Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems |
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Authors: | Ching-Hung Lee Bo-Hang Wang |
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Affiliation: | (1) Department of Electrical Engineering, Yuan Ze University, Chungli, 320, Taoyuan, Taiwan |
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Abstract: | This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive
supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for
obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For
nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal.
The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive
SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control
of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach
for nonlinear uncertain systems. |
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Keywords: | Adaptive control Neural networks CMAC Wavelet |
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