Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma
{"title":"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","authors":"Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma","doi":"10.1016/j.conengprac.2024.106137","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106137"},"PeriodicalIF":5.4000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Active disturbance rejection path tracking control of vehicles with adaptive observer bandwidth based on Q-learning\",\"authors\":\"Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma\",\"doi\":\"10.1016/j.conengprac.2024.106137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":\"154 \",\"pages\":\"Article 106137\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S096706612400296X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706612400296X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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.