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Actuators
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Model Predictive Control in Mechatronic, Robotic, and Networked Systems
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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: closed (30 April 2022) | Viewed by 12396

Special Issue Editors

Prof. Dr. Constantin Caruntu

E-Mail Website
Guest Editor
Department of Automatic Control and Applied Informatics, Gheorghe Asachi Technical University of Iasi, Str. Prof. D. Mangeron, No. 26, 700050 Iasi, Romania
Interests: model predictive control; networked/distributed control systems; automotive control systems; vehicle dynamics and control; cooperative systems; connected and automated mobility; vehicle connectivity; 5G applications
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals

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 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

  • 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 (5 papers)

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23 pages, 775 KiB  
Article
Distributed Model Predictive Control and Coalitional Control Strategies—Comparative Performance Analysis Using an Eight-Tank Process Case Study
by Anca Maxim, Ovidiu Pauca and Constantin-Florin Caruntu
Actuators 2023, 12(7), 281; https://doi.org/10.3390/act12070281 - 10 Jul 2023
Cited by 1 | Viewed by 1176
Abstract
Complex systems composed of multiple interconnected sub-systems need to be controlled with specialized control algorithms. In this paper, two classes of control algorithms suitable for such processes are presented. Firstly, two distributed model predictive control (DMPC) strategies with different formulations are described. Afterward, [...] Read more.
Complex systems composed of multiple interconnected sub-systems need to be controlled with specialized control algorithms. In this paper, two classes of control algorithms suitable for such processes are presented. Firstly, two distributed model predictive control (DMPC) strategies with different formulations are described. Afterward, a coalitional control (CC) strategy is proposed, with two different communication topologies, i.e., a default decentralized topology and a distributed topology. All algorithms were tested on the same simulation setup consisting of eight water tanks. The simulation results show that the coalitional control methodology has a similar performance to the distributed algorithms. Moreover, due to its simplified formulation, the former can be easily tested on embedded systems with limited computation storage. Full article
(This article belongs to the Special Issue Model Predictive Control in Mechatronic, Robotic, and Networked Systems)
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14 pages, 926 KiB  
Article
Energy-Aware Model Predictive Control of Assembly Lines
by Francesco Liberati, Chiara Maria Francesca Cirino and Andrea Tortorelli
Actuators 2022, 11(6), 172; https://doi.org/10.3390/act11060172 - 20 Jun 2022
Cited by 1 | Viewed by 2055
Abstract
This paper presents a model predictive approach to the energy-aware control of tasks’ execution in an assembly line. The proposed algorithm takes into account both the need for optimizing the assembly line operations (in terms of the minimization of the total cycle time) [...] Read more.
This paper presents a model predictive approach to the energy-aware control of tasks’ execution in an assembly line. The proposed algorithm takes into account both the need for optimizing the assembly line operations (in terms of the minimization of the total cycle time) and that of optimizing the energy consumption deriving from the operations, by exploiting the flexibility added by the presence of a local source of renewable energy (a common scenario of industries that are often equipped, e.g., with photovoltaic plants) and, possibly, also exploiting an energy storage plant. The energy-related objectives we take into account refer to the minimization of the energy bill and the minimization of the peaks in the power injected and absorbed from the grid (which is desirable also from the perspective of the network operator). We propose a mixed-integer linear formulation of the optimization problem, through the use of H-infinite norms, instead of the quadratic ones. Simulation results show the effectiveness of the proposed algorithm in finding a trade-off that allows keeping at a minimum the cycle time, while saving on the energy bill and reducing peak powers. Full article
(This article belongs to the Special Issue Model Predictive Control in Mechatronic, Robotic, and Networked Systems)
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12 pages, 585 KiB  
Article
Data-Driven Predictive Control of Interconnected Systems Using the Koopman Operator
by Duvan Tellez-Castro, Camilo Garcia-Tenorio, Eduardo Mojica-Nava, Jorge Sofrony and Alain Vande Wouwer
Actuators 2022, 11(6), 151; https://doi.org/10.3390/act11060151 - 6 Jun 2022
Cited by 5 | Viewed by 2057
Abstract
Interconnected systems are widespread in modern technological systems. Designing a reliable control strategy requires modeling and analysis of the system, which can be a complicated, or even impossible, task in some cases. However, current technological developments in data sensing, processing, and storage make [...] Read more.
Interconnected systems are widespread in modern technological systems. Designing a reliable control strategy requires modeling and analysis of the system, which can be a complicated, or even impossible, task in some cases. However, current technological developments in data sensing, processing, and storage make data-driven control techniques an appealing alternative solution. In this work, a design methodology of a decentralized control strategy is developed for interconnected systems based only on local and interconnection time series. Then, the optimization problem associated with the predictive control design is defined. Finally, an extension to interconnected systems coupled through their input signals is discussed. Simulations of two coupled Duffing oscillators, a bipedal locomotion model, and a four water tank system show the effectiveness of the approach. Full article
(This article belongs to the Special Issue Model Predictive Control in Mechatronic, Robotic, and Networked Systems)
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19 pages, 450 KiB  
Article
Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model
by Alejandro Piñón, Antonio Favela-Contreras, Francisco Beltran-Carbajal, Camilo Lozoya and Graciano Dieck-Assad
Actuators 2022, 11(1), 21; https://doi.org/10.3390/act11010021 - 10 Jan 2022
Cited by 6 | Viewed by 2306
Abstract
Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This [...] Read more.
Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies. Full article
(This article belongs to the Special Issue Model Predictive Control in Mechatronic, Robotic, and Networked Systems)
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25 pages, 5065 KiB  
Article
Path Tracking Control of Autonomous Vehicle Based on Nonlinear Tire Model
by Fen Lin, Minghong Sun, Jian Wu and Chengliang Qian
Actuators 2021, 10(9), 242; https://doi.org/10.3390/act10090242 - 21 Sep 2021
Cited by 2 | Viewed by 2357
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
(This article belongs to the Special Issue Model Predictive Control in Mechatronic, Robotic, and Networked Systems)
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