关键词: 无轨胶轮车, LQR, PID, 前馈补偿, 电机模型, 横纵向协调控制, 智能矿山

Abstract: Unmanned driving technology is one of the important schemes to realize the safe,intelligent and efficient transportation of trackless rubber-tyred vehicles. In order to improve the accuracy of trajectory tracking in the process of unmanned driving,a control strategy based on feedforward compensation is proposed,which combines the lateral linear quadratic optimal controller (LQR) with the longitudinal proportional integral differential (PID) displacement speed regulator to realize the lateral and longitudinal coordinated control of the vehicle. By establishing a two-degree-of-freedom trackless rubber-tyred vehicle dynamics model and tracking error model considering tire cornering characteristics,and using the actual vehicle parameters of the underground trackless rubber-tyred vehicle to establish its motor model to obtain the driving and braking output of the vehicle. Carsim and Matlab / Simulink are used to build a joint simulation environment,and trajectory tracking simulation verification is carried out under underground two-lane conditions,single-lane conditions and bumpy road conditions. The simulation results show that the maximum lateral error in the process of vehicle trajectory tracking under three working conditions is only 5 cm,the maximum longitudinal error is only 10 cm,the speed error is controlled within 1 m / s,the heading error range is ±0. 1 rad,and the front wheel deflection angle changes smoothly without jitter. At the same time,in order to verify the tracking performance of the controller in the actual underground environment,a laboratory car was used to conduct field experiments in a underground roadway in Shaanxi. The experimental results show that the error of the experimental results under the actual roadway is still within a reasonable range,the problem of time-varying speed and path during vehicle operation is solved,which reflects that the controller has high precision and good stability.

Key words: trackless rubber-tyred vehicle,LQR,PID,feedforward compensation,motor model,lateral-longitudinal coordinated control,intelligent mine