Norm Optimal Iterative Learning Control for Improved Trajectory Tracking of Servo Motor

摘要

To improve the trajectory tracking and robustness of closed-loop servo system against the model perturbation, this paper presents a novel norm optimal iterative learning control (NOILC) scheme combined with proportional velocity (PV) feedback control. It is well known that the feedback controller performance is always limited due to the so-called Bode sensitivity integral, which states that the feedback controller performance is always a trade-off between the reference tracking and the disturbance rejection. Hence, to address this trade-off called "waterbed effect", we synthesize a NOILC scheme, which can significantly improve the tracking performance by learning the system dynamics through the past tracking errors and the control effort. Formulating the ILC design as an optimization problem, we determine the optimal learning filters and present the hardware in loop testing (HIL) validation of the proposed scheme on a servo motor. Experimental results substantiate that the NOILC combined with PV can significantly reduce the tracking error and enhance the transient and steady-performance.