An Observer Based Adaptive Iterative Learning Control for Robotic Systems

In this paper, an observer based adaptive iterative learning control is proposed for robotic systems. Due to the joint velocities are assumed to be not measurable, a state observer is introduced to design the iterative learning controller. We first derive an observation error model based on an tracking error observer. Then we apply an averaging filter to design the ILC algorithm. A fuzzy neural learning component using a filtered fuzzy neural network is presented to solve the problem of unknown nonlinearities. A robust learning component using sliding-mode like design is used to overcome the uncertainties, including fuzzy neural approximation error and the error induced by using state estimation errors. We show that all the adjustable parameters as well as internal signals remain bounded for all iterations. Finally, the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.