Event-Triggered Robust Optimal Control for PMSM With Unknown Internal Dynamics, Disturbances, and Constrained Inputs

In industry, for driving a permanent magnet synchronous motor (PMSM), it is more favorable to optimize control performances and reduce computational complexity and communication waste from a controller to actuators. For that reason, this paper employs an event-triggering mechanism to design a robust optimal control strategy for PMSM. Firstly, the PMSM model is presented as a strict-feedback nonlinear system with unknown internal dynamics, disturbances, and constrained inputs. Then, an event-triggered (ET) feedforward control strategy is introduced to convert the separated speed and current dynamics into an augmented system. Secondly, an ET-robust optimal feedback control strategy and an ET disturbance compensation strategy are designed using adaptive dynamic programming (ADP) and zero-sum game theory. All controller parameters are tuned online without identifying unknown dynamics or using a persistent excitation condition. It is shown that system stability and the exclusion of Zeno’s behavior are fulfilled. Finally, compared with the existing time-triggering control strategies in simulation and experiments with TMS320F28335 of Texas Instruments, the proposed strategy is more effective in reducing the burden of computation bandwidth and communication load.