Against the background that efficient energy utilization has become a global focus and the demand for energy conservation and consumption reduction of industrial equipment is increasingly urgent, aiming at the problems of permanent magnet synchronous motors (PMSMs) in actual operation, such as parameter perturbation, time-varying load and control saturation constraints, which lead to decreased operation efficiency, insufficient energy utilization, low trajectory tracking accuracy, slow convergence speed, weak anti-interference ability and poor engineering applicability, this paper proposes a predefined-time convergent guaranteed-performance control strategy to provide technical support for the efficient and stable operation of PMSMs. Firstly, a prescribed performance control structure independent of the initial value is designed, which breaks through the dependence of traditional Prescribed Performance Control (PPC) on initial states and lays a control foundation for efficient energy utilization. Secondly, the traditional reinforcement learning algorithm is improved to overcome its randomness defect, which is used to accurately online estimate the composite time-varying disturbances (including parameter perturbation and time-varying load) during the operation of PMSMs. Furthermore, the predefined-time convergence control mechanism is integrated to design a prescribed performance control law for PMSMs, which ensures that the angular velocity tracking error converges to zero within a pre-specified time, realizes time-optimal control, effectively suppresses the adverse effects caused by various disturbances and control saturation, and improves the motor operation efficiency and energy utilization efficiency. Finally, the effectiveness is verified by simulation. The results show that the strategy can effectively improve the trajectory tracking accuracy of PMSMs, achieve fast convergence within the predefined time, enhance the adaptability of the motor to complex working conditions, and further improve the energy utilization efficiency.
Jin et al. (Sat,) studied this question.