Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

Abstract

Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.