Special Issue "Model Predictive Control in Mechatronic, Robotic, and Networked Systems"

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

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Constantin Caruntu
E-Mail Website
Guest Editor
Department of Automatic Control and Applied Infromatics, Gheorghe Asachi Technical University of Iasi, Iasi, Romania
Interests: model predictive control; networked/distributed control systems; automotive control systems; vehicle connectivity
Dr. Cosmin Copot
E-Mail Website
Guest Editor
Department of Electromechanics, University of Antwerp, Antwerp, Belgium
Interests: robotics; mechatronics; visual serving systems; identification and control
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Special Issue Information

Dear Colleagues,

Model predictive control (MPC) is a control design methodology that appeared at the beginning of the 80s through which an open-loop finite-horizon optimization problem with embedded constraints is solved at each time-step based on the receding horizon principle. This principle involves the application of the first value from the computed control sequence, and, at the next time-step, the system state is measured/estimated and the optimal control sequence is re-computed.

MPC has received increasing interest among researchers and control practitioners in industries. The predictive control strategies were initially utilized for slow processes, e.g., oil refineries, petrochemicals, pulp and paper, primary metal industries, and gas plants. Starting with the evolution of hardware components and algorithms, the possibility to implement these types of control algorithms to fast processes with reduced sampling periods, appeared, e.g., mechatronics, automotive control, aero-spatial applications, autonomous robotics, power generation, and distribution.

Predictive control techniques have been introduced mainly in order to deal with plants that have complex dynamics (unstable inverse systems, time-varying delay, etc.) and plant model mismatch. They are of particular interest from the point of view of both broad applicability and implementation simplicity, being applied on a large scale in industry processes, with good performances and being robust at the same time.

Contributions from all fields related to Model Predictive Control in Mechatronic, Robotic, and Networked Systems are welcome to this Special Issue, including, particularly, the following:

  • Decentralized, hierarchical, and distributed MPC
  • Large-scale and cloud-based MPC
  • MPC for cyber-physical systems
  • Artificial intelligence in MPC
  • Real-time implementation of MPC
  • Applications of MPC in servo drives and electrical power drives
  • Applications of MPC in industrial and mobile robotics
  • Applications of MPC in industrial process control
  • Applications of MPC in automotive systems
  • Applications of MPC in networked and distributed systems

Prof. Dr. Constantin Caruntu
Dr. Cosmin Copot
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 papers will be 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 1600 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

  • decentralized, hierarchical, and distributed MPC
  • large-scale and cloud-based MPC
  • MPC for cyber-physical systems
  • artificial intelligence in MPC
  • real-time implementation of MPC
  • applications of MPC in servo drives and electrical power drives
  • applications of MPC in industrial and mobile robotics
  • applications of MPC in industrial process control
  • applications of MPC in automotive systems
  • applications of MPC in networked and distributed systems

Published Papers (1 paper)

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Research

Article
Path Tracking Control of Autonomous Vehicle Based on Nonlinear Tire Model
Actuators 2021, 10(9), 242; https://doi.org/10.3390/act10090242 - 21 Sep 2021
Viewed by 459
Abstract
The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear [...] Read more.
The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear composite functions of the magic formula instead of a simple linear relation model. Taylor expansion is used to linearize the controller, the first-order difference quotient method is used for discretization, and the partial derivative of the composite function is used for matrix transformation. Constant velocity and variable velocity conditions are selected to compare the designed controller with the conventional controller in Carsim/Simulink. The results show that when the tire forces fall in the nonlinear region, two controllers have good stability, and the tracking accuracy of the controller designed in this paper is slightly better. However, after the tire forces become nonlinear, the controller with linear tire force becomes worse, the tracking accuracy is far worse than the controller with the nonlinear tire model, and the vehicle stability is also degraded. In addition, an active steering test platform based on LabVIEW-RT is established, and hardware-in-the-loop tests are carried out. The effectiveness of the designed controller is verified. Full article
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