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Repetitive Tracking Control of Nonlinear Systems Using Reinforcement Fuzzy-Neural Adaptive Iterative Learning Controller |
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PP: 475-482 |
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Author(s) |
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Ying-Chung Wang,
Chiang-Ju Chien,
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Abstract |
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| This paper proposes a new fuzzy neural network based reinforcement adaptive iterative learning controller for a class of
nonlinear systems. Different from some existing reinforcement learning schemes, the reinforcement adaptive iterative learning controller
has the advantages of rigorous proofs without using an approximation of the plant Jacobian. The critic is appended into the
reinforcement adaptive iterative learning controller to generate the simple discrete reinforcement signal, which provides a satisfaction
about the tracking performance. In addition, the reinforcement signal can be further applied in the weight adaptation rules. Iterative
learning components of the reinforcement adaptive iterative learning controller are designed to compensate for the uncertainties of
plant nonlinearities. The overall adaptive scheme guarantees all adjustable parameters and the internal signals remain bounded for all
iterations. Moreover, the norm of tracking error vector at each time instant will asymptotically converge to a tunable residual set as
iteration goes to infinity even the initial state error exists. Finally, a simulation result is given to demonstrate the learning performance
of the fuzzy neural network based reinforcement adaptive iterative learning controller. |
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