A control method based on multi-step motion compensation is proposed to enhance the accuracy of path tracking control for car-like robots in the presence of signal time delay. By analyzing the response of existing path tracking control systems and those based on single-step motion compensation to signal time delay, we propose the multi-step motion compensation principle and develop a motion compensation algorithm based on multi-step trajectory prediction. Subsequently, integrating the multi-step motion compensation method with model predictive control (MPC) and proportional-integral-derivative (PID) control algorithms, we develop two distinct path tracking control systems for car-like robots. Joint simulations using MATLAB and Carsim under various working conditions show that the displacement error amplitude of the proposed systems remains below 0.0787 m, indicating high accuracy. When the average signal time delay is 0.2 s, the multi-step method reduces the displacement error amplitude by over 20.61% compared with the single-step method. When the delay is 0.4 s, the reduction exceeds 53.04%. Additionally, the path tracking control method based on multi-step motion compensation is robust against external disturbances like positioning errors, and shortening the control period can further improve its performance.

为提高信号时滞影响下类车机器人路径跟踪控制的精确性, 提出一种基于多步运动补偿的控制方法。首先, 通过分析现有路径跟踪控制系统和单步运动补偿路径跟踪控制系统在信号时滞影响下的响应原理, 提出多步运动补偿原理和基于多步轨迹预测的运动补偿算法。然后, 将多步运动补偿方法分别与模型预测控制方法(MPC)和PID控制算法相结合, 构建面向类车机器人的路径跟踪控制系统。最后, 利用MATLAB与Carsim联合仿真, 在不同工况下对所提控制系统进行仿真验证。在所有仿真结果中, 所提控制系统的位移误差幅值不超过0.0787 m, 具有较高的精确性。相比单步运动补偿方法, 在随机信号时滞的平均长度为0.2 s时, 多步运动补偿方法可将位移误差幅值减小20.61%以上; 在随机信号时滞的平均长度为0.4 s时, 则可以减小53.04%以上。此外, 对于定位误差等外界扰动, 基于多步运动补偿的路径跟踪控制方法具有较强的鲁棒性, 缩短控制周期可以进一步提升多步运动补偿方法的效果。