Pub Date : 2024-05-16DOI: 10.1177/09544070241248030
Yang Sun, Chao Wang, Haiyang Wang, Bin Tian, Haonan Ning
In order to ensure the following accuracy and improve the operational stability of four-wheel independent driving and four-wheel independent steering autonomous vehicles, this paper proposes a path-following control strategy based on the β−[Formula: see text] phase plane. First, based on the kinematic relationship between the vehicle and the reference path, the linear matrix inequality theory is used to design the H∞ controller to obtain the wheel steering angle. Then, the vehicle steering system is subjected to nonlinear analysis according to phase plane theory, and a partition region controller is designed. In the unstable region, the instability degree of the vehicle is predicted by quadratic polynomial extrapolation and the particle swarm optimization PID controller is designed to determine the required yaw moment to restore the vehicle to the stable region. In the stable region, a fuzzy sliding mode controller is adopted to determine the required yaw moment so that the actual state variable of the vehicle follows the ideal state variable. Finally, the optimal tire force distributor is designed such that the required forces are allocated to all four wheels. The simulation results show that the proposed method can obtain excellent path-following performance and stability performance under different driving conditions.
{"title":"Path-following control of 4WIS/4WID autonomous vehicles considering vehicle stability based on phase plane","authors":"Yang Sun, Chao Wang, Haiyang Wang, Bin Tian, Haonan Ning","doi":"10.1177/09544070241248030","DOIUrl":"https://doi.org/10.1177/09544070241248030","url":null,"abstract":"In order to ensure the following accuracy and improve the operational stability of four-wheel independent driving and four-wheel independent steering autonomous vehicles, this paper proposes a path-following control strategy based on the β−[Formula: see text] phase plane. First, based on the kinematic relationship between the vehicle and the reference path, the linear matrix inequality theory is used to design the H∞ controller to obtain the wheel steering angle. Then, the vehicle steering system is subjected to nonlinear analysis according to phase plane theory, and a partition region controller is designed. In the unstable region, the instability degree of the vehicle is predicted by quadratic polynomial extrapolation and the particle swarm optimization PID controller is designed to determine the required yaw moment to restore the vehicle to the stable region. In the stable region, a fuzzy sliding mode controller is adopted to determine the required yaw moment so that the actual state variable of the vehicle follows the ideal state variable. Finally, the optimal tire force distributor is designed such that the required forces are allocated to all four wheels. The simulation results show that the proposed method can obtain excellent path-following performance and stability performance under different driving conditions.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.1177/09544070241246022
Huilong Yu, Erhang Li, Matteo Corno, S. Savaresi
In recent years, autonomous tractor semi-trailers have been considered a promising solution for transportation on highways, ports, and large logistics centers to improve safety and efficiency. However, due to the complicated dynamics introduced by articulation structure, there are still challenges in achieving high-precision path tracking and stability control for tractor semi-trailers, especially in critical scenarios. In this work, a bi-level path tracking control scheme for the autonomous tractor semi-trailer is proposed. At the higher level, a robust model predictive controller coordinating active front steering and differential braking is devised for addressing path tracking, stability control and robustness for an autonomous tractor semi-trailer. At the lower level, incorporating a logic switching mechanism, the designed optimal braking torque distribution module and the PID-based longitudinal control module prioritize the implementation of differential braking and target speed tracking. Simulation results demonstrate that the proposed control strategy offers remarkable path tracking performance in three designed testing scenarios.
{"title":"Bi-level path tracking control of tractor semi-trailers by coordinated active front steering and differential braking","authors":"Huilong Yu, Erhang Li, Matteo Corno, S. Savaresi","doi":"10.1177/09544070241246022","DOIUrl":"https://doi.org/10.1177/09544070241246022","url":null,"abstract":"In recent years, autonomous tractor semi-trailers have been considered a promising solution for transportation on highways, ports, and large logistics centers to improve safety and efficiency. However, due to the complicated dynamics introduced by articulation structure, there are still challenges in achieving high-precision path tracking and stability control for tractor semi-trailers, especially in critical scenarios. In this work, a bi-level path tracking control scheme for the autonomous tractor semi-trailer is proposed. At the higher level, a robust model predictive controller coordinating active front steering and differential braking is devised for addressing path tracking, stability control and robustness for an autonomous tractor semi-trailer. At the lower level, incorporating a logic switching mechanism, the designed optimal braking torque distribution module and the PID-based longitudinal control module prioritize the implementation of differential braking and target speed tracking. Simulation results demonstrate that the proposed control strategy offers remarkable path tracking performance in three designed testing scenarios.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1177/09544070241245766
Jeremy S Liang
Recent intelligent systems as required for Industry 4.0 merge data from diverse domains and more gradually demand data to be combined with field knowledge. The convergence and scenarization of data permits for the high-level inferring required to create knowledge based on the data under consideration. In this study, a framework for an ontology-assisted multi-scenario inference platform is proposed to help some of the desirable platform qualities in automotive troubleshooting service involve message clarity, platform interoperability, and elegant maturing. This framework is constructed through the model with triple modes (Conception-Expression-Manipulation, CEM), which is a communication-based framework. This proposed framework applies a two-tier class with three performers and can combine and use multiple scenarios. There are several characteristics, including flexibility, interaction, and handily maintenance. The transformation of data is separated from one element of the platform and thus does not implicate several other elements. A field of employment can be easily decided by the utilization of prototypes and field-norm elements. This proposed framework is instantiated applying an instance study including data from the troubleshooting tasks of automotive system.
{"title":"A novel ontology-assisted inference platform in automotive troubleshooting tasks","authors":"Jeremy S Liang","doi":"10.1177/09544070241245766","DOIUrl":"https://doi.org/10.1177/09544070241245766","url":null,"abstract":"Recent intelligent systems as required for Industry 4.0 merge data from diverse domains and more gradually demand data to be combined with field knowledge. The convergence and scenarization of data permits for the high-level inferring required to create knowledge based on the data under consideration. In this study, a framework for an ontology-assisted multi-scenario inference platform is proposed to help some of the desirable platform qualities in automotive troubleshooting service involve message clarity, platform interoperability, and elegant maturing. This framework is constructed through the model with triple modes (Conception-Expression-Manipulation, CEM), which is a communication-based framework. This proposed framework applies a two-tier class with three performers and can combine and use multiple scenarios. There are several characteristics, including flexibility, interaction, and handily maintenance. The transformation of data is separated from one element of the platform and thus does not implicate several other elements. A field of employment can be easily decided by the utilization of prototypes and field-norm elements. This proposed framework is instantiated applying an instance study including data from the troubleshooting tasks of automotive system.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1177/09544070241246046
Xuzhao Hou, Yue Ma, Changle Xiang
Autonomous and remote-controlled tracked vehicles are freeing humans from exhausting off-road maneuvers. Advanced motion control is a necessity to achieve high mobility and enhanced safety with less dependence on operator skill. Tracked vehicles may slide laterally or roll over under large centrifugal forces. Precise yaw motion and sideslip prevention are required for steering controller development. A switching control architecture is proposed for the underactuated tracked vehicle in this study. Two control laws for yaw rate tracking and anti-sideslip are proposed respectively based on second-order disturbance observers (DO-2s) with a given bandwidth. The controller is optimized for the two objectives using Nash bargaining method. The proposed steering controller is verified on a small electric track vehicle. Under large disturbance, the optimized DO-2-based controller prevents potential sideslip and reduces the yaw rate tracking error by 42.6% compared with LADRC. The chattering induced by switching is moderate because the estimated disturbances are smoothly switched.
{"title":"Steering control of electric tracked vehicle based on second-order disturbance observer and multiobjective optimization","authors":"Xuzhao Hou, Yue Ma, Changle Xiang","doi":"10.1177/09544070241246046","DOIUrl":"https://doi.org/10.1177/09544070241246046","url":null,"abstract":"Autonomous and remote-controlled tracked vehicles are freeing humans from exhausting off-road maneuvers. Advanced motion control is a necessity to achieve high mobility and enhanced safety with less dependence on operator skill. Tracked vehicles may slide laterally or roll over under large centrifugal forces. Precise yaw motion and sideslip prevention are required for steering controller development. A switching control architecture is proposed for the underactuated tracked vehicle in this study. Two control laws for yaw rate tracking and anti-sideslip are proposed respectively based on second-order disturbance observers (DO-2s) with a given bandwidth. The controller is optimized for the two objectives using Nash bargaining method. The proposed steering controller is verified on a small electric track vehicle. Under large disturbance, the optimized DO-2-based controller prevents potential sideslip and reduces the yaw rate tracking error by 42.6% compared with LADRC. The chattering induced by switching is moderate because the estimated disturbances are smoothly switched.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1177/09544070241244858
Xiuchen Cao, Yingfeng Cai, Yicheng Li, Xiaoqiang Sun, Long Chen, Hai Wang
In order to solve the accuracy problem of trajectory tracking control method based on data-driven model, an intelligent vehicle trajectory tracking control method based on physics-informed neural network (PINN) vehicle dynamics model is proposed. Aiming at the problem of poor interpretability of data-driven model, a vehicle dynamics model based on the PINN is established, and the physics-driven deep learning method is used instead of the data-driven deep learning method to obtain the dynamic characteristics of the intelligent vehicle, to benefit from both the physical-based method and the data-driven method. A sequential training method is also proposed to solve the coupling problem when training multiple PINNs simultaneously. The model takes the nonlinearity of the neural network model and physical interpretability into consideration compared to the standard neural network model. Then, based on the PINN vehicle dynamics model, a trajectory tracking controller based on the iterative linear quadratic regulator (ILQR) control algorithm is developed. The optimal control law is derived by optimizing the ILQR control algorithm to implement the intelligent vehicle’s precise and stable tracking for the desired trajectory. The Levenberg-Marquardt (LM) algorithm and line search technology are used and damping factor adjustment rules are set up to enhance the convergence performance of the ILQR control algorithm. In order to verify the effectiveness of the proposed method, the simulation is conducted under the condition of double lane change. The simulation results demonstrate that the proposed method can track the reference trajectory accurately under the limited conditions. Its control performance is much better than other algorithms.
{"title":"Intelligent vehicle trajectory tracking control based on physics-informed neural network dynamics model","authors":"Xiuchen Cao, Yingfeng Cai, Yicheng Li, Xiaoqiang Sun, Long Chen, Hai Wang","doi":"10.1177/09544070241244858","DOIUrl":"https://doi.org/10.1177/09544070241244858","url":null,"abstract":"In order to solve the accuracy problem of trajectory tracking control method based on data-driven model, an intelligent vehicle trajectory tracking control method based on physics-informed neural network (PINN) vehicle dynamics model is proposed. Aiming at the problem of poor interpretability of data-driven model, a vehicle dynamics model based on the PINN is established, and the physics-driven deep learning method is used instead of the data-driven deep learning method to obtain the dynamic characteristics of the intelligent vehicle, to benefit from both the physical-based method and the data-driven method. A sequential training method is also proposed to solve the coupling problem when training multiple PINNs simultaneously. The model takes the nonlinearity of the neural network model and physical interpretability into consideration compared to the standard neural network model. Then, based on the PINN vehicle dynamics model, a trajectory tracking controller based on the iterative linear quadratic regulator (ILQR) control algorithm is developed. The optimal control law is derived by optimizing the ILQR control algorithm to implement the intelligent vehicle’s precise and stable tracking for the desired trajectory. The Levenberg-Marquardt (LM) algorithm and line search technology are used and damping factor adjustment rules are set up to enhance the convergence performance of the ILQR control algorithm. In order to verify the effectiveness of the proposed method, the simulation is conducted under the condition of double lane change. The simulation results demonstrate that the proposed method can track the reference trajectory accurately under the limited conditions. Its control performance is much better than other algorithms.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1177/09544070241235719
Cheng Peng, Li Chen, D. Miao, Shenglai Fu
Mode transitions of multi-mode hybrid electric vehicles need careful coordination among the engine torque, motor torque, and clutch transmitted torque. However, the three torques have different actuation delays which make it difficult to ensure mode transition performance. A non-square internal model controller (IMC) is proposed in this paper for mode transition during which the input number is greater than the output number and each input has a different actuation delay. At first, the Moore-Penrose generalized inverse matrix is introduced to solve the inverse of the non-square matrix and make the IMC applicable. Secondly, the all-pole method is adopted to approximate the three delay models. Based on these specialized techniques, the tracking controller and anti-disturbance controller of the IMC are derived. Experiment results verify the effectiveness of the proposed controller.
{"title":"Non-square internal model control for mode transition of hybrid electric vehicles with multiple time delays","authors":"Cheng Peng, Li Chen, D. Miao, Shenglai Fu","doi":"10.1177/09544070241235719","DOIUrl":"https://doi.org/10.1177/09544070241235719","url":null,"abstract":"Mode transitions of multi-mode hybrid electric vehicles need careful coordination among the engine torque, motor torque, and clutch transmitted torque. However, the three torques have different actuation delays which make it difficult to ensure mode transition performance. A non-square internal model controller (IMC) is proposed in this paper for mode transition during which the input number is greater than the output number and each input has a different actuation delay. At first, the Moore-Penrose generalized inverse matrix is introduced to solve the inverse of the non-square matrix and make the IMC applicable. Secondly, the all-pole method is adopted to approximate the three delay models. Based on these specialized techniques, the tracking controller and anti-disturbance controller of the IMC are derived. Experiment results verify the effectiveness of the proposed controller.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The timely intervention of the assisted driving system is the key to improving the handling stability and roll stability of the vehicle, and the vehicle stability region serves as the core basis for determining the intervention timing of the assisted driving system. With the aim of modeling and analyzing the vehicle stability region, a three-degree-of-freedom (3-DOF) vehicle dynamics model including yaw, roll, and lateral motions, as well as a nonlinear Magic Formula tire model are established in this paper. Based on this, a simplified but improved cubic tire model is developed to accurately fit the tire lateral force of Magic Formula tire model within a larger range of slip angles. Subsequently, using the Lyapunov method, the roll stability region and the yaw stability region are respectively constructed, and the accuracy verification and robustness analysis of the established stability region are conducted in the Matlab/Simulink environment. Finally, a model-free adaptive control method is employed to keep the vehicle state within the stability region, without tracking specific vehicle state objectives. The study in this paper can provide theoretical support for stability boundary determination, formulation of intervention timing for assisted driving stability control, and coordination control of vehicle stability and anti-rollover with local compatibility or even conflicts.
辅助驾驶系统的及时干预是提高车辆操控稳定性和侧倾稳定性的关键，而车辆稳定区域是确定辅助驾驶系统干预时机的核心依据。为了对车辆稳定区域进行建模和分析，本文建立了包括偏航、侧倾和横向运动在内的三自由度（3-DOF）车辆动力学模型以及非线性魔术配方轮胎模型。在此基础上，建立了简化但改进的立方轮胎模型，以在更大的滑移角范围内精确拟合 Magic Formula 轮胎模型的轮胎侧向力。随后，利用 Lyapunov 方法分别构建了滚动稳定区域和偏航稳定区域，并在 Matlab/Simulink 环境下对所建立的稳定区域进行了精度验证和鲁棒性分析。最后，在不跟踪特定车辆状态目标的情况下，采用无模型自适应控制方法将车辆状态保持在稳定区域内。本文的研究可为稳定性边界的确定、辅助驾驶稳定性控制干预时机的制定，以及车辆稳定性与防侧翻的协调控制提供理论支持，并具有局部兼容性甚至冲突性。
{"title":"Modeling and analysis of vehicle stability region based on Lyapunov and coordinated control","authors":"Minghao Zhang, Xiaojian Wu, Jiansheng Liu, Aichun Wang, Huihua Jiang","doi":"10.1177/09544070241244406","DOIUrl":"https://doi.org/10.1177/09544070241244406","url":null,"abstract":"The timely intervention of the assisted driving system is the key to improving the handling stability and roll stability of the vehicle, and the vehicle stability region serves as the core basis for determining the intervention timing of the assisted driving system. With the aim of modeling and analyzing the vehicle stability region, a three-degree-of-freedom (3-DOF) vehicle dynamics model including yaw, roll, and lateral motions, as well as a nonlinear Magic Formula tire model are established in this paper. Based on this, a simplified but improved cubic tire model is developed to accurately fit the tire lateral force of Magic Formula tire model within a larger range of slip angles. Subsequently, using the Lyapunov method, the roll stability region and the yaw stability region are respectively constructed, and the accuracy verification and robustness analysis of the established stability region are conducted in the Matlab/Simulink environment. Finally, a model-free adaptive control method is employed to keep the vehicle state within the stability region, without tracking specific vehicle state objectives. The study in this paper can provide theoretical support for stability boundary determination, formulation of intervention timing for assisted driving stability control, and coordination control of vehicle stability and anti-rollover with local compatibility or even conflicts.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-31DOI: 10.1177/09544070241240001
Xuzhao Hou, Yue Ma, Changle Xiang
Advanced motion controllers have the potential to make automated or remotely operated vehicles less dependent on human operation. Among the different control strategies, model predictive control (MPC) has proven to have good performance in constrained systems. In this study, a combination of disturbance observer and robust model predictive control is proposed as a dynamics controller for tracked vehicles. Two different robust MPC approaches, nominal robust MPC and Tube-MPC, are compared. The latter has the potential to achieve offline computation based only on pre-planned reference states, which makes it possible to achieve real-time control with small sampling intervals. The effect of the reduced sampling interval on the state tracking accuracy is also investigated. The simulation results indicate that the nominal robust MPC has a significant advantage over the Tube-MPC when the control constraints become active and with the same sampling interval. Two model predictive controllers are evaluated on an electric tracked mobile robot. Compared to the nominal robust MPC with a sampling interval of 0.1 s, the Tube-MPC with a sampling interval of 0.03 s reduces vehicle velocity and yaw rate tracking errors by 3.8% and 9.6%, respectively.
{"title":"Robust model predictive dynamics control for electric tracked vehicle combined with disturbance observer","authors":"Xuzhao Hou, Yue Ma, Changle Xiang","doi":"10.1177/09544070241240001","DOIUrl":"https://doi.org/10.1177/09544070241240001","url":null,"abstract":"Advanced motion controllers have the potential to make automated or remotely operated vehicles less dependent on human operation. Among the different control strategies, model predictive control (MPC) has proven to have good performance in constrained systems. In this study, a combination of disturbance observer and robust model predictive control is proposed as a dynamics controller for tracked vehicles. Two different robust MPC approaches, nominal robust MPC and Tube-MPC, are compared. The latter has the potential to achieve offline computation based only on pre-planned reference states, which makes it possible to achieve real-time control with small sampling intervals. The effect of the reduced sampling interval on the state tracking accuracy is also investigated. The simulation results indicate that the nominal robust MPC has a significant advantage over the Tube-MPC when the control constraints become active and with the same sampling interval. Two model predictive controllers are evaluated on an electric tracked mobile robot. Compared to the nominal robust MPC with a sampling interval of 0.1 s, the Tube-MPC with a sampling interval of 0.03 s reduces vehicle velocity and yaw rate tracking errors by 3.8% and 9.6%, respectively.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-31DOI: 10.1177/09544070241240010
Yuecheng Ma, Ming Yue, Chen Xu, Ludian Pang, Jinyong Shangguan
This paper proposes a preview-based active suspension control method for heavy-duty trucks, where the vehicle front wheel terrain preview information is employed to improve the performance of the rear, focusing on enhancing the handling stability and vehicle smoothness. To begin with, the vehicle front wheel preview information is introduced to the half-vehicle model, and a state quantity is employed to calculate the time lag between front and rear for predicting the control input of the rear wheel. Secondly, based on the developed model, an [Formula: see text] controller is designed with the half-vehicle model based on the current stochastic linear optimal control combined with the preview controller, which provides more stable effect than the prior controllers by merging perceived ground information. Furthermore, an established seven-degree-of-freedom vehicle suspension model is utilized for the preview-based controller to govern the kinetic behavior of the heavy-duty truck, allowing for a more thorough analysis of operation smoothness and vehicle stability. At last, the vehicle comparison simulations are carried out, which indicates that the preview-based [Formula: see text] controller designed by inspecting the terrain preview information can straighten out the smoothness and safety of the heavy-duty truck more effectively in contrast with the LQG controller.
{"title":"Preview-based terrain adaptive active suspension control strategy for heavy-duty trucks","authors":"Yuecheng Ma, Ming Yue, Chen Xu, Ludian Pang, Jinyong Shangguan","doi":"10.1177/09544070241240010","DOIUrl":"https://doi.org/10.1177/09544070241240010","url":null,"abstract":"This paper proposes a preview-based active suspension control method for heavy-duty trucks, where the vehicle front wheel terrain preview information is employed to improve the performance of the rear, focusing on enhancing the handling stability and vehicle smoothness. To begin with, the vehicle front wheel preview information is introduced to the half-vehicle model, and a state quantity is employed to calculate the time lag between front and rear for predicting the control input of the rear wheel. Secondly, based on the developed model, an [Formula: see text] controller is designed with the half-vehicle model based on the current stochastic linear optimal control combined with the preview controller, which provides more stable effect than the prior controllers by merging perceived ground information. Furthermore, an established seven-degree-of-freedom vehicle suspension model is utilized for the preview-based controller to govern the kinetic behavior of the heavy-duty truck, allowing for a more thorough analysis of operation smoothness and vehicle stability. At last, the vehicle comparison simulations are carried out, which indicates that the preview-based [Formula: see text] controller designed by inspecting the terrain preview information can straighten out the smoothness and safety of the heavy-duty truck more effectively in contrast with the LQG controller.","PeriodicalId":509770,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.1177/09544070241240168
Xuan Zhou, Hengliang Jiang, J. Long
A collaborative multi-objective optimization design is conducted for the rear seat of a passenger car. This study introduces a combined optimization strategy that integrates both the multi-objective optimization problem and multi-criteria decision-making approaches. Firstly, a finite element model of the rear seat luggage compartment crash is established, and its accuracy is validated. Secondly, the thickness and material type of the primary stress components of the backrest framework for the rear seat are considered as design variables. The safety test point displacement, material cost, and weight are defined as the optimization objectives, while regulatory standards are taken as constraints to construct a multi-objective optimization problem. Once more, the Pareto frontier solution sets are achieved by constructing the genetic aggregation response surface surrogate model combined with the non-dominated sorting genetic algorithm-III optimization algorithm through experimental design. Finally, the Pareto frontier solution sets are ranked to determine the best compromise solution using the multi-criteria decision-making method, which involves the optimal combination weight and the technique for order preference by similarity to an ideal solution based on the Kullback-Leibler distance. The safety performance, lightweight, and cost-effectiveness of the optimized rear car seat are improved. Specifically, the displacement of the headrest skeleton and backrest skeleton is reduced by 5.96% and 4.47% respectively, the material cost is decreased by 7.1%, and the weight is reduced by 5.54%.
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