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

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