输入受约束的高速列车鲁棒自适应动态面控制

实现高速列车对期望速度与位移的精确跟踪至关重要。考虑输入饱和约束以及由于不确定的运行阻力、未知的黏滞摩擦系数和未测量的运行状态等引起的系统不确定性,提出高速列车的鲁棒自适应动态面控制算法。建立考虑牵引与制动转矩产生动态过程的高速列车动力学模型;引入扩张状态观测器在线估计和补偿系统总的不确定性,应用跟踪微分器代替动态面控制中的一阶滤波器,构造附加系统处理输入约束问题,设计了高速列车的鲁棒自适应动态面控制律;基于李雅普诺夫稳定性理论证明了闭环系统的稳定性以及高速列车速度跟踪误差和位移跟踪误差的半全局一致最终有界性。仿真结果验证了所提算法的有效性。

Robust Adaptive Dynamic Surface Control for High-speed Train with Input Constraints

The accurate tracking on desired speed and position of high-speed train(HST)is of vital importance.This paper investigated the robust adaptive dynamic surface control of HST with input saturation constraints and system uncertainties caused by the uncertain running resistance,the unknown viscous friction coefficients and the unmeasured states.Firstly,the dynamic model of HST was established considering the traction and braking torque generation dynamics.Secondly,a robust adaptive dynamics control law was presented,in which the extended state observer(ESO)was introduced to estimate and compensate the total uncertainties of the system.A tracking differentiator(TD)was adopted to replace the first-order filter in dynamic surface control while an additional system was designed to handle the input constraints.Finally,the stability of the closed-loop system and the semi-global uniformly ultimate boundedness of the speed tracking errors and position tracking errors of HST were guaranteed based on Lyapunov stability analysis.Simulation results show the effectiveness of the proposed method.