【车辆控制】基于LQR的自动驾驶车辆控制附Matlab代码和报告

原创于 2025-12-03 15:44:43 发布 · 605 阅读

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#自动驾驶 #matlab #人工智能

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🔥 内容介绍

自动驾驶车辆的技术发展对鲁棒控制系统提出了需求，该系统需具备确保稳定性、精度及抗干扰能力的特性。本项目旨在采用线性二次调节器（LQR）方法，设计并实现一套鲁棒控制策略。为捕捉车辆的基本运动动力学特性，项目引入了运动学自行车模型，并围绕直线行驶状态对该模型进行线性化处理，进而推导适用于最优控制设计的状态空间表达式。

控制目标是通过 LQR 框架，在最小化参考轨迹偏差的同时优化控制量。设计过程包含多个关键步骤：为成本函数选择合适的加权矩阵、求解连续时间代数黎卡提方程（CARE），以及获取最优反馈增益矩阵。

系统采用 Python 进行仿真，仿真场景中车辆从初始位置出发，以抵达指定目标位置为任务。控制器能够有效稳定车辆行驶状态，实现平稳转向与加速，且横向误差极小。为验证控制器的鲁棒性，项目中引入了小幅干扰与模型不确定性因素。仿真结果表明，该 LQR 控制器可实现预期的轨迹跟踪性能，确保系统稳定运行。

本项目证实，LQR 控制器是实现自动驾驶车辆鲁棒控制的可行方案，具备简洁性、高效性及坚实的理论保障。未来可通过融入动力学模型、增加避障功能及开展实验验证等方式进一步拓展研究，以提升系统性能。

⛳️ 运行结果

📣 部分代码

🔗 参考文献

[1] S. Abdallaoui, H. Ikaouassen, A. Kribèche, A. Chaibet, and E.-H. Aglzim, "Advancing autonomous vehicle control systems: An in-depth overview of decision-making and manoeuvre execution state of the art," *The Journal of Engineering*, vol. 2023, 2023, doi: 10.1049/tje2.12333.

[2] C. Samak, T. Samak, and S. Kandhasamy, "Control Strategies for Autonomous Vehicles," *arXiv preprint arXiv:2011.08729*, 2020, doi: 10.48550/arXiv.2011.08729.

[3] T. Gindele, S. Brechtel, and R. Dillmann, "Learning driver behavior models from traffic observations for decision making and planning," *IEEE Intelligent Transportation Systems Magazine*, vol. 7, no. 1, pp. 69–79, 2015.

[4] P. Falcone, F. Borrelli, H. E. Tseng, J. Asgari, and D. Hrovat, "MPC-based yaw and lateral stabilization via active front steering and braking," *Vehicle System Dynamics*, vol. 46, no. 6, pp. 611–628, 2008.

[5] M. Buehler, K. Iagnemma, and S. Singh, *The DARPA Urban Challenge: Autonomous Vehicles in City Traffic*, Springer Tracts in Advanced Robotics, vol. 56, 2009.

[6] J. Ziegler et al., "Making Bertha Drive — An Autonomous Journey on a Historic Route," *IEEE Intelligent Transportation Systems Magazine*, vol. 6, no. 2, pp. 8–20, 2014.

[7] S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A survey of deep learning techniques for autonomous driving," *Journal of Field Robotics*, vol. 37, no. 3, pp. 362–386, 2020.

[8] R. E. Kalman, "A new approach to linear filtering and prediction problems," *Journal of Basic Engineering*, vol. 82, no. 1, pp. 35–45, 1960.

[9] M. Kuderer, S. Gulati, and W. Burgard, "Learning driving styles for autonomous vehicles from demonstration," in *Proc. IEEE Int. Conf. Robotics and Automation (ICRA)*, 2015, pp. 2641–2646.

[10] S. Ulbrich and M. Maurer, "Probabilistic online POMDP decision making for lane changes in fully automated driving," in *Proc. IEEE Intelligent Vehicles Symposium (IV)*, 2015, pp. 1074–1079.