静液传动履带车辆转向神经元自适应PID控制

为实现静液传动履带车辆快速稳定转向,且转向轨迹可控,基于双侧轮边液压驱动结构特点,提出了转向时外侧马达排量采用压力、发动机转速双参数控制,内侧采用神经元自适应PID控制以跟随外侧的转向控制策略. 在Matlab/Simulink中建立了包含基于S函数的神经元PID控制器和综合控制策略Stateflow模块的整车模型,对转向控制进行仿真分析,阶跃输入时,神经元PID比传统PID控制能有效抑制系统超调量,加快系统响应速度;不同转向工况仿真结果表明:神经元PID控制具有较好的目标跟随能力,提高了系统的实时性和鲁棒性,使得静液传动履带车辆具有良好的转向性能. 

Steering Control of Tracked Vehicle with Hydrostatic Transmission Based on Neural PID

To achieve fast and stable steering along with the controllable trajectory,a steering control strategy is proposed and applied for tracked vehicle with hydrostatic transmission. Considering the structural features of hydrostatic driving wheels of both sides, the outer hydraulic motor is controlled by parameters of outside pressure and engine speed,while the inside motor is controlled by neural PID controller to follow the movement of outside motor. The whole vehicle including S-function based neural PID controller and stateflow module of control strategy was modeled in Matlab/Simulink environment. Simulation under different typical working conditions was implemented with the driver's inputs and control algorithm in-loop. The results indicate that,with the step signal as the system input, compared with PID control, the neural PID control could better reduce the system overshoot and speed up the response time. Simulation results also show that, under different typical working conditions, the neural PID control can improve the robustness and real-time ability of the system as well as the steering performance of tracked vehicle to follow the target.