UNCERTAINTY DECOMPOSITION-BASED ROBUST-CONTROL OF ROBOT MANIPULATORS

In this paper, a new robust control approach is proposed for robot man ipulators based on a decomposition of model uncertainty, Parameterized uncertainty is distinguished from unparameterized uncertainty, A comp ensator is designed for each uncertainty group, and the combination of both compensators yields the robust controller. The effect of paramet erized uncertainty is completely compensated by an integral compensato r, and the unparameterized uncertainty is compensated with a saturatio n-based robust compensator, As a result, since the magnitude of the un parameterized uncertainty is usually much lower than the global uncert ainty, the typical demand of robust controllers for high feedback gain s is dramatically reduced, In the proposed control law, double boundar y layers are used to achieve both good transient response and accurate steady-state tracking. Uniform ultimate boundedness of the tracking e rror is obtained, and it is shown that the ultimate error bound is not affected by the parameterized uncertainty, The proposed method has be en experimentally tested on a direct-drive robot arm and the results a re presented in this paper, The effectiveness of the new method has be en confirmed by both simulation and experiments.