Particle swarm optimization based active disturbance rejection control for maglev train suspension gap
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摘要:
针对磁浮列车电磁悬浮系统存在的不确定性内外扰动、开环不稳定以及控制器参数整定困难等问题，提出了一种基于粒子群优化的自抗扰悬浮间隙控制方法。将模型线性化误差、参数摄动等内扰与轨道不平顺、负载突变等外扰集结为总扰动，利用扩张状态观测器进行实时估计与主动补偿，在实现系统动态线性化的同时，设计了新型离散时间最优控制综合函数并构造对应的跟踪微分器以优化信号跟踪性能；针对自抗扰控制器中观测器带宽、控制器增益等关键参数难以人工获取最优解的难题，引入了粒子群优化算法；通过构建包含绝对误差积分与控制能耗的多目标适应度函数，对控制器参数进行了全局寻优，以平衡系统的快速跟踪能力与抗噪声干扰能力。研究结果表明：在起浮、负载突变及随机轨道噪声等工况下，该方法相较于传统人工整定的自抗扰控制，具有显著的性能优势；起浮过程调节时间明显缩短且无超调；在突加负载扰动下，悬浮间隙的累计绝对误差降低了28.3%，且能更快恢复至稳态；同时在传感器含噪环境下，控制电压曲线保持平滑，未出现高频抖动。该方法可有效解决磁浮系统快速性与抗扰性之间的矛盾，实现控制参数的优化整定，为复杂工况下磁浮列车的稳定悬浮提供有效的技术方案。
Abstract:To address such issues as uncertain internal and external disturbances, open-loop instability, and the difficulty of controller parameter tuning in the electromagnetic suspension systems of maglev trains, a suspension gap control method was proposed active disturbance rejection control (ADRC) based on particle swarm optimization. The internal disturbances (e.g., model linearization errors, parameter perturbations) and external disturbances (e.g., track irregularities, sudden load changes) were treated as a unified total disturbance. An extended state observer was employed to estimate and compensate for them in real time. While the dynamic linearization of the system was achieved, a novel integrated discrete time-optimal control function was designed, and the corresponding tracking differentiator was established to enhance signal tracking performance. In response to the difficulty in manually obtaining optimal solutions for key parameters such as observer bandwidth and controller gain in the active disturbance rejection controller, the particle swarm optimization algorithm was introduced. By constructing a multi-objective fitness function incorporating the integral of absolute error and control energy consumption, the global optimization was carried out for controller parameters to balance the fast tracking capability and the noise suppression ability of the system. The research results demonstrate that under conditions of suspension startup, sudden load changes, and random track noise, the proposed method exhibits significant performance advantages over traditional manually tuned ADRC. The settling time during the startup process is significantly shortened with no overshoot. Under sudden applied load disturbances, the cumulative absolute error of the suspension gap is reduced by 28.3%, and a steady state can be recovered more rapidly. Furthermore, in a noisy sensor environment, the control voltage curve remains smooth without high-frequency chattering. This method effectively resolves the conflict between the rapidity and disturbance rejection of the maglev system, and realizes the optimal tuning of control parameters, providing an effective technical solution for the stable suspension of maglev trains under complex operating conditions.
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图 5 扰动加速度和系统质量变化曲线
Figure 5. Variation curves of disturbance acceleration and system mass
图 7 典型工况响应曲线局部放大图
Figure 7. Enlarged views of response curves under typical operating conditions
表 1 性能指标对比
Table 1. Comparison of performance metrices
指标 手动调参ADRC PSO优化ADRC 改善率/% EIAE/(mm·s) 0.571 67 0.409 72 28.3 EISE/(mm2·s) 0.319 94 0.230 71 27.9 ERMSE/mm 0.063 05 0.048 52 23.0 Emax/mm 0.323 4 0.147 4 54.4 发生时刻/s 3.02 4.09 
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