Article
Engineering, Mechanical
Ruochen Wang, Wei Liu, Renkai Ding, Xiangpeng Meng, Zeyu Sun, Lin Yang, Dong Sun
Summary: This study proposes a switching control strategy for the semi-active suspension based on road profile estimation, which effectively improves vehicle dynamic performance under different road conditions. By studying the mapping relationship between driving conditions and performance requirements and designing a switching controller with multiple sub-controllers, the study demonstrates the effectiveness of the road estimation results and the superiority of the switching control strategy.
VEHICLE SYSTEM DYNAMICS
(2022)
Article
Chemistry, Multidisciplinary
Hong Jiang, Chengchong Wang, Zhongxing Li, Chenlai Liu
Summary: A semiactive suspension control method is proposed for hub-motor electric vehicles, with a hybrid model predictive controller (HMPC) designed to improve vehicle dynamic performance. A Kalman filter is used to provide state variables for the controller, and simulation results show that the proposed control algorithm can significantly improve ride comfort, reduce motor vibration, and enhance handling stability.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Analytical
Jianze Liu, Jiang Liu, Yang Li, Guangzheng Wang, Fazhan Yang
Summary: In this paper, the least squares method is used to determine the vertical height of the road space domain. A control strategy for multiple mode switching under different road surfaces and speeds is constructed using the active suspension control mode switching model based on the road estimation method. The particle swarm optimization algorithm (PSO) is used to optimize the weight coefficients of LQR control under different modes, and the dynamic performance of vehicle driving is comprehensively analyzed.
Article
Automation & Control Systems
Khalid El Majdoub, Fouad Giri, Fatima-Zahra Chaoui
Summary: This paper investigates the control problem of a half-vehicle semi-active suspension system involving a magnetorheological damper, using observers and an adaptive controller to estimate the internal hysteresis states online and achieve the desired control objectives. Several simulations confirm the superiority of the proposed controller compared to skyhook control and passive suspension.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Information Systems
Haolin Yang, Bo-Gyu Kim, Jong-Seok Oh, Gi-Woo Kim
Summary: This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input, which simultaneously estimates the time-varying parameter of vehicle suspension systems and road roughness. An adaptive forgetting factor technique is employed to track time-varying parameters and unknown inputs.
Article
Acoustics
Hoa Thi Truong, Xuan Bao Nguyen, Cuong Mai Bui
Summary: The magnetorheological elastomer (MRE) is a smart material widely used in recent vibration systems. A system using these materials often faces difficulties designing the controller such as unknown parameters, hysteresis state, and input constraints. A singularity-free adaptive controller is proposed to eliminate the control singularities caused by parametric uncertainty. The controller consists of four components and is designed to obtain stabilization of hysteretic state estimation. Adaptive algorithms are proposed to update the unknown system parameters and observe the unmeasurable hysteretic state. The proposed controller's efficiency is demonstrated through simulation on a quarter-car suspension with an MRE-based absorber.
SHOCK AND VIBRATION
(2022)
Article
Computer Science, Information Systems
Gyuwon Kim, Soo Young Lee, Jong-Seok Oh, Seungchul Lee
Summary: The vehicle suspension control unit is crucial for steering stability and ride quality, requiring prior knowledge of road profile and internal state variables. A data-driven deep learning method is proposed as an alternative to conventional model-based approaches, showing superior estimation accuracy and computation efficiency. Additionally, the model's autoregressive capability allows for estimating future values, surpassing existing baseline methods.
Article
Computer Science, Information Systems
K. B. Devika, G. Rohith, Shankar C. Subramanian
Summary: This paper investigates the impact of brake fade phenomenon on the string stability of heavy commercial road vehicle platoons. A sliding mode control based string stable controller is designed to compensate for brake fade, along with an algorithm that adaptively estimates mass and gradient values. Experimental results show that the proposed approach ensures string stability for various road conditions and platoon operations.
Article
Chemistry, Multidisciplinary
Fei-Xue Wang, Qian Peng, Xin-Liang Zang, Qi-Fan Xue
Summary: The paper proposes a novel ACC control strategy for intelligent pure electric city buses, addressing issues of tracking capability, driving safety, energy saving, and driving comfort through a hierarchical control architecture and Model Predictive Control theory. Simulation and Hardware-in-the-Loop test results demonstrate that the strategy ensures driving safety, tracking ability, and significantly improves vehicle comfort and energy economy.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Mechanical
Mingde Gong, Xin Yan
Summary: The study introduces a new adaptive robust control strategy for heavy rescue vehicles based on road level estimation. It includes a new road estimation method, a T-S fuzzy controller for road level estimation, and an adaptive optimal H(infinity) controller for adaptive control of an active suspension on different road levels. Experiment results show improved ride comfort and handling stability.
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY
(2021)
Article
Engineering, Mechanical
Zijun Liu, Shuo Cheng, Jinzhao Liu, Qiong Wu, Liang Li, Huawei Liang
Summary: This paper proposes a braking control strategy for hybrid electric buses based on vehicle mass and road slope estimation, aiming to improve braking performance and increase energy recovery through precise control of the braking torque.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Engineering, Mechanical
Jia Ye, Zhifei Zhang, Jie Jin, Ruiqi Su, Bo Huang
Summary: The tire-road friction coefficient is crucial for vehicle safety systems. Existing methods have limited accuracy, while the proposed estimation method improves accuracy by adaptively adjusting tire stiffness and accurately identifies tire damage.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Renkai Ding, Ruocheng Wang, Xiangpeng Meng, Wei Liu, Long Chen
Summary: This study investigates the intelligent switching control system for hybrid electromagnetic active suspension, which effectively reduces energy consumption while maintaining dynamic performance through fast-slow switching method across all road conditions. The research includes the design of system architecture, optimization of control parameters, simultaneous estimation of road profile and system variables, identification of road level, and design of a road adaptive intelligent switching control system. Hardware-in-the-loop bench tests verify the effectiveness of the system in improving comprehensive performances of the active suspension system.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Linhui Zhao, Pengliang Qin, Shijin Gao, Siyuan Zhang, Vladimir V. Vantsevich, Zhiyuan Liu
Summary: The accuracy of road profile height estimation is crucial for suspension control quality and has a direct impact on vehicle handling, stability, and occupants' comfort. This article proposes a novel method for road profile estimation based on suspension nonlinear dynamics. The method considers the nonlinear characteristics of the suspension components and establishes a model that requires only two easily measurable parameters. The proposed method combines a sliding mode observer and the framework of linear parameter-varying systems to estimate the road profile height in real-time.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Hakan Basargan, Andras Mihaly, Peter Gaspar, Olivier Sename
Summary: This paper proposes an integration method for an intelligent, road-adaptive, semi-active suspension control and cruise control system. The road-adaptive, semi-active suspension controller is designed through the linear parameter-varying (LPV) method, and road adaptation is performed with a road adaptivity algorithm that considers road irregularities and vehicle velocity. The designed controllers are integrated, and the operation of the integrated method is validated in a TruckSim simulation environment.
Article
Engineering, Electrical & Electronic
Lin Li, Serdar Coskun, Fengqi Zhang, Reza Langari, Junqiang Xi
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Review
Thermodynamics
Fengqi Zhang, Xiaosong Hu, Reza Langari, Dongpu Cao
PROGRESS IN ENERGY AND COMBUSTION SCIENCE
(2019)
Article
Engineering, Electrical & Electronic
Gang Chen, Shoubao Chen, Reza Lang, Xu Li, Weigong Zhang
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Engineering, Civil
Jehong Yoo, Reza Langari
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Green & Sustainable Science & Technology
Feifei Jin, Lidan Pei, Huayou Chen, Reza Langari, Jinpei Liu
Article
Engineering, Biomedical
Md Ferdous Wahid, Reza Tafreshi, Reza Langari
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2020)
Article
Chemistry, Multidisciplinary
Jianfeng Chen, Congcong Guo, Shulin Hu, Jiantian Sun, Reza Langari, Chuanye Tang
APPLIED SCIENCES-BASEL
(2020)
Article
Engineering, Electrical & Electronic
Jinbao Yao, Jie Zhao, Yuanyuan Deng, Reza Langari
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Robotics
Amin Zeiaee, Rana Soltani Zarrin, Andrew Eib, Reza Langari, Reza Tafreshi
Summary: This letter presents the design and evaluation of a novel upper-limb exoskeleton, Cleverarm, which focuses on portability, design robustness, and ergonomic performance. The performance of Cleverarm is validated through experiments involving healthy subjects.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Jianfeng Chen, Yicai Ye, Qiang Wu, Reza Langari, Chuanye Tang
Summary: This paper proposes a high-performance adaptive cruise control system based on an inertial-triggered mechanism and multi-objective optimization. A simple method is adopted to predict the preceding vehicle's acceleration, and the inertial-triggered mechanism is established based on the zero-crossing points extracted from the predicted acceleration. Multiple objectives are optimized within the framework of a model predictive control algorithm by properly releasing the kinetic energy stored in vehicle inertia. Verification results show significant improvements in fuel economy and braking time, leading to enhanced vehicle safety.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Mojgan Fayyazi, Monireh Abdoos, Duong Phan, Mohsen Golafrouz, Mahdi Jalili, Reza N. Jazar, Reza Langari, Hamid Khayyam
Summary: This paper presents an intelligent energy management system based on reinforcement learning for conventional autonomous vehicles, aiming to reduce emissions and energy consumption. A new exploration strategy is proposed to replace the traditional epsilon-greedy strategy in the Q-learning algorithm. The Q-learning and SAQ-learning controllers are shown to generate the desired engine torque and control the air/fuel ratio efficiently in real-time, improving operational time compared to standard Q-learning.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Civil
Jinghui Peng, Fengqi Zhang, Serdar Coskun, Xiaosong Hu, Yalian Yang, Reza Langari, Jinsong He
Summary: Connected and automated vehicle technology can improve energy efficiency for hybrid electric vehicles through vehicle-to-everything communication. This paper proposes a multi-lane hierarchical optimization algorithm based on a predictive control framework, which significantly improves the fuel economy of HEVs by exchanging information between traffic vehicles and using traffic light timing and vehicle state information. Simulation results show that the fuel economy of the proposed algorithm is improved by 32% on average compared to human-driven speed profiles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Zhuo Wang, Wei Wei, Reza Langari, Qingyu Zhang, Qingdong Yan
Summary: Excessively high brake temperatures of hydrodynamic retarders can result in brake fading and failure, reducing brake effectiveness. Constructing temperature models for hydrodynamic retarders is challenging due to the system's non-linear characteristics. A model based on an artificial neural network has been shown to accurately predict temperature performance in constant-torque braking processes.
Article
Computer Science, Information Systems
Feifei Jin, Jinpei Liu, Huayou Chen, Reza Langari
Article
Computer Science, Artificial Intelligence
Qingyu Zhang, Reza Langari, H. Eric Tseng, Dimitar Filev, Steven Szwabowski, Serdar Coskun
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2020)
