基于特征模型的高速列车黄金分割自适应控制

针对高速列车传统建模方法中模型参数时变、非线性、计算复杂等问题,提出了基于特征模型的高速列车黄金分割智能自适应控制方法。特征建模可以克服列车在传统建模过程中遇到的难题,将模型简化,降低模型的复杂度。利用特征建模方法,根据一般动力学模型推导建立特征模型,并采用梯度辨识算法对模型参数进行在线辨识。设计全系数黄金分割自适应控制器、PID控制器、迭代控制器,分别对列车位移、速度进行误差跟踪控制。在控制器的设计中又考虑了列车节能、乘坐舒适性等问题,并进行对比分析。仿真结果表明,黄金分割自适应控制器的控制效果更优于PID控制器和迭代控制器,也说明了特征建模方法和黄金分割自适应控制方法在列车速度、位移跟踪控制中的有效性。

Characteristic Model Based Golden Section Adaptive Control of High Speed Train

Aiming at the problems of time-varying model parameters, non-linearity and complex calculation in traditional high-speed train modeling methods, an intelligent adaptive golden section control method for high-speed train based on characteristic model is proposed. Characteristic modeling can overcome the difficulties encountered in the traditional train modeling process, simplify the model and reduce the complexity of the model. Based on the general dynamic model, the characteristic model was deduced by using the characteristic modeling method, and the gradient identification algorithm was used to identify the model parameters online. A full coefficient golden section adaptive controller, a PID controller, and an iterative controller were designed to track the errors of train displacement and speed respectively. In the design of the controller, some problems such as train energy saving and comfort were considered and comparative analyzed. The simulation results show that the Golden Section Adaptive Controller is better than the PID controller and the Iterative Controller. It also shows the effectiveness of the feature modeling method and Golden Section adaptive control method in the train speed and displacement tracking control.