基于解耦策略的直线感应牵引电机法向力自适应最优控制

中低速磁悬浮列车和部分地铁车辆均采用直线感应牵引电机作为驱动机构，该电机特有的边缘效应和法向力对直线感应牵引电机的效率以及性能均产生很大的负面影响，因此直线感应牵引电机采用常规的电传动控制策略并不能取得良好的控制效果。该文充分考虑边缘效应的影响，建立了直线感应牵引电机的数学模型。将推力与磁通进行解耦，并分析磁通、推力与电流控制环节的非线性与时变性，提出了神经自适应控制的设计方法。分析了法向力的组成与特性，得出其数学表达式，以推力与磁通解耦为基础，结合最优化理论，考虑多个优化目标建立损耗最小函数，实现直线感应牵引电机的损耗最优控制。计算机仿真与实验对所提控制策略与传统控制策略进行对比，验证了该方法在典型工况下的有效性。

Normal Force Adaptive Optimal Control for Linear Induction Motor Based on Decoupling Strategy

The linear induction motor has been applied to the magnetic suspension maglev and metro vehicle. The edge effect and normal force have strong nonlinear influence on the performance and efficiency of the linear induction motor, so the electric drive system of the linear induction motor can’t be designed by traditional control strategy. Considering end effect completely, we got the mathematical model of the linear induction motor. Aiming at the nonlinear and time-variation of the flux, thrust force and current, a neuron adaptive design method was proposed for the electric drive system based on decoupling control of the force and flux. According to the physical characteristics analyses of the normal force, its mathematical model was proposed. Using optimal theory, the least loss function considering multi-objective optimization to complete adaptive optimal control based on the neuron adaptive decoupling strategy are obtained. Simulations and experiment have been conducted to validate the effectiveness of the proposed control scheme under reference conditions. The merits of the proposed control system are indicated in comparison with a traditional control system.