基于分数阶滑模自适应神经网络的中速磁浮列车运行控制方法

Operation Control Method for Medium-Speed Maglev Trains Based on Fractional Order Sliding Mode Adaptive Neural Network

为减小中速磁浮列车运行阻力对运行性能的不利影响,设计一种基于分数阶滑模自适应神经网络(FO-SMAC-NN)的运行控制器。首先,在考虑列车实际运行过程中受到的空气阻力、涡流阻力以及坡道附加阻力3种阻力的基础上,构造运动学方程;其次,设计分数阶滑模自适应神经网络控制律和滑模自适应参数的更新律,得到由速度前馈、分数阶滑模自适应等效控制和自适应神经网络阻力补偿3个部分组成的FO-SMAC-NN运行控制器;然后,运用李雅普诺夫定理证明采用该运行控制器的闭环控制系统的稳定性;最后,依托长10 km的仿真试验线和6节编组仿真列车,分别在理想情况和功率谱密度为108的白噪声干扰情况下,仿真对比FO-SMAC-NN运行控制器和传统比例-积分-微分(PID)运行控制器控制下的列车运行性能。结果表明:FO-SMAC-NN运行控制器可以准确估计并补偿列车运行中受到的各项阻力,增强了运行控制系统的鲁棒性,有效提高了列车位置与速度的控制性能。

To reduce the adverse effect of the mediumspeed maglev train operation resistance on the operation performance,an operation controller based on the fractional order sliding mode adaptive control neural network(FOSMACNN)is proposed.Firstly,a kinematic equation is constructed on the basis of considering the air resistance,the eddy current resistance and the additional ramp resistance in the actual operation of the train.Secondly,the FOSMACNN control law and the updating law of sliding mode adaptative parameters are designed to obtain the FOSMACNN operation controller which consists of three components:velocity feedforward,fractional order sliding mode adaptive equivalent control and adaptive neural network resistance compensation.Thirdly,the stability of the closedloop control system applying the operation controller is proved through Lyapunov theorem.Finally,based on a 10 km simulation test line and a 6-car formation simulation train,the train operation performance with the FOSMACNN operation controller and the conventional proportionalintegralderivative(PID)operation controller are simulated and compared under the ideal condition and the white noise disturbance with the power spectral density of 108,respectively.Results demonstrate that the FOSMACNN operation controller can accurately estimate and compensate the resistance during the train operation,enhance the robustness of the operation control system,and effectively improve the control performance of the train position and speed.