Two-dimensional repetitive control strategy for PMSM based on multi-objective intelligent optimization

High-precision current control of Permanent Magnet Synchronous Motors (PMSMs) is pivotal for advanced servo systems but is frequently compromised by coexisting periodic and aperiodic disturbances. This paper introduces a composite control strategy that synergistically combines a Two-Dimensional Repetitive Controller (2D-RC) with a Proportional-Integral Equivalent-Input-Disturbance (PI-EID) estimator to achieve superior current regulation in PMSMs. The 2D-RC effectively attenuates periodic disturbances by leveraging an internal model and an inter-period learning mechanism, while the PI-EID estimator actively compensates for non-periodic disturbances and system uncertainties, notably mitigating the phase lag inherent in conventional EID methods. A key contribution of this work is the explicit formulation of the controller tuning as a multi-objective optimization problem, addressing the inherent trade-offs among tracking accuracy, disturbance rejection, and control effort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to derive a Pareto-optimal set of parameters, providing designers with a spectrum of optimal choices tailored to specific application needs. Comprehensive stability analysis, grounded in the 2D Lyapunov theory, ensures robust closed-loop performance. Extensive simulations and experimental results demonstrate that the proposed method achieves a more favorable performance compromise compared to conventional single-objective and non-optimized strategies, conclusively validating its efficacy for high-performance PMSM current control applications.