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 proportional-derivative 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 roboti..