driven mechanical systems, high-gain observer, RBF network, ,
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Abstract
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Cite this Reference:
Yu-Xiang Wu, Jing Zhang and Cong Wang, . Learning from Adaptive Neural Control of Electrically-Driven Mechanical Systems. Research Journal of Applied Sciences, Engineering and Technology, (16): 3034-3043.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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