This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed-loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc.

The authors propose a learning controller with parameter adaptation for constrained motion of robot manipulators. The additional computed torque error term is used in this controller to reduce the load from feedback and keeps its gain reasonably low. Position and force trackings are achieved by applying the position learning control algorithms to position controlled axes and the force learning control algorithms to force control axes. Joint acceleration terms are never used in the controller and the PE condition is not required for force tracking. The uniform convergence of position, velocity, acceleration and force trajectories is obtained under simple conditions.