Zero-Power Control of Hybrid Magnetic Levitation System Based on Particle Swarm Optimization
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摘要:
为有效抑制永磁电磁混合悬浮系统在零功率控制过程中出现的气隙超调与碰撞问题，提出一种基于电流积分反馈的自适应超螺旋滑模零功率控制器，并利用粒子群优化（PSO）算法对电流积分系数进行在线整定. 首先，建立混合悬浮系统的数学模型，基于电流积分反馈策略设计零功率控制器；在此基础上，构造快速非奇异终端（FNST）滑模面以加速收敛，并在超螺旋滑模控制中引入自适应双模态切换策略，形成零功率控制器，实现系统在干扰下的快速气隙调整与跟踪. 针对控制器中固定积分系数所引发的问题，分析其对系统动态性能的影响，利用PSO算法对积分系数进行在线优化，使其能够根据系统状态实时调整，有效抑制气隙超调、提升收敛速度，从而改善整体控制性能. 此外，为降低零功率控制过程中的撞轨风险，在传统气隙阈值策略基础上引入气隙速度信息，构建“速度 + 尺寸”双重判据，增强垂直方向的碰撞预测能力. 仿真与实验结果表明：所提策略可显著降低气隙超调并加快收敛过程，超调量小于0.30 mm，收敛时间缩短至0.67 s；相比传统阈值法，提出的双重判据可将判断时间提前约0.10 s，气隙超调降低1.70 mm，能更有效预测并预防碰撞发生.
Abstract:To effectively suppress the airgap overshoot and collision issues during the zero-power control process of the hybrid permanent magnet electromagnetic levitation system, an adaptive super-twisting sliding mode zero-power controller based on current integral feedback was proposed, and the particle swarm optimization (PSO) algorithm was utilized for online tuning of the current integral coefficient. First, the mathematical model of the hybrid levitation system was established, and a zero-power controller was designed based on the current integral feedback strategy. On this basis, a fast non-singular terminal (FNST) sliding mode surface was constructed to accelerate convergence, and an adaptive dual-mode switching strategy was introduced into the super-twisting sliding mode control to form the zero-power controller, realizing rapid airgap adjustment and tracking under disturbances. To address the problems caused by the fixed integral coefficient in the controller, its impact on the dynamic performance of the system was analyzed. The PSO algorithm was utilized for online optimization of the integral coefficient, enabling it to adjust in real time according to the system state, effectively suppressing airgap overshoot, improving convergence speed, and thereby enhancing the overall control performance. Furthermore, to reduce the risk of track collision during the zero-power control process, airgap velocity information was introduced based on the traditional airgap threshold strategy to construct a “velocity + size” dual criterion, enhancing the collision prediction capability in the vertical direction. Simulation and experimental results indicate that the proposed strategy significantly reduces airgap overshoot and accelerates the convergence process. The overshoot is less than 0.30 mm, and the convergence time is shortened to 0.67 s; compared with the traditional threshold method, the proposed dual criterion advances the decision time by approximately 0.10 s, reduces the airgap overshoot by 1.70 mm, and can more effectively predict and prevent the occurrence of collisions.
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Key words:
- magnetic levitation /
- zero-power control /
- nonlinear system /
- particle swarm optimization
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图 6 安全阈值为4 mm时间隙-速度临界曲线
Figure 6. Gap and velocity critical curve at safety threshold of 4 mm
表 1 平台参数
Table 1. Platform parameters
参数 数值 hpm/mm 6 N/匝 550 Hc/（A·m−1） 5.8 × 105 A/m2 0.0025 m/kg 50 μ0/（H·m−1） 4π × 10−7 μr 1.05 初始气隙/mm 16 额定气隙/mm 6.2 线圈电阻/Ω 3 
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表 2 仿真工况1的气隙超调和收敛时间
Table 2. Airgap overshoot and convergence time of simulation condition 1
Ki 加载 减载 超调/mm 收敛时间/s 超调/mm 收敛时间/s 0.01 0.41 （100%） 1.27 （100%） 0.36 （100%） 1.63 （100%） 0.07 0.21 （51%） 0.51 （40%） 0.18 （50%） 0.71 （44%） PSO 0.14 （34%） 0.22 （17%） 0.05 （14%） 0.50 （31%） 0.13 碰撞
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表 3 实验工况1的气隙超调和收敛时间
Table 3. Airgap overshoot and convergence time of experiment condition 1
Ki 加载 减载 超调/mm 收敛时间/s 超调/mm 收敛时间/s 0.01 1.13 （100%） 2.15 （100%） 0.81 （100%） 1.65 （100%） 0.07 0.66 （58%） 1.37 （63%） 0.53 （65%） 1.25 （75%） PSO 0.28 （25%） 0.67 （31%） 0.19 （23%） 0.67 （40%） 0.13 碰撞
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表 4 实验工况2的气隙超调和收敛时间
Table 4. Airgap overshoot and convergence time of experiment condition 2
策略 加载 减载 超调/mm 收敛时间/s 超调/mm 收敛时间/s PID 1.32 （100%） 1.64 （92%） 1.18 （100%） 1.57 （100%） SMC 0.92 （69%） 1.53 （86%） 0.71 （60%） 1.45 （92%） FNSMC 0.77 （58%） 1.78 （100%） 0.54 （46%） 1.55 （98%） FNSTSMC 0.66 （50%） 1.37 （77%） 0.53 （45%） 1.25 （79%）
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