Adaptive pseudo-derivative feedback with a feed-forward gain controller for permanent magnet synchronous motor servo system based on integrated iterative learning control

Abstract

This article proposes an adaptive pseudo-derivative feedback with a feed-forward gain controller based on the integrated iterative learning control for the permanent magnet synchronous motor servo system. At first, the improved just-in-time online model identification method is adopted to identify and linearize the nonlinear servo system to obtain the model information for the control strategy. Second, a model-based iterative learning control strategy is presented for the tracking control of permanent magnet synchronous motor servo system. Meanwhile, to guarantee the robust convergence of the iterative learning control system, a new tuning methodology considering the model uncertainties is proposed to select the weighting matrices of the iterative learning control. Third, to further improve control performance, an online generalized predictive control is integrated in the iterative learning control framework, referred to as integrated iterative learning control. By combining generalized predictive control and iterative learning control, the integrated iterative learning control can complement both control methods to obtain good performance, because online generalized predictive control can respond to disturbances immediately and iterative learning control can correct bias left uncorrected by the online controller. Finally, an adaptive pseudo-derivative feedback with a feed-forward gain controller is designed based on the integrated iterative learning control. Since the integrated iterative learning control can be expressed by the pseudo-derivative feedback with a feed-forward gain parameter, the design can achieve both performance improvement and simple controller structure. Experiments confirm the effectiveness of the proposed adaptive pseudo-derivative feedback with a feed-forward gain controller.