Optimal Design of MPC Autonomous Vehicle Trajectory Tracking Controller Considering Variable Time Domain

IF 2.9 4区 综合性期刊 Q1 Multidisciplinary
Hao Ma, Wenhui Pei, Qi Zhang
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引用次数: 0

Abstract

In recent years, with the indepth research on driverless technology, model predictive control theory was extensively applied in the field of vehicle control. In order to improve the accurate tracking of reference trajectories by driverless vehicles, a model predictive control trajectory tracking controller for driverless vehicles optimized by an improved sparrow search algorithm is proposed. Firstly, an objective function with constraints is added to the model predictive control trajectory tracking controller by establishing the vehicle dynamics model; Secondly, the improved sparrow search algorithm is enhanced to speed up convergence and expand the program's search capabilities; Then, in order to discover the best value, the model predictive control trajectory tracking controller's prediction time domain and control time domain are optimized using the improved sparrow search algorithm; Finally, to confirm the method's viability, collaborative simulations in Simulink/Carsim were completed. The simulation results show that the lateral errors generated by the improved sparrow search algorithm-based optimized model predictive control trajectory tracking controller are reduced by 53.53% and 65.44%, respectively, when the vehicle speed is 36 km/h, compared with the traditional model predictive control trajectory tracking controller. When the vehicle speed is 54 km/h, the lateral deviations are reduced by 81.08% and 86.76%, respectively. In addition, the optimized model predictive control trajectory tracking controller improves the accuracy and at the same time, the driving stability of the control vehicle is significantly improved.

Abstract Image

考虑变时域的 MPC 自主车辆轨迹跟踪控制器的优化设计
近年来,随着无人驾驶技术研究的深入,模型预测控制理论被广泛应用于车辆控制领域。为了提高无人驾驶车辆对参考轨迹的精确跟踪,提出了一种通过改进的麻雀搜索算法优化的无人驾驶车辆模型预测控制轨迹跟踪控制器。首先,通过建立车辆动力学模型,为模型预测控制轨迹跟踪控制器添加了带约束条件的目标函数;其次,增强了改进的麻雀搜索算法,以加快收敛速度并扩展程序的搜索能力;然后,为了发现最佳值,利用改进的麻雀搜索算法优化了模型预测控制轨迹跟踪控制器的预测时域和控制时域;最后,为了证实该方法的可行性,在 Simulink/Carsim 中完成了协同仿真。仿真结果表明,与传统的模型预测控制轨迹跟踪控制器相比,当车速为 36 km/h 时,基于改进的麻雀搜索算法的优化模型预测控制轨迹跟踪控制器产生的横向误差分别减少了 53.53% 和 65.44%。当车速为 54 km/h 时,横向偏差分别减少了 81.08% 和 86.76%。此外,优化后的模型预测控制轨迹跟踪控制器在提高精度的同时,还显著提高了控制车辆的行驶稳定性。
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来源期刊
Arabian Journal for Science and Engineering
Arabian Journal for Science and Engineering 综合性期刊-综合性期刊
CiteScore
5.20
自引率
3.40%
发文量
0
审稿时长
4.3 months
期刊介绍: King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE). AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.
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