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Machines
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Advances in Autonomous Vehicles Dynamics and Control
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Advances in Autonomous Vehicles Dynamics and Control

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4462

Special Issue Editors

Dr. Zhongchao Liang

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: nonlinear and adaptive control for intelligent vehicles and mobile robots; distributed control for multi-agent system; unmanned and manned lunar exploration rover
Dr. Jing Zhao

E-Mail Website
Guest Editor
Department of Electromechanical Engineering, University of Macau, Macao, China
Interests: intelligent control; dynamics and control; mechanism and machine theory; autonoumous system; fault tolerant control; artificial intelligence with engineering applications; machine learning methods; signal processing; intelligent transportation; system modeling and identification
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongguo Li

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: distributed control; robotic path planning; multi-agent systems; distributed learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles present a promising solution to many of the challenges faced by the transportation industry, including reducing the number of accidents caused by human error, improving traffic flow, and increasing fuel efficiency. However, to fully realize the potential of this technology, significant technical challenges must be overcome. One of the key challenges is the development of robust and reliable control algorithms that can ensure the safe and smooth operation of autonomous vehicles in a wide range of driving scenarios, including complex urban and highway environments. Furthermore, the highly complex non-linear vehicle dynamics bring significant difficulties in the development of control systems.

This Special Issue seeks to bring together the latest research and developments in the field of autonomous vehicle dynamics and control. We invite submissions that address a broad range of topics related to this field, including advanced control system designs, dynamics modeling and simulation, and machine learning approaches. Specifically, topics of interest include, but are not limited to, the following:

  • Dynamics modeling and real-world implementation of autonomous vehicle control systems;
  • Energy-efficient control and optimization for autonomous vehicles;
  • Advanced control system designs for precise control and maneuvering of autonomous vehicles in complex driving scenarios;
  • Data-driven approaches to vehicle dynamics modeling and simulation;
  • Cooperative and coordinated control of multiple autonomous vehicles;
  • Applications of autonomous vehicles in transportation, warehouses, construction, manufacturing and space exploration, etc.

Dr. Zhongchao Liang
Dr. Jing Zhao
Dr. Zhongguo Li
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. Machines 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

  • autonomous vehicles
  • advanced non-linear control
  • dynamics modeling
  • machine learning

Published Papers (4 papers)

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Research

17 pages, 3515 KiB  
Article
Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions
by Chengyang Feng, Mingyu Shen, Zhongnan Wang, Hao Wu, Zenghui Liang and Zhongchao Liang
Machines 2024, 12(4), 237; https://doi.org/10.3390/machines12040237 - 05 Apr 2024
Viewed by 531
Abstract
In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system [...] Read more.
In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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21 pages, 10994 KiB  
Article
PID-Based Longitudinal Control of Platooning Trucks
by Aashish Shaju, Steve Southward and Mehdi Ahmadian
Machines 2023, 11(12), 1069; https://doi.org/10.3390/machines11121069 - 05 Dec 2023
Viewed by 1151
Abstract
This article focuses on the development and assessment of a PID-based computationally cost-efficient longitudinal control algorithm for platooning trucks. The study employs a linear controller with a nested architecture, wherein the inner loop regulates relative velocities while the outer loop governs inter-vehicle distances [...] Read more.
This article focuses on the development and assessment of a PID-based computationally cost-efficient longitudinal control algorithm for platooning trucks. The study employs a linear controller with a nested architecture, wherein the inner loop regulates relative velocities while the outer loop governs inter-vehicle distances within platoon vehicles. The design of the proposed PID controller entails a comprehensive focus on system identification, particularly emphasizing actuation dynamics. The simulation framework used in this study has been established through the integration of TruckSim® and Simulink®, resulting in a co-simulation environment. Simulink® serves as the platform for control action implementation, while TruckSim® simulates the vehicle’s dynamic behavior, thereby closely replicating real world conditions. The significant effort in fine-tuning the PID controller is described in detail, including the system identification of the linearized longitudinal dynamic model of the truck. The implementation is followed by an extensive series of simulation tests, systematically evaluating the controller’s performance, stability, and robustness. The results verify the effectiveness of the proposed controller in various leading truck operational scenarios. Furthermore, the controller’s robustness to large fluctuations in road grade and payload weight, which is commonly experienced in commercial vehicles, is evaluated. The simulation results indicate the controller’s ability to compensate for changes in both road grade and payload. Additionally, an initial assessment of the controller’s efficiency is conducted by comparing the commanded control efforts (total torque on wheels) along with the total fuel consumed. This initial analysis suggests that the controller exhibits minimal aggressive tendencies. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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30 pages, 7567 KiB  
Article
Research on Lane-Change Decision and Planning in Multilane Expressway Scenarios for Autonomous Vehicles
by Chuanyin Tang, Lv Pan, Jifeng Xia and Shi Fan
Machines 2023, 11(8), 820; https://doi.org/10.3390/machines11080820 - 10 Aug 2023
Cited by 2 | Viewed by 1045
Abstract
Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is [...] Read more.
Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is studied by the phase-plane method. Firstly, a state-machine-based driving logic is designed and a decision method is proposed to design the lane-change intention based on the surrounding traffic information and to consider the influence of the motion state of other vehicles in the adjacent lanes on the self-driving vehicle. In order to realize adaptive cruising under the full working conditions of the vehicle, a safety distance model is established for different driving speeds and switching strategies for fixed-speed cruising, following driving, and emergency braking are developed. Secondly, for the trajectory planning problem, a lane-change trajectory based on a quintuple polynomial optimization method is proposed. Then, the vehicle lateral stability boundary is investigated; the stability boundary and rollover boundary are incorporated into the designed path-tracking controller to improve the tracking accuracy while enhancing the rollover prevention capability. Finally, a simulation analysis is carried out through a joint simulation platform; the simulation results show that the proposed method can ensure the driving safety of autonomous vehicles in a multilane scenario. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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16 pages, 7454 KiB  
Article
Stability Analysis of a Vehicle–Cargo Securing System for Autonomous Trucks Based on 6-SPS-Type Parallel Mechanisms
by Guosheng Zhang, Tao Wang, Han Wang, Shilei Wu and Zhongxi Shao
Machines 2023, 11(7), 745; https://doi.org/10.3390/machines11070745 - 15 Jul 2023
Cited by 1 | Viewed by 993
Abstract
Stability prediction of the securing system for autonomous trucks is an important prerequisite for achieving safety monitoring of large cargo transportation and improving logistics efficiency. Considering the side slide risk of large cargo and the inability to predict stability using the existing under-constrained [...] Read more.
Stability prediction of the securing system for autonomous trucks is an important prerequisite for achieving safety monitoring of large cargo transportation and improving logistics efficiency. Considering the side slide risk of large cargo and the inability to predict stability using the existing under-constrained friction securing model, this paper proposes a new vehicle–cargo securing model based on the 6-SPS parallel mechanism. By establishing an analytical 3-DOF model, the dynamics performance of the vehicle–cargo system is analyzed based on the response solution under sinusoidal excitations. To verify the correctness of the analytical model, a multi-body dynamics model of the whole vehicle–cargo system based on the three-dimensional geometric model and the 6-SPS parallel mechanism is established for simulation in ADAMS. According to road class, pavement roughness is modeled by a white noise power spectrum method as the excitation in the simulation. The results show that the dynamics response of the analytical model accords well with that of the simulation model, with relative errors of 8.34% and 0.036% in amplitude and frequency, respectively. The proposed method can provide theoretical support for accurate stability prediction and for achieving safety monitoring of large cargo transportation for autonomous trucks. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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