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自适应时域参数MPC的智能车辆轨迹跟踪控制 被引量:1

Intelligent Vehicle Trajectory Tracking Control Based on Adaptive Time Domain Parameter MPC
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摘要 为了解决智能车辆在低附着路面下主动转向跟踪控制的稳定性和控制精度问题,提出了一种基于自适应时域参数的智能车辆轨迹跟踪控制策略。基于车辆动力学模型和模型预测控制算法(MPC)建立线性时变MPC控制器,并加入包括轮胎侧偏角约束、质心侧偏角约束以及前轮转角约束的动力学约束,求解出最优前轮转向角。分析控制器中的时域参数对控制效果的影响,设计了一种自适应时域参数控制器,能够根据获取的车辆速度,将求解得到最优的预测时域和控制时域参数输入到控制器,提高控制器在不同速度下的控制精度和稳定性。通过搭建MATLAB/SimuLink与CarSim联合仿真平台,在低附着路面情况下对固定时域控制器和自适应时域控制器进行对比仿真实验。结果表明:自适应时域控制器能够有效改善控制器的性能、减少横向偏差、提高轨迹跟踪控制精度,同时对不同速度也具有较强的适应性,车辆质心侧偏角也控制在0°~15°内,有效保证了车辆行驶的稳定性。 In order to solve the problem of stability and control accuracy of intelligent vehicle active steering tracking control on low adhesion road surface,an intelligent vehicle trajectory tracking control strategy based on adaptive time domain parameters was proposed.Based on the vehicle dynamics model and model predictive control algorithm(MPC),a linear time-varying MPC controller was established,and dynamic constraints including tire side deflection constraints,centroid side deflection constraints and front wheel angle constraints were added to solve the optimal front wheel steering angle.The influence of time domain parameters in the controller on the control effect was analyzed,and an adaptive time domain parameter controller was designed.According to the acquired vehicle speed,the optimal predictive time domain and control time domain parameters were obtained and input to the controller,improving the control accuracy and stability of the controller at different speeds.By building the MATLAB/SimuLink and CarSim co-simulation platform,the fixed time domain controller and adaptive time domain controller were compared and simulated with the condition of low adhesion road surface.The results showed that the adaptive time-domain controller could effectively improve the performance of the controller,reduce the lateral deviation,and improve the control accuracy of trajectory tracking.At the same time,it also had strong adaptability to different speeds,and the lateral deflection angle of the vehicle center of mass was controlled within 0°-1.5°,which effectively ensured the stability of the vehicle.
作者 刘志强 张晴 LIU Zhiqiang;ZHANG Qing(College of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,China)
出处 《郑州大学学报(工学版)》 北大核心 2024年第1期47-53,共7页 Journal of Zhengzhou University(Engineering Science)
基金 国家自然科学基金资助项目(72001095)。
关键词 智能车辆 轨迹跟踪 模型预测控制 自适应 前轮主动转向 intelligent vehicle trajectory tracking model predictive control adaptive active front steering
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