面向工业电机考虑参数自调节的优化模型预测控制

针对常规控制器在工业电机无参数或者参数失配时引起的控制性能下降等问题,不满足工业自动化发展需求,因此提出一种考虑现实工业情况的无参数模型预测控制。首先引入模型预测控制的思想,将离散异步电机数学模型作为模型预测控制的预测模型并在线估计更新其集总参数,从而无需任何电机参数即可实现模型预测控制。针对工业用电机复杂工况,进行优化使其能够快速且准确地辨识出集总参数。仿真结果表明,所提出的控制策略预测精度高,在工况与电机参数均为未知时变的情况下,无参数模型预测控制仍然能在不同情况下达到较为优良的控制性能,证明了该方法优良的鲁棒性和有效性。

Parametric model predictive control considering weak magnetic

Aiming at the problems of the conventional controller in the absence of parameters or parameter mismatch of industrial motors, the development performance of industrial automation is not satisfied. a model predictive control based on system identification (SI-MPC) for the asynchronous motor of electric vehicle is proposed. Firstly, the idea of model predictive control is introduced. The discrete mathematical model of asynchronous motor is derived, which is regarded as a predictive model for MPC, and online estimation updates its lumped parameters. the model predictive control can be realized without any motor parameters. Simulation and experimental results show that the proposed control strategy has high prediction accuracy. Under the condition that both working conditions and motor parameters are unknown and time-varying, SI-MPC can still achieve excellent control performance under different conditions, which proves the excellent parameter robustness and effectiveness of this method.