Uncertainty decomposition-based robust control of robot manipulators

Abstract

In this paper, a new robust control approach is proposed for robot manipulators based on a decomposition of model uncertainty. Parameterized uncertainty is distinguished from unparameterized uncertainty. A compensator is designed for each uncertainty group, and the combination of both compensators yields the robust controller. The effect of parameterized uncertainty is completely compensated by an integral compensator, and the unparameterized uncertainty is compensated with a saturation-based robust compensator. As a result, since the magnitude of the unparameterized uncertainty is usually much lower than the global uncertainty, the typical demand of robust controllers for high feedback gains is dramatically reduced. In the proposed control law, double boundary layers are used to achieve both good transient response and accurate steady-state tracking. Uniform ultimate boundedness of the tracking error is obtained, and it is shown that the ultimate error bound is not affected by the parameterized uncertainty. The proposed method has been experimentally tested on a direct-drive robot arm and the results are presented in this paper. The effectiveness of the new method has been confirmed by both simulation and experiments.