Adaptive Dynamic Programming for PMSM Control Under Safety, Robustness, and Optimality Constraints
针对受扰动和作动器故障影响的永磁同步电机，提出一种自适应动态规划方法，通过扰动观测器和神经网络设计满足最优性、鲁棒性和安全性的复合控制器，并用仿真和实验验证。
This article aims to derive an adaptive optimal speed regulator for permanent magnet synchronous motors (PMSMs) affected by both disturbances and actuator faults. Load torque is first modeled as a mismatched disturbance, where its estimation via a disturbance observer is drawn to construct an error system. Then, optimal speed regulation problem for PMSM is equivalently transformed into an optimal control problem for the error system. Given the presence of model variable couplings, an adaptive dynamic programming method is adopted to derive the optimal controller, where a critic neural network (NN) and an actor NN are used to approximate the cost function and the optimal controller, respectively. Notably, this article addresses the simultaneous occurrence of disturbances and actuator faults within the optimal control framework by designing separate treatments. Eventually, a composite controller, fulfilling optimality, robustness and safety constraints, is presented with rigorous proof via Lyapunov method. The proposed method is substantiated through both comparative numerical examples and experimental validation on a PMSM platform.