Feedback-Based Iterative Learning Design and Synthesis With Output Constraints for Robotic Manipulators

Abstract

Feedback-based iterative learning control (ILC) has been proposed to improve the unacceptable transient performance (either in state or in output) in the iteration-domain. This letter addresses a special performance requirement of output constraints, which are motivated from the safety requirements in robotic manipulators. A barrier-function like Lyapunov function is used to design a new state feedback (or a proportionalderivative controller) to ensure that output constraints are satisfied in the finite time-domain. This state feedback is then combined with the standard feed-forward ILC to track the desired trajectory. With the help of composite energy function, it is shown that, for robotic manipulators, the proposed control method can achieve perfect tracking performance without violating output constraints in any iteration. Simulation results, which are based on the model of recently developed rehabilitation robot EMU, are presented to illustrate the effectiveness of the proposed controller.