Norm Optimal Iterative Learning Control for Non-Repetitive Trajectory Tracking of Servo System
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This paper presents a novel control scheme that augments the Norm Optimal Iterative Learning Control (NOILC) with the Proportional Velocity (PV) feedback control in order to improve the non-repetitive trajectory tracking performance of servo system. According to Bode’s sensitivity integral, the performance of feedback controller is always limited due to the trade-off between the low frequency tracking and high frequency disturbance rejection. Hence, in order to address this so called “waterbed effect” and improve the transient and steady state performance of the motion control system, an optimal iterative learning control scheme based on the current cycle configuration is synthesized. For satisfying the performance objectives, which are formulated as a quadratic cost function with weighted norms, the monotonic convergence of the tracking error using the maximum singular value condition is discussed. Moreover, the asymptotic stability of the PV augmented NOILC is proved using the spectral norm condition. The efficacy of the proposed control scheme is experimentally validated on a rotary servo system using Hardware In Loop (HIL) testing. The experimental results of the proposed control scheme compared with the ones of the conventional feedback and ILC schemes highlight the improvement in trajectory tracking and robustness of the NOILC augmented PV control scheme.
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