Article
Engineering, Mechanical
Zhida Zhang, Ling Zheng, Hang Wu, Ziwei Zhang, Yinong Li, Yixiao Liang
Summary: A novel estimation framework based on a new tire model and ISCKF algorithm is proposed in this paper to more accurately determine the road friction coefficient of the vehicle's left and right sides. The presented method demonstrates excellent robustness in the presence of abnormal measurement noise interference and good adaptability to the uncertainty of road friction coefficients distribution.
VEHICLE SYSTEM DYNAMICS
(2022)
Article
Engineering, Multidisciplinary
Zhida Zhang, Ling Zheng, Yinong Li, Hang Wu, Yixiao Liang, Xuqiang Qiao
Summary: In this paper, a weighted adaptive SCKF (WASCKF) algorithm and a correction adaptive SCKF (CASCKF) algorithm are proposed to improve the accuracy and robustness of SCKF under irregular noise. WASCKF enhances accuracy by adaptively adjusting noise covariances, while CASCKF improves robustness by using fault detection mechanism and isolate rule. The numerical experiments on autonomous vehicle target tracking problem demonstrate that CASCKF algorithm has good accuracy and robustness against sudden abnormal noise interference.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
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
Rongyun Zhang, Bin Zhang, Peicheng Shi, Linfeng Zhao, Yongle Feng, Yaming Liu
Summary: In this paper, a permanent magnet synchronous motor (PMSM) was used as the in-wheel motor for a distributed drive electric vehicle (DDEV) simulation model. The PMSM speed estimation by Adaptive Sliding Mode Observer (ASMO) was used to estimate state parameters and road adhesion coefficient. A nonlinear three-degrees-of-freedom whole-vehicle dynamics reference model was established based on the Dugoff tire model, and a strong tracking square-root cubature Kalman filter (ST-SCKF) observer was constructed to improve estimation accuracy for the DDEV driving state parameters and road adhesion coefficient. CarSim and MATLAB/Simulink simulations and real vehicle experiments were conducted, showing that the ST-SCKF algorithm can estimate vehicle state parameters and road adhesion coefficients more accurately.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Wan Wenkang, Feng Jingan, Song Bao, Li Xinxin
Summary: The study focuses on improving vehicle dynamics control using the IMM-SCKF algorithm and shows that the fusion output results of IMM-SCKF algorithm outperform those of the traditional SCKF algorithm through simulation and validation.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics, Applied
Maria Kulikova, Gennady Yu Kulikov
Summary: This paper explores the design of accurate nonlinear Bayesian filters and proposes a numerical method for solving mean and error covariance of dynamic state. The key idea is to use methods with discretization error control to ensure accurate implementation of the filters' moment differential equations. The new method, tested on various application examples including stiff systems, utilizes a numerically stable square-root approach within singular value decomposition for a hybrid filter.
APPLIED NUMERICAL MATHEMATICS
(2022)
Article
Biochemistry & Molecular Biology
Chih-Hsu Huang, Peng-Hsiang Wang, Ming-Shaung Ju, Chou-Ching K. Lin
Summary: In this study, a parameter ratio derived from CSCKF was formulated to quantify the excitability of the neural network and compared with three commonly used indicators. The results showed that CSCKF had the potential to accurately reflect the neural network excitability and quantify the severity of epileptiform discharges in EEG with a preserved DC component.
Article
Engineering, Electrical & Electronic
Junwei Wang, Zhen Ma, Xiyuan Chen
Summary: By introducing a new neuron growth-attenuation mechanism based on the fuzzy neural network model and incorporating the theory of strong tracking filter, a generalized dynamic fuzzy neural network model based on MSCKF (MSCKF-GDFNN) is proposed, which demonstrates improved generalization ability and prediction accuracy during GNSS signal loss.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Pingshu Ge, Ce Zhang, Tao Zhang, Lie Guo, Qingyang Xiang
Summary: This paper proposes a square-root cubature Kalman filter with the maximum correlation entropy criterion (MCSRCKF) to accurately estimate vehicle states under non-Gaussian noise environments. Experimental results demonstrate that MCSRCKF achieves high accuracy and enhanced robustness in real-world scenarios.
APPLIED SCIENCES-BASEL
(2023)
Article
Physics, Multidisciplinary
Dengliang Qi, Jingan Feng, Yongbin Li, Lei Wang, Bao Song
Summary: This paper proposes a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. The results prove that the proposed estimation scheme can accurately estimate the vehicle's driving state compared to other common methods, and the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations.
Article
Thermodynamics
Lin Chen, Wentao Yu, Guoyang Cheng, Jierui Wang
Summary: This paper focuses on the SOC estimation of lithium batteries using a fractional-order adaptive square-root cubature Kalman filter (FO-ASRCKF). A fractional-order model (FOM) of the battery is established using fractional-order derivative theory. An improved adaptive genetic algorithm is applied for accurate identification of the multi-parameter model. The proposed FO-ASRCKF algorithm based on FOM and adaptive rules outperforms other filters in terms of SOC estimation accuracy and robustness.
Article
Engineering, Multidisciplinary
Yang Kang, Zizhen Qiu, Qiming Fan, Hao Zhang, Zhanqun Shi, Fengshou Gu
Summary: This paper proposes a novel estimation method for accurately estimating the time-varying dynamic coefficients (TVDCs) of journal bearings without the need for a tachometer. The method combines a phase-based approach to extract the instantaneous angular speed and an iteration strategy based on the square-root cubature Kalman filter (SRCKF) to estimate the TVDCs in the time domain. Simulation and experimental studies demonstrate the effectiveness of the proposed method in estimating the TVDCs of journal bearings under speed-variable conditions and different levels of measurement noise.
Article
Energy & Fuels
Vedik Basetti, Ashwani Kumar Chandel, Chandan Kumar Shiva
Summary: This paper investigates the problem of dynamic state estimation in power systems and proposes a new method based on square-root cubature Kalman filter to improve the performance of estimation. Through testing under various system operating conditions, the results demonstrate the versatility of the proposed method.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Energy & Fuels
Peng Guo, Wentao Ma, Dele Yi, Xinghua Liu, Xiaofei Wang, Lujuan Dang
Summary: This paper proposes a novel robust state estimation method that enhances the robustness of the square-root cubature Kalman filter by incorporating a mixture correntropy loss. The method overcomes the issue of non-Gaussian measurement noise interference and achieves high estimation accuracy in different cases.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Qiuyu Ding, Yujie Wang, Zonghai Chen
Summary: This study utilizes reduced-order electrochemical models for parameter identification and real-time state estimation in battery management systems, achieving state estimation through square-root cubature Kalman filter. The results demonstrate high accuracy and reduced computational burden of the reduced-order model.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Mechanical
Changle Xiang, Haonan Peng, Weida Wang, Liang Li, Quan An, Shuo Cheng
Summary: This paper proposes a hierarchical path tracking control strategy to enhance the lateral stability and safety performance of autonomous four in-wheel-motor independent-drive vehicles by coordinating direct yaw moment control and utilizing a vehicle dynamic model for multi-objective cooperative control.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2021)
Review
Engineering, Mechanical
Chao Huang, Liang Li, Xiangyu Wang
Summary: In an era of automated driving, the steer-by-wire system plays a crucial role in autonomous vehicles. Different types of control loops can affect the system's performance, with the type I control loop having a faster response speed and the type II control loop having a smoother response. Theoretical, simulation, and experimental results are provided to aid in understanding and application in both research and engineering practice.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Liang Li, Xianyao Ping, Jialei Shi, Xiangyu Wang, Xiuheng Wu
Summary: Regenerative braking system can recover energy in electric vehicles, but traditional optimization methods may not be sufficient to meet complex driving demands. Driverless vehicles can better optimize energy recovery, route tracking, and dynamics stability, thus improving efficiency.
IET INTELLIGENT TRANSPORT SYSTEMS
(2021)
Article
Engineering, Mechanical
Qiong Wu, Shuo Cheng, Liang Li, Fan Yang, Li Jun Meng, Zhi Xian Fan, Hua Wei Liang
Summary: This paper proposes a fuzzy-inference-based reinforcement learning approach for autonomous overtaking decision making in automated vehicles, considering various factors such as vehicle safety, driving comfort, and vehicle efficiency, and validating the effectiveness of the method on a simulation platform.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Lingtao Wei, Xiangyu Wang, Liang Li, Zhixian Fan, Ruzhen Dou, Jingui Lin
Summary: This study proposes a model predictive control (MPC) method based on the Takagi-Sugeno (T-S) fuzzy model to realize yaw stability control (YSC) in the nonlinear region. The results show that the proposed strategy has similar performance in the vehicle stable region with linear MPC, and it is able to suppress the instability of the vehicle in the nonlinear region, with an acceptable computation burden.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Baiming Chen, Mengdi Xu, Liang Li, Ding Zhao
Summary: Action delays can reduce the performance of reinforcement learning in real-world systems. This paper introduces a formal definition of delay-aware Markov Decision Process and presents a delay-aware model-based reinforcement learning framework. Experimental results demonstrate that the proposed algorithm is more efficient in training and transferable between systems with different durations of delay compared to state-of-the-art model-free reinforcement learning methods.
Article
Engineering, Multidisciplinary
Cheng Shuo, Zhang Yan, Yang YiYong, Fang ShengNan, Li Liang, Wang XiangYu
Summary: The study proposes a dynamic-programming-rule-based (DPRB) downshifting strategy for a specific hybrid electric bus (HEB) driving condition. By analyzing the braking characteristics of the HEB during the process of pulling in, a medium-time-distance (MTD) representing the dimension of time and space is proposed to define the boundary condition of the running bus. Look-up tables are established based on a dynamic programming algorithm offline using multiple sets of historical data, allowing real-time decision-making on whether to enter the optimal gear selection process based on driving data.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
Article
Automation & Control Systems
Lingtao Wei, Xiangyu Wang, Liang Li, Lu Yu, Zijun Liu
Summary: This study proposed a machine learning-based framework for monitoring the tire pressure of vehicles without the need for additional sensors. By extracting features, removing manufacturing errors, and analyzing signals, the framework can accurately judge the normal state and pressure loss of tires.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Civil
Shuo Cheng, Liang Li, Yong-Gang Liu, Wei-Bing Li, Hong-Qiang Guo
Summary: The paper introduces a lane-keeping integrated with collision avoidance control system based on a virtual fluid-flow model, combining lane-keeping and collision avoidance functions. Through co-simulations and real-bus tests, the effectiveness of the proposed control system has been verified.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Zijun Liu, Shuo Cheng, Xuewu Ji, Liang Li, Lingtao Wei
Summary: The paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire system to improve tracking accuracy and dynamic stability for autonomous vehicles. The architecture is robust against different types of disturbances in the path tracking process through hierarchical decoupling.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Liang Li, Yuewen Jiang, Biqing Huang
Summary: The study focuses on using a Transformer-based model to predict influenza outbreaks, showing superior performance in long-term forecasting compared to traditional AR and RNN models by using a source selection module based on curve similarity measurement to capture spatial dependency.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Engineering, Mechanical
Qiuyue Du, Chenxi Zhu, Quantong Li, Bin Tian, Liang Li
Summary: The article introduces a new four-wheel active steering control strategy, which utilizes the MPC algorithm for path tracking control, designs an estimator based on UKF theory and low-cost sensors, and combines it with the LQR optimal controller to achieve optimized control of front and rear steering. Simulation results demonstrate that this method performs well in lateral control stability and path tracking accuracy.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2022)
Article
Engineering, Civil
Cong-Zhi Liu, Liang Li, Xiang Chen, Jia-Wang Yong, Shuo Cheng, Hong-Lei Dong
Summary: In this study, a novel mixed H-2/H-infinity observer-based controller is proposed to address delayed measurements for real-time feedback control in advanced driver assistant system (ADAS). The controller enables object tracking and car-following, as well as attenuation of noises in the adaptive cruise control (ACC) system. The design criterion for the proposed controller is established based on linear matrix inequality (LMI) technique, demonstrating effectiveness through experiment scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Baiming Chen, Xiang Chen, Qiong Wu, Liang Li
Summary: This paper proposes an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning. By using ensemble models and nonparametric Bayesian methods to achieve diversity and cluster adversarial policies. Results show that the proposed method significantly degrades the performance of tested vehicles and can be used to infer weaknesses.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Quan An, Shuo Cheng, Chenfeng Li, Liang Li, Haonan Peng
Summary: The paper introduces a novel two-agent non-cooperative game framework to address the coordination of trajectory following control and lateral stability control for Autonomous Ground Vehicles. Control strategies based on game theory are proposed and tested through simulations and experiments. Additionally, a Linear Parameter-Varying method is used to facilitate the process of discretizing the state-space model.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)