Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances With Application to PMSM Drives
Xin Dong, Jianliang Mao, Yunda Yan, Chuanlin Zhang, Jun Yang
Abstract
This article 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 nonrecursive 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 drive system are carried out to demonstrate the effectiveness and conciseness of the proposed GDPC method.