张俊娜, 白国星. 基于多层MPC框架的类车机器人调速路径跟踪[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.02.14.001
引用本文: 张俊娜, 白国星. 基于多层MPC框架的类车机器人调速路径跟踪[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.02.14.001
Path tracking with speed adjusting for car-like robots based on a multilayer MPC framework[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.02.14.001
Citation: Path tracking with speed adjusting for car-like robots based on a multilayer MPC framework[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.02.14.001

基于多层MPC框架的类车机器人调速路径跟踪

Path tracking with speed adjusting for car-like robots based on a multilayer MPC framework

  • 摘要: 目前针对类车机器人路径跟踪控制的研究工作尚未考虑主动调速与路径跟踪之间的关联,现有控制系统为了保证较高的路径跟踪控制精度只能将纵向速度设置为较低值。同时,在其他移动装备的主动调速路径跟踪控制策略中,以模型预测控制(Model predictive control, MPC)为基础的多层模型预测控制(Multilayer MPC, MMPC)具有对于误差来源兼容性较强的优势,但是现有系统采用速度较高时精度不佳的线性模型预测控制(Linear MPC, LMPC)作为底层路径跟踪控制算法,因此纵向行驶速度仍然较低。针对这些问题,结合基于MMPC的主动调速路径跟踪控制框架、前馈模型预测控制(Feedforward MPC, FMPC)路径跟踪控制算法与非线性模型预测控制(Nonlinear MPC, NMPC)速度决策算法,构建了新的基于MMPC框架的主动调速路径跟踪控制系统。通过MATLAB和CarSim联合仿真对提出的MMPC系统进行了测试。在仿真结果中,提出的MMPC系统可以在平均行驶速度较高时实现较高精度的路径跟踪,在平均行驶速度为4.2859m·s-1时,位移误差的最大幅值为0.1838m,航向误差的最大幅值为0.1350rad。在纵向速度较高时,提出的MMPC相比恒速的FMPC、NMPC系统和已有的MMPC系统精度更高,在相同工况下,FMPC系统误差发散,提出的MMPC系统可以相对已有的NMPC和MMPC系统将位移误差最大幅值减小46.29%和62.22%。在能够保障较高精度时,提出的MMPC系统的平均行驶速度较高,相比已有的FMPC和MMPC系统,可以将平均行驶速度提高43.06%和317.48%,与NMPC系统指标接近。

     

    Abstract: Car-like robot is a front wheel steering robot with a structure similar to that of an unmanned vehicle, which is widely used in manufacturing, warehousing, and other industries because of advantages such as simple structure and strong load-bearing capacity. The path tracking control of car-like robots has the characteristics of a smaller system constraint range and lower degree of component standardisation and is now getting more and more widespread attention. At present, there are many research works dedicated to the path tracking control of car-like robots, but these research works have not yet considered the correlation between active speed adjusting and path tracking, and the existing control system can only set the longitudinal speed to a low value to ensure a high path tracking control accuracy. Meanwhile, among active speed control strategies for other mobile equipment, multilayer model predictive control (MMPC) based on model predictive control (MPC) has the advantage of strong compatibility with the error source, but the existing MMPC system adopts linear MPC (LMPC) as the lower path tracking control algorithm. Typical LMPC design methods are unable to take into account the reference path information in front of a car-like robot and are not accurate enough at higher longitudinal speeds, so the longitudinal speeds of existing MMPC systems are still low. To address these problems, an MMPC-based active speed adjusting path tracking control framework is introduced, combining the feedforward MPC (FMPC) path tracking control algorithm with the nonlinear MPC (NMPC) speed adjusting algorithm, and constructing a new MMPC-based path tracking control system for car-like robots with active speed adjusting. The proposed MMPC system is tested by joint simulation with MATLAB and CarSim. In the simulation results, the proposed MMPC system can achieve higher accuracy path tracking at higher average travelling speeds, with the maximum magnitude of displacement error of 0.1838 m and heading error of 0.1350 rad at an average travelling speed of 4.2859 m-s-1. At higher longitudinal speeds, the proposed MMPC achieves higher accuracy compared to the constant speed FMPC, NMPC systems and the existing MMPC system. Under the same working conditions, the error of the FMPC system is dispersed, and the proposed MMPC system can reduce the maximum displacement error by 46.29% and 62.22% compared with the existing NMPC and MMPC systems. When higher accuracy can be guaranteed, the average travelling speed of the proposed MMPC system is higher, and it can increase the average travelling speed by 43.06% and 317.48% compared with existing FMPC and MMPC systems with smaller errors, which is close to the index of the NMPC system.

     

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