Adaptive Iterative Learning Control of Robot Manipulators for Friction Compensation

Abstract

Iterative learning control (ILC) has been well recognized for its ability to improve the tracking performance of systems that perform repetitive tasks. Its achievable performance, however, can be significantly degraded by the presence of non-repetitive disturbances which vary every iteration. Such may be a case for high-precision robot manipulators which are commonly subject to nonlinear and velocity-dependent friction forces. With a traditional PD-type ILC scheme, as joint velocities change along iterations, friction forces can vary substantially and deteriorate the performance of ILC. To address this problem, we propose an adaptive ILC algorithm, in which an adaptive friction compensation signal is introduced with ILC to adaptively identify the friction model over multiple iterations. Theoretical convergence analysis is provided with simulation verification on a 3-degree-of-freedom (DOF) planar manipulator. Experimental verification is also performed on a 5-DOF robot for silicon wafer handling. The verification results show that the proposed adaptive ILC approach can achieve significantly better tracking performance than traditional ILC methods.