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Machines
Special Issues
Adaptive and Optimal Control of Vehicles
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Adaptive and Optimal Control of Vehicles

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 5065

Special Issue Editor

Dr. Arash Farnam

E-Mail Website
Guest Editor
Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Ghent University, Ghent, Belgium
Interests: systems and control; distributed control and estimation; multi-agents; intelligent transportation systems

Special Issue Information

Dear Colleagues,

Optimal and adaptive control systems have become important tools in control systems theory and its applications. Furthermore, research on vehicle modeling and control has long been a crucial issue in vehicle system dynamics. In the topic of vehicle system dynamics, vehicle control applications in general and automobiles have brought opportunities to improve the quality of transportation systems as well as create safety and more comfort for new-generation vehicles. Recently, several control techniques and algorithms have been developed to improve the performance of vehicles. In this Special Issue, we will focus on developing optimal and adaptive control systems for vehicles. This Special Issue will bring together original research articles at a high-quality level through an international standard peer-review process with the main subjects (not an exclusive subjects list) described as follows:  

  • Optimal and adaptive linear/nonlinear control for vehicle system dynamics and vehicle system components, including suspension, steering, braking, chassis systems, noise-vibration-harshness, and power train.
  • Control of motion and external forces affecting vehicle performance.
  • Optimal and adaptive computer-aided modeling and simulation, validation, parameter identification and testing, and driver modeling.
  • Vehicle interactions with the environment, including wheel–rail and tire–ground behaviors.
  • Safety systems, including collision avoidance and derailment warning.
  • Intelligent transportation systems with interconnected vehicles.
  • Guided vehicles, automated traffic systems related to vehicle dynamics and unconventional vehicles.

Dr. Arash Farnam
Guest Editor

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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 6121 KiB  
Article
Design and Verification of Offline Robust Model Predictive Controller for Wheel Slip Control in ABS Brakes
by Jaffar Seyyed Esmaeili, Abdullah Başçi and Arash Farnam
Machines 2023, 11(8), 803; https://doi.org/10.3390/machines11080803 - 4 Aug 2023
Cited by 4 | Viewed by 2071
Abstract
Wheel slip control is a critical aspect of vehicle safety systems, notably the antilock braking system (ABS). Designing a robust controller for the ABS faces the challenge of accommodating its strong nonlinear behavior across varying road conditions and parameters. To ensure optimal performance [...] Read more.
Wheel slip control is a critical aspect of vehicle safety systems, notably the antilock braking system (ABS). Designing a robust controller for the ABS faces the challenge of accommodating its strong nonlinear behavior across varying road conditions and parameters. To ensure optimal performance during braking and prevent skidding or lock-up, the ideal wheel slip value can be determined from the peak of the tire–road friction curve and maintained throughout the braking process. Among various control approaches, model predictive control (MPC) demonstrates superior performance and robustness. However, online MPC implementation encounters significant computational burdens and real-time limitations, particularly when dealing with larger problem sizes. To address these issues, this study introduces an offline robust model predictive control (RMPC) methodology. The proposed approach is based on the robust asymptotically stable invariant ellipsoid methodology, which employs linear matrix inequalities (LMIs) to calculate a collection of invariant state feedback laws associated with a sequence of nested invariant stable ellipsoids. Simulation results indicate a significant reduction in computational burden with the offline RMPC approach compared to online implementation, while effectively tracking the desired wheel slip reference values and system constraints. Moreover, the offline RMPC design aligns well with the online MPC design and verifies its effectiveness in practice. Full article
(This article belongs to the Special Issue Adaptive and Optimal Control of Vehicles)
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18 pages, 9443 KiB  
Article
Yaw Rate Prediction and Tilting Feedforward Synchronous Control of Narrow Tilting Vehicle Based on RNN
by Ruolin Gao, Haitao Li, Ya Wang, Shaobing Xu, Wenjun Wei, Xiao Zhang and Na Li
Machines 2023, 11(3), 370; https://doi.org/10.3390/machines11030370 - 9 Mar 2023
Cited by 1 | Viewed by 2146
Abstract
The synchronous control of yaw motion and tilting motion is an important problem related to the lateral stability and energy consumption of narrow tilting vehicles. This paper proposes a method for the tilting control of narrow tilting vehicles: tilting feedforward synchronous control. This [...] Read more.
The synchronous control of yaw motion and tilting motion is an important problem related to the lateral stability and energy consumption of narrow tilting vehicles. This paper proposes a method for the tilting control of narrow tilting vehicles: tilting feedforward synchronous control. This method utilizes a proposed novel prediction method for yaw rate based on a recurrent neural network. Meanwhile, considering that classical recurrent neural networks can only predict yaw rate at a given time, and that yaw rate prediction generally needs to analyze a large amount of computer vision data, in this paper, the yaw rate is represented by a polynomial operation to predict the continuous yaw rate in the time domain; this prediction is realized using only the driving data of the vehicle itself and does not include the data generated by computer vision. A prototype experiment is provided in this work to prove the advantages and feasibility of the proposed tilting feedforward synchronous control method for narrow tilting vehicles. The proposed tilting feedforward synchronous control method can ensure the synchronous response of the yaw motion and the tilting motion of narrow tilting vehicles. Full article
(This article belongs to the Special Issue Adaptive and Optimal Control of Vehicles)
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