Output Based Adaptive Iterative Learning Control Design for Nonaffine Nonlinear Systems

نویسندگان

  • Ying-Chung Wang
  • Chiang-Ju Chien
چکیده

In this paper, an output based adaptive iterative learning controller using an output recurrent fuzzy neural network is proposed for a class of uncertain nonaffine nonlinear systems. It is assumed that the states are not measurable. Without state observer, a sliding window of measurement is introduced to design the iterative learning controller. The main structure of this controller is constructed by a fuzzy neural learning component and a robust learning component. For the fuzzy neural learning component, an output recurrent fuzzy neural network is introduced to design the controller in order to overcome the design difficulty in dealing with the unknown input dependent nonlinearities. For the robust learning component, an averaging filter approach is adopted to compensate for the uncertainties due to fuzzy neural approximation error, input disturbance and state estimation errors. These formulations enable us to utilize a Lyapunov-like analysis to prove the convergence and stability of the learning system. We show that all the adjustable parameters as well as internal signals remain bounded for all iterations. Furthermore, the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

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تاریخ انتشار 2009