基于 Q-learning 的自适应观测器带宽的车辆主动干扰抑制路径跟踪控制

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Control Engineering Practice Pub Date : 2024-11-04 DOI:10.1016/j.conengprac.2024.106137
Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma
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引用次数: 0

摘要

本文介绍了一种新的车辆路径跟踪控制算法,重点是保持跟踪精度和尽量减少方向盘摆动,以提高机构寿命和乘客舒适度。利用车辆运动学模型制定了横向误差的二阶动态方程,并将偏航误差整合到标准的一阶动态方程中。设计了一个比例-派生(PD)控制器,其中包含一个 "扩展状态",用于补偿模型与实际车辆动态之间的差异,即 "总干扰"。这种 "总扰动 "由扩展状态观测器(ESO)进行观测,并采用扰动抑制法则与 PD 控制器相结合,以实现所需的偏航率。为了提高车辆的安全性和舒适性,根据车辆的运动和动态原理,提出了偏航率动态约束。通过反馈线性化处理车辆的非线性动态,将目标偏航率转换为所需的转向角,然后由线控转向系统执行。利用 Q-learning 实现了调整 ESO 带宽的自适应在线算法。这种优化旨在平衡跟踪精度和方向盘振荡。数学分析证实了时变带宽 ESO 和整个系统的稳定性,确保了有限的估计和控制误差。与经典斯坦利和模型预测控制(MPC)方法的实验对比证明了该算法的有效性,可将横向误差保持在 ±0.1 米以内。
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Active disturbance rejection path tracking control of vehicles with adaptive observer bandwidth based on Q-learning
In this paper, a novel algorithm for vehicle path tracking control is introduced, focusing on maintaining tracking accuracy and minimizing steering wheel oscillation to enhance mechanism lifespan and passenger comfort. Vehicle kinematics model is utilized to formulate a second-order dynamic equation for lateral error, integrating yaw error into the standard first-order dynamic equation. A Proportional-Derivative (PD) controller is designed, incorporating an ‘extended state’ to compensate for the discrepancy between the model and actual vehicle dynamics, termed as the ‘total disturbance’. This ‘total disturbance’ is observed by an Extended State Observer (ESO), and a disturbance rejection law, combined with the PD controller, is employed to achieve the desired yaw rate. For improved vehicle safety and comfort, a dynamic constraint on the yaw rate, based on the vehicle’s motion and dynamic principles, is proposed. The vehicle’s nonlinear dynamics are addressed through feedback linearization, converting the target yaw rate into the required steering angle, which is then executed by the steer-by-wire system. An adaptive online algorithm for adjusting the ESO bandwidth, using Q-learning, is implemented. This optimization aims to balance tracking accuracy and steering wheel oscillation. A mathematical analysis confirms the stability of the time-varying bandwidth ESO and the overall system, ensuring limited estimation and control errors. Experimental comparison with the classical Stanley and Model Predictive Control (MPC) method demonstrates the algorithm’s effectiveness, maintaining lateral error within ±0.1 m.
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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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