Yue Lili, Wang Yidong, Xiao Baodi, Wang Yiqing, Lin Junting
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China.
Research and Development Department, Beijing Consen Traffic Equipment Co., Ltd., Beijing, China.
Sci Prog. 2023 Oct-Dec;106(4):368504231208505. doi: 10.1177/00368504231208505.
Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.
由于列车运行环境复杂多变，参数不稳定且时变，精确建模受到限制。因此，针对外部干扰情况下列车的速度控制问题，提出了一种基于前馈补偿的改进型自抗扰控制算法（FC-MADRC），该算法降低了对列车模型的依赖。首先，基于列车的单质点数学模型建立状态空间方程，并将所有运行阻力视为干扰。其次，设计FC-MADRC算法。基于终端吸引子函数和新型Sigmoid函数，设计了一种改进的跟踪微分器（ITD）。利用狄拉克δ函数的性质构造了具有更好平滑性的改进型fal（nsfal）函数，并基于nsfal函数设计了改进的非线性状态误差反馈（ANLSEF）和改进的扩张状态观测器（IESO）。此外，基于PID思想，将误差积分项引入ANLSEF用于非线性运行，以减小列车速度跟踪的稳态误差。为提高系统的鲁棒性和控制精度，将前馈补偿项和干扰补偿项相结合，对干扰进行实时动态补偿。最后，利用CRH380A列车数据进行仿真。结果表明，与传统自抗扰控制和二自由度PID相比，FC-MADRC具有更强的抗干扰能力和更高的跟踪精度。FC-MADRC具有抗干扰能力强、响应速度快、跟踪精度高的优点。在外部干扰的前提下，它在不同路况下仍能实现精确的速度跟踪。
