Browsing by Author "Yang, Jun"
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Item Open Access Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances with Application to PMSM Drives(IEEE, 2023-02-22) Dong, Xin; Mao, Jianliang; Yan, Yunda; Zhang, Chuanlin; Yang, JunThis 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.Item Open Access Robust Predictive Speed Regulation of Converter-Driven DC Motors Via A Discrete-Time Reduced-Order GPIO(IEEE, 2018-10-31) Yang, Jun; Wu, Hao; Hu, Liang; Li, ShihuaConverter-driven direct current (DC) motors exhibit various advantages in industry, but impose several challenges to higher-precision speed regulation in the presence of parametric uncertainties and exogenous, time-varying load torque disturbances. In this paper, the robust predictive speed regulation problem of a generic DC-DC buck converter-driven permanent magnet DC motors is addressed by using an output feedback discrete-time model predictive control (MPC) algorithm. A new discrete-time reduced-order generalized proportional-integral observer (GPIO) is proposed to reconstruct the virtual system states as well as the lumped disturbances. The estimates of GPIO are then collected for output speed prediction. An optimized duty ratio law of the converter is obtained by solving a constrained receding horizon optimization problem, where the operational constraint on control input is explicitly taken into account. Finally, the effectiveness of the proposed new algorithm is demonstrated by various experimental testing results.