基于干扰观测器的高速列车RBF自适应滑模控制方法

Adaptive sliding mode control method for high-speed train RBF based on disturbance observer

针对高速列车动力学模型的不确定性和存在外部干扰难以实现高速列车对目标轨迹的高精度跟踪控制的问题,设计了一种基于非线性干扰观测器的RBF神经网络自适应滑模控制方法。首先,针对高速列车模型非线性系统的不确定性问题,设计自适应RBF神经网络鲁棒控制器进行跟踪控制,基于RBF神经网络的特性设计神经网络权值自适应律,对列车模型中的未知函数进行估计。其次,针对高速列车跟踪控制外部干扰问题,采用指数收敛干扰观测器进行干扰补偿,提高高速列车对目标轨迹追踪的抗干扰能力。最后,李雅普诺夫(Lyapunov)稳定性分析保证了闭环系统的渐近稳定性,以秦沈客运专线为仿真对象。结果表明,所设计的控制方法不仅解决了列车模型未知阻力部分的自适应逼近,而且在此基础上引入干扰观测器对外部非线性干扰进行补偿实现了对期望轨迹的高精度快速跟踪。

Aiming at the uncertainty of the high-speed train dynamics model and the problems that it is difficult to realize the high-precision tracking control of the target trajectory of the high-speed train due to the existence of external disturbances, a RBF neural network adaptive sliding mode control method based on nonlinear disturbance observer is designed. Firstly, aiming at the uncertainty of the nonlinear system of the high-speed train model, an adaptive RBF neural network robust controller is designed for tracking control. function to estimate. Secondly, in view of the external interference problem of high-speed train tracking control, an exponential convergence interference observer is used to compensate for interference, so as to improve the anti-interference ability of high-speed trains to target trajectory tracking. Finally, Lyapunov stability analysis ensures the asymptotic stability of the closed-loop system, taking the Qin-Shen Passenger Dedicated Line as the simulation object. The results show that the designed control method not only solves the adaptive approximation of the unknown resistance part of the train model, but also introduces a disturbance observer to compensate the external nonlinear disturbance to achieve high-precision and fast tracking of the desired trajectory.