A new learning controller for periodic disturbance rejection

A new acceleration based learning control approach is developed to tackle the robust periodic trajectory tracking problem for robot manipulators. The acceleration feedback is incorporated into the learning feedforward term to provide high stiffness to the system against unknown periodic dynamics with a known period. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed learning controller achieves global asymptotic position tracking. Simulation results obtained from a high fidelity model show that the proposed controller outperforms the learning controller that does not utilize the acceleration feedback.