Multiscenarios Parameter Optimization Method for Active Disturbance Rejection Control of PMSM Based on Deep Reinforcement Learning

In this article, a multiscenario parameter optimization method for an active disturbance rejection controller (ADRC) of a permanent magnet synchronous motor (PMSM) based on deep reinforcement learning (DRL) is proposed as MSPO-DRL. The parameter setting of the nonlinear ADRC has always been one of the difficulties affecting the optimal performance of ADRC, and there will be different optimal parameters under different control requirements. In this article, an artificial intelligence algorithm is introduced into the parameter optimization process of ADRC, and the DRL parameter optimization model that can automatically optimize and adjust the ADRC parameters in different application scenarios is constructed so that the ADRC can achieve the best control effect conveniently and the limitation of current methods has been solved. ADRC is applied to the speed loop of flux weakening control of PMSM for more electric aircraft, and the mathematical model of ADRC in this environment is built above all. The Markov decision process is integrated into ADRC. The interface module and reward function between ADRC and MSPO-DRL are designed. The concept of the ADRC control scenario is defined and integrated into the concept of the Markov decision process to improve the generalization of DRL. Then, the MSPO-DRL model is established, and the deep deterministic gradient strategy is used as the gradient descent strategy to converge the parameters optimization. After the model learning is completed, different environmental conditions are randomly selected for simulation and experiments to verify the optimization effect and generalization performance of the algorithm. Optimizations that are carried out by the heuristic algorithms are used for comparisons, and the superiority and feasibility of the proposed algorithm are verified.