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Actuators
Special Issues
Integrated Intelligent Vehicle Dynamics and Control
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Integrated Intelligent Vehicle Dynamics and Control

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Land Transport".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 5549

Special Issue Editors

Prof. Dr. Wuwei Chen

E-Mail Website
Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
Interests: vehicle system dynamics and control; vehicle vibration; system modeling and simulation; intelligent vehicles and assisted driving; robot control and application; intelligent transportation
Dr. Hongbo Wang

E-Mail Website
Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
Interests: vehicle system dynamics and control; vehicle vibration, vehicle system analysis and control; in-wheel motor drive vehicle control

Special Issue Information

Dear Colleagues,

The development of intelligent vehicle and connected autonous vehicle has made the traffic environment changed very much. In the recent condition, the automated vehicle and human-driving vehicle coexist on the road. These new change has brung new scientific and technological challenge to the academia and industry. Intelligent vehicle dynamics and control should face the new problems to enhance vehicle function design and performance. Integrated intelligent vehicle dynamics and control is important to make clear the vehicle dynamics behavior and provide the basis for the control strategy and algorithm design. The aim of the present Special Issue is to collect original papers concerned with integrated intelligent vehicle dynamics and control. Theoretical, numerical and experimental contributions for intelligent, electric, rail and special vehicles are welcome, including but not limited in dynamics analysis, fault diagnosis and control of X-by-wire chassis with suspension, steering and brake, powertrain and energy management.

Prof. Dr. Wuwei Chen
Dr. Hongbo Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent vehicle
  • electric vehicle
  • rail vehicle
  • special vehicle
  • vehicle dynamics modelling and control
  • integrated vehicle control
  • dynamic modelling and control of powertrain
  • chassis system dynamics and integrated control
  • X-by-wire chassis system dynamics and control
  • fault diagnosis and control of intelligent vehicle
  • vehicle stability analysis
  • energy management strategy of electric vehicle
  • connected autonomous vehicle control
  • vehicle dynamics control in mixed traffic environment

Published Papers (5 papers)

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Research

29 pages, 7167 KiB  
Article
A Tube-Based Model Predictive Control for Path Tracking of Autonomous Articulated Vehicle
by Taeyeon Lee and Yonghwan Jeong
Actuators 2024, 13(5), 164; https://doi.org/10.3390/act13050164 - 1 May 2024
Viewed by 350
Abstract
This paper presents tube-based Model Predictive Control (MPC) for the path and velocity tracking of an autonomous articulated vehicle. The target platform of this study is an autonomous articulated vehicle with a non-steerable axle. Consequently, the articulation angle and wheel torque input are [...] Read more.
This paper presents tube-based Model Predictive Control (MPC) for the path and velocity tracking of an autonomous articulated vehicle. The target platform of this study is an autonomous articulated vehicle with a non-steerable axle. Consequently, the articulation angle and wheel torque input are determined by the tube-based MPC. The proposed MPC aims to achieve two objectives: minimizing path tracking error and enhancing robustness to disturbances. Furthermore, the lateral stability of the autonomous articulated vehicle is considered to reflect its dynamic characteristics. The vehicle model for the MPC is formulated using local linearization to minimize modeling errors. The reference state is determined using a virtual controller based on the linear quadratic regulator to provide the optimal reference for the MPC solver. The proposed algorithm was evaluated through a simulation study with base algorithms under noise injection into the sensor signal. Simulation results demonstrate that the proposed algorithm achieved the smallest path tracking error, compared to the base algorithms. Additionally, the proposed algorithm demonstrated robustness to external noise for multiple signals. Full article
(This article belongs to the Special Issue Integrated Intelligent Vehicle Dynamics and Control)
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21 pages, 849 KiB  
Article
Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control
by Minan Tang, Yaqi Zhang, Wenjuan Wang, Bo An and Yaguang Yan
Actuators 2024, 13(3), 103; https://doi.org/10.3390/act13030103 - 6 Mar 2024
Viewed by 895
Abstract
The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller [...] Read more.
The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability. Full article
(This article belongs to the Special Issue Integrated Intelligent Vehicle Dynamics and Control)
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34 pages, 12225 KiB  
Article
Coordinated Control for the Trajectory Tracking of Four-Wheel Independent Drive–Four-Wheel Independent Steering Electric Vehicles Based on the Extension Dynamic Stability Domain
by Yiran Qiao, Xinbo Chen and Dongxiao Yin
Actuators 2024, 13(2), 77; https://doi.org/10.3390/act13020077 - 16 Feb 2024
Viewed by 1214
Abstract
In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. [...] Read more.
In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. Firstly, expert PID and model predictive control (MPC) are used to achieve longitudinal speed tracking and lateral path tracking, respectively. Then, a sliding mode controller is designed to calculate the expected yaw moment based on the desired vehicle states. The extension theory is applied to construct the extension dynamic stability domain, taking into account the linear response characteristics of the vehicle. Different coordinated allocation strategies are devised within various extension domains, providing control targets for direct yaw moment control (DYC) and active rear steering (ARS). Additionally, a compound torque distribution strategy is formulated to optimize driving efficiency and tire adhesion rate, considering the vehicle’s economy and stability requirements. The optimal wheel torque is calculated based on this strategy. Simulation tests using the CarSim/Simulink co-simulation platform are conducted under slalom test and double-lane change to validate the control strategy. The test results demonstrate that the proposed control strategy not only achieves good trajectory tracking performance but also enhances handling stability and economy during driving. Full article
(This article belongs to the Special Issue Integrated Intelligent Vehicle Dynamics and Control)
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22 pages, 8667 KiB  
Article
Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition
by Changyin Wei, Xiaodong Wang, Yunxing Chen, Huawei Wu and Yong Chen
Actuators 2023, 12(11), 410; https://doi.org/10.3390/act12110410 - 3 Nov 2023
Cited by 2 | Viewed by 1047
Abstract
The primary objective of an energy management strategy is to achieve optimal fuel economy through proper energy distribution. The adoption of a fuzzy energy management strategy is hindered due to different reasons, such as uncertainties surrounding its adaptability and sustainability compared to conventional [...] Read more.
The primary objective of an energy management strategy is to achieve optimal fuel economy through proper energy distribution. The adoption of a fuzzy energy management strategy is hindered due to different reasons, such as uncertainties surrounding its adaptability and sustainability compared to conventional energy control methods. To address this issue, a fuzzy energy management strategy based on long short-term memory neural network driving pattern recognition is proposed. The time-frequency characteristics of vehicle speed are obtained using the Hilbert–Huang transform method. The multi-dimensional features are composed of the time-frequency features of vehicle speed and the time-domain signals of the accelerator pedal and brake pedal. A novel driving pattern recognition approach is designed using a long short-term memory neural network. A dual-input and single-output fuzzy controller is proposed, which takes the required power of the vehicle and the state of charge of the battery as the input, and the comprehensive power of the range extender as the output. The parameters of the fuzzy controller are selected according to the category of driving pattern. The results show that the fuel consumption of the method proposed in this paper is 5.8% lower than that of the traditional fuzzy strategy, and 4.2% lower than the fuzzy strategy of the two-dimensional feature recognition model. In general, the proposed EMS can effectively improve the fuel consumption of extended-range electric vehicles. Full article
(This article belongs to the Special Issue Integrated Intelligent Vehicle Dynamics and Control)
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17 pages, 4101 KiB  
Article
Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm
by Chuanwei Zhang, Yansong Feng, Jianlong Wang, Peng Gao and Peilin Qin
Actuators 2023, 12(10), 371; https://doi.org/10.3390/act12100371 - 26 Sep 2023
Cited by 2 | Viewed by 1277
Abstract
Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the [...] Read more.
Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect. Full article
(This article belongs to the Special Issue Integrated Intelligent Vehicle Dynamics and Control)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A tube-based model predictive control for path tracking of autonomous articulated vehicle
Authors: Yonghwan Jeong
Affiliation: Seoul National University of Science & Technology
Abstract: The proposed paper aims to develop a path-following controller for articulated vehicles without steering wheels using tube MPC. This approach minimizes the effect of the model uncertainty to path tracking, thereby improving path tracking performance.

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