The suspension system of high-speed maglev trains has composite time-delay factors, such as inductance delay and control circuit latency, which lead to a decrease in the tracking and robustness of the current control loop. Based on the study of electromagnetic characteristics of suspension systems, this paper proposes a composite anti-time-delay current loop based on adaptive parameter optimization. First, a finite element analysis model of the suspension electromagnet is constructed to analyze the changes in suspension force and inductance of the suspension electromagnet. A self-tuning PI current loop is constructed to achieve time-varying parameter matching. Second, to tackle the inherent time delays and disturbances in the control loop, a predictive PI control algorithm combined with an extended state observer (ESO) is introduced, which effectively estimates and compensates for disturbances and phase lags. Furthermore, a parameter optimization strategy based on the adaptive differential evolution (ADE) algorithm is proposed to address the difficulties in current loop tuning. The results demonstrate that compared to traditional current loop strategies, the dynamic performance of the designed composite anti-time-delay current loop is significantly improved, enhancing the current following control capability of the suspension system under complex operating conditions.
Han et al. (Sun,) studied this question.
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