An Observer-Based Adaptive Iterative Learning Control Using Filtered-FNN Design for Robotic Systems

Abstract

An observer-based adaptive iterative learning control using a filtered fuzzy neural network is proposed for repetitive tracking control of robotic systems. A state tracking error observer is introduced to design the iterative learning controller using only the measurement of joint position. We first derive an observation error model based on the state tracking error observer. Then, by introducing some auxiliary signals, the iterative learning controller is proposed based on the use of an averaging filter. The main control force consists of a filtered fuzzy neural network used to approximate for unknown system nonlinearity, a robust learning term used to compensate for uncertainty, and a stabilization term used to guarantee the boundedness of internal signals. The adaptive laws combining time domain and iteration domain adaptation are presented to ensure the convergence of learning error. We show that all the adjustable parameters as well as internal signals remain bounded for all iterations. The norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.