伊力夏提·伊力哈木江, 孟宇, 白国星, 顾青, 王国栋, 常鑫睿, 黄建秀, 郑燕. 基于前馈非线性模型预测控制的类车机器人路径跟踪[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.04.29.008
引用本文: 伊力夏提·伊力哈木江, 孟宇, 白国星, 顾青, 王国栋, 常鑫睿, 黄建秀, 郑燕. 基于前馈非线性模型预测控制的类车机器人路径跟踪[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.04.29.008
ELHAM Elxat, MENG Yu, BAI Guoxing, GU Qing, WANG Guodong, CHANG Xinrui, HUANG JianXiu, ZHENG Yan. Path tracking for car-like robots based on feed-forward nonlinear model predictive control[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.04.29.008
Citation: ELHAM Elxat, MENG Yu, BAI Guoxing, GU Qing, WANG Guodong, CHANG Xinrui, HUANG JianXiu, ZHENG Yan. Path tracking for car-like robots based on feed-forward nonlinear model predictive control[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.04.29.008

基于前馈非线性模型预测控制的类车机器人路径跟踪

Path tracking for car-like robots based on feed-forward nonlinear model predictive control

  • 摘要: 类车机器人由于零件标准化程度低,侧偏刚度等轮胎力学参数难以准确获得,存在动力学建模十分困难的问题,因此现有研究工作通常以运动学模型作为类车机器人的控制模型,但由于其运动学模型存在模型失配,导致类车机器人与参考路径之间的误差、类车机器人的前轮转角和前轮转角速度出现剧烈振荡现象. 针对前述问题,本文基于非线性模型预测控制(Nonlinear model predictive control, NMPC)的滚动优化原理,引入基于逆运动学模型的前馈转角信息,将前轮转向角作为预测模型的第四维,提出了一种基于前馈非线性模型预测控制(Feedforward NMPC, FNMPC)的类车机器人路径跟踪控制算法. 并通过Simulink和CarSim进行了联合仿真,结果表明FNMPC有效减小了模型失配导致的振荡现象,同时具有较高的跟踪精度. 其中前馈非线性模型预测控制器的位移误差幅值不超过0.1106 m,航向误差幅值不超过0.1253 rad. 在相同工况下,线性模型预测控制、前馈线性模型预测控制、纯跟踪控制和Stanley控制误差发散,而本文提出的FNMPC相比已有NMPC跟踪精度更高,且控制增量绝对累计值相比NMPC控制器减小67.53%. 通过线控类车机器人底盘作为实验平台完成的测试结果表明,NMPC系统在进入弯道时出现控制失控现象,在相同工况下,FNMPC系统能够有效完成对参考路径的跟踪,同时将位移误差幅值控制在0.1624 m以内,航向误差幅值控制在0.1138 rad以内.

     

    Abstract: Car-like robots often struggle with dynamics modeling owing to the use of nonstandardized parts and the complexity of accurately determining mechanical parameters, particularly tire characteristics like lateral deflection stiffness. Consequently, most current research and applications have relied on kinematic models for control, which frequently lead to inaccuracies and mismatches. These issues result in significant errors between actual and desired paths, causing erratic oscillations in the front wheel angle and angular velocity and adversely affecting the robot’s performance and smoothness. To address these issues, this paper introduces a novel approach: Feed-forward nonlinear model predictive control (FNMPC). This method is built on the principles of inverse kinematics and rolling optimization. Unlike other traditional methods, FNMPC incorporates feed-forward corner information into its predictive model, treating the front wheel angle as an additional dimension. This enhancement allows the model to better predict and correct deviations, thereby improving path-tracking accuracy. Extensive simulations conducted using Simulink and CarSim demonstrated the efficacy of the FNMPC approach. Results indicated that FNMPC significantly reduces the oscillations caused by model inaccuracies while maintaining high tracking accuracy. Specifically, FNMPC managed to keep displacement errors below 0.1106 m and heading errors within 0.1253 radians. When compared with other control strategies such as linear model predictive control, feed-forward linear model predictive control, pure tracking control, and Stanley control, FNMPC consistently demonstrated smaller and less dispersed errors, highlighting its superior performance in handling the complex dynamics of car-like robots. Moreover, FNMPC showed a remarkable improvement over traditional nonlinear model predictive control (NMPC), reducing the absolute cumulative control increment by 67.53% at its maximum values. Experimental validations using a wire-controlled car-like robot further validated FNMPC’s practical benefits. In these tests, the robot under FNMPC control maintained displacement errors within 0.1624 m and heading errors within 0.1138 radians, whereas traditional NMPC lost control of entering curves. In summary, FNMPC presents a substantial advancement in controlling car-like robots, offering enhanced accuracy and smoothness over existing methods. By effectively incorporating feed-forward corner information into the predictive model, FNMPC addresses the inherent challenges in car-like robot dynamics more efficiently. This approach not only improves control performance but also offers a more reliable and accurate method that could enhance the development of car-like robotic systems.

     

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