Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances with Application to PMSM Drives

Abstract

This paper investigates a generalized dynamic predictive control (GDPC) strategy with a novel autonomous tuning mechanism of the horizon for a class of nonlinear systems subject to mismatched disturbances. As a new incremental function for the predictive control method, the horizon can be determined autonomously with respect to the system working conditions, instead of selecting a fixed value via experience before, which is able to effectively improve the control performance optimization ability to a certain extent considering different system perturbation levels. To this aim, firstly, a non-recursive composite control framework is constructed based on a series of disturbance observations via higher-order sliding modes. Secondly, by designing a simple one-step scaling gain update mechanism into the receding horizon optimization, the horizon can be therefore adaptively tuned according to its real-time practical operating conditions. A three-order numerical simulation and a typical engineering application of permanent magnet synchronous motor (PMSM) drive system are carried out to demonstrate the effectiveness and conciseness of the proposed GDPC method.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

continuous-time predictive control, self-tuning receding horizon, mismatched disturbance, robustness and adaptiveness balance, PMSM

Citation

Dong, X. et al. (2023) Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances with Application to PMSM Drives. IEEE Transactions on Industrial Electronicsz, 71 (1), pp. 954-964

Rights

Research Institute

Institute of Engineering Sciences (IES)