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
ZHANG Junna, BAI Guoxing. Path tracking via speed adjustment 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: ZHANG Junna, BAI Guoxing. Path tracking via speed adjustment 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 via speed adjustment for car-like robots based on a multilayer MPC framework

  • 摘要: 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、精确性和实时性均较好的前馈模型预测控制(Feedforward MPC, FMPC)底层路径跟踪控制算法与长时域预测精度较高的非线性模型预测控制(Nonlinear MPC, NMPC)顶层速度决策算法,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. 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4.2859 m·s−1时,位移误差的最大幅值为0.1838 m,航向误差的最大幅值为0.1350 rad. 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 robots are front wheel steering robots with a structure similar to that of unmanned vehicles and are widely used in manufacturing, warehousing, and other industries because of their advantages, such as simple structure and high load-bearing capacity. The path tracking control of car-like robots, characterized by a small range of system constraints and a low degree of component standardization, is garnering increasing widespread attention. Several studies have been conducted on this topic; however, these studies have not considered the correlation between active speed adjustment and path tracking. Existing control systems set the longitudinal speed to a low value to ensure 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 high compatibility with error sources. However, existing MMPC systems adopt linear MPC (LMPC) as the bottom path tracking control algorithm. Typical LMPC design methods are unable to take into account the reference path information in front of car-like robots and are not accurate enough at high longitudinal speeds; therefore, the longitudinal speeds of existing MMPC systems are still low. To address these problems, an MMPC-based framework for active speed adjustment and path-tracking control is introduced. The framework combines a feedforward MPC (FMPC) bottom path-tracking control algorithm with high accuracy and real-time performance and a nonlinear MPC (NMPC) top-speed adjustment algorithm with high long-term predictive accuracy. This new system aims to enhance car-like robots’ path tracking capabilities while actively adjusting their speed. The effectiveness of the proposed MMPC system is validated through joint simulations using MATLAB and CarSim. Results show that the proposed system achieves high-accuracy path tracking at high average traveling speeds, recording a maximum displacement error of 0.1838 m and a heading error of 0.1350 rad at an average traveling speed of 4.2859 m·s−1. Compared with constant-speed FMPC, NMPC systems, and the existing MMPC system, the proposed system demonstrated higher accuracy at higher longitudinal speeds. 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 of path tracking can be guaranteed, the average traveling speed of the proposed MMPC system is higher, and this system can increase the average traveling speed by 43.06% and 317.48% compared with existing FMPC and MMPC systems with smaller errors, approaching the performance index of the NMPC system.

     

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