Ebrahim Muhammad, Vahid Behnamgol, Ahmadreza Vali, Abdoreza Kashaninia, Mohammad Mirzaei
{"title":"自适应MIMO滑模控制与协调的AFS和DYC系统增强车辆稳定性","authors":"Ebrahim Muhammad, Vahid Behnamgol, Ahmadreza Vali, Abdoreza Kashaninia, Mohammad Mirzaei","doi":"10.1002/asjc.3695","DOIUrl":null,"url":null,"abstract":"<p>This paper presents an Adaptive MIMO Sliding Mode Control (AMSMC) strategy for coordinating Active Front Steering (AFS) and Direct Yaw Control (DYC) systems to enhance vehicle stability and handling under uncertain conditions. Traditional Single Input Single Output (SISO) models fail to capture the complex interactions and nonlinearities inherent in vehicle dynamics, leading to suboptimal performance. The proposed method addresses these limitations by utilizing a Multiple Input Multiple Output (MIMO) framework, which accurately models the nonlinear interactions between AFS and DYC systems. Additionally, the method introduces a dynamic coefficient in the sliding mode control, enabling real-time adaptation to unknown uncertainties and enhancing robustness. The slip angle is estimated by an observer and a first-order delay is introduced into the lateral forces modeling to improve the accuracy of vehicle dynamics representation. The stability of the closed-loop system is further validated using the Lyapunov method. The performance of the proposed controller is evaluated considering the coefficient of road tire friction and parametric uncertainties in vehicle parameters, such as total mass, moment of inertia, and tire stiffness. The efficacy of this method has been rigorously confirmed through MATLAB simulations using a nonlinear 8-DOF vehicle model, which includes parameter uncertainties to ensure the control strategy's resilience to varying road conditions. The simulation results demonstrate significant improvements in the vehicle's stability and handling performance across a variety of driving maneuvers.</p>","PeriodicalId":55453,"journal":{"name":"Asian Journal of Control","volume":"27 3","pages":"1442-1454"},"PeriodicalIF":2.7000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive MIMO sliding mode control for enhanced vehicle stability with coordinated AFS and DYC systems\",\"authors\":\"Ebrahim Muhammad, Vahid Behnamgol, Ahmadreza Vali, Abdoreza Kashaninia, Mohammad Mirzaei\",\"doi\":\"10.1002/asjc.3695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper presents an Adaptive MIMO Sliding Mode Control (AMSMC) strategy for coordinating Active Front Steering (AFS) and Direct Yaw Control (DYC) systems to enhance vehicle stability and handling under uncertain conditions. Traditional Single Input Single Output (SISO) models fail to capture the complex interactions and nonlinearities inherent in vehicle dynamics, leading to suboptimal performance. The proposed method addresses these limitations by utilizing a Multiple Input Multiple Output (MIMO) framework, which accurately models the nonlinear interactions between AFS and DYC systems. Additionally, the method introduces a dynamic coefficient in the sliding mode control, enabling real-time adaptation to unknown uncertainties and enhancing robustness. The slip angle is estimated by an observer and a first-order delay is introduced into the lateral forces modeling to improve the accuracy of vehicle dynamics representation. The stability of the closed-loop system is further validated using the Lyapunov method. The performance of the proposed controller is evaluated considering the coefficient of road tire friction and parametric uncertainties in vehicle parameters, such as total mass, moment of inertia, and tire stiffness. The efficacy of this method has been rigorously confirmed through MATLAB simulations using a nonlinear 8-DOF vehicle model, which includes parameter uncertainties to ensure the control strategy's resilience to varying road conditions. The simulation results demonstrate significant improvements in the vehicle's stability and handling performance across a variety of driving maneuvers.</p>\",\"PeriodicalId\":55453,\"journal\":{\"name\":\"Asian Journal of Control\",\"volume\":\"27 3\",\"pages\":\"1442-1454\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Journal of Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/asjc.3695\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asjc.3695","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Adaptive MIMO sliding mode control for enhanced vehicle stability with coordinated AFS and DYC systems
This paper presents an Adaptive MIMO Sliding Mode Control (AMSMC) strategy for coordinating Active Front Steering (AFS) and Direct Yaw Control (DYC) systems to enhance vehicle stability and handling under uncertain conditions. Traditional Single Input Single Output (SISO) models fail to capture the complex interactions and nonlinearities inherent in vehicle dynamics, leading to suboptimal performance. The proposed method addresses these limitations by utilizing a Multiple Input Multiple Output (MIMO) framework, which accurately models the nonlinear interactions between AFS and DYC systems. Additionally, the method introduces a dynamic coefficient in the sliding mode control, enabling real-time adaptation to unknown uncertainties and enhancing robustness. The slip angle is estimated by an observer and a first-order delay is introduced into the lateral forces modeling to improve the accuracy of vehicle dynamics representation. The stability of the closed-loop system is further validated using the Lyapunov method. The performance of the proposed controller is evaluated considering the coefficient of road tire friction and parametric uncertainties in vehicle parameters, such as total mass, moment of inertia, and tire stiffness. The efficacy of this method has been rigorously confirmed through MATLAB simulations using a nonlinear 8-DOF vehicle model, which includes parameter uncertainties to ensure the control strategy's resilience to varying road conditions. The simulation results demonstrate significant improvements in the vehicle's stability and handling performance across a variety of driving maneuvers.
期刊介绍:
The Asian Journal of Control, an Asian Control Association (ACA) and Chinese Automatic Control Society (CACS) affiliated journal, is the first international journal originating from the Asia Pacific region. The Asian Journal of Control publishes papers on original theoretical and practical research and developments in the areas of control, involving all facets of control theory and its application.
Published six times a year, the Journal aims to be a key platform for control communities throughout the world.
The Journal provides a forum where control researchers and practitioners can exchange knowledge and experiences on the latest advances in the control areas, and plays an educational role for students and experienced researchers in other disciplines interested in this continually growing field. The scope of the journal is extensive.
Topics include:
The theory and design of control systems and components, encompassing:
Robust and distributed control using geometric, optimal, stochastic and nonlinear methods
Game theory and state estimation
Adaptive control, including neural networks, learning, parameter estimation
and system fault detection
Artificial intelligence, fuzzy and expert systems
Hierarchical and man-machine systems
All parts of systems engineering which consider the reliability of components and systems
Emerging application areas, such as:
Robotics
Mechatronics
Computers for computer-aided design, manufacturing, and control of
various industrial processes
Space vehicles and aircraft, ships, and traffic
Biomedical systems
National economies
Power systems
Agriculture
Natural resources.