Special Issue "Machines Predictive Control"

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Dr. Imed Jlassi
E-Mail
Guest Editor
CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P - 6201-001 Covilhã, Portugal
Interests: digital control of power electronics converters, fault diagnosis, and fault-tolerant control of ac motor drives and wind turbine systems

Special Issue Information

Dear Colleagues,

In recent years, Model Predictive Control (MPC) has been a powerful advanced control technology in industrial machine drives, due to its superior control performance, excellent dynamic response and the ability to easily include multiple-objective control into the cost function. At each sampling time, MPC defines the control action by minimizing a cost function that describes the desired machine behavior. This cost function compares the predicted variables to be controlled with their references. The predicted variables are calculated from the machine model and duplicated according to the possible voltage vectors of the power converter.

In classical MPC, much attention has been paid to the control performance, through the development of several control techniques for the different power converters topologies and machines. However, there are still some issues that constitute an open topic for research. Despite the huge progress of MPC for electrical machines, the control stability and robustness under harsh operating conditions, as well as the formal way of selecting optimally the weighting factor in the cost function considering the multi-objective control, are topics of interest that require further investigation. Nowadays, with the increasing complexity of power converters and machines, the independence from the model and parameters that may change with the operating point and environment, as well as the reduction of the excessive computational burden due to the duplicated prediction have a significant impact on the machine performance and drive cost.

The aim of this Special Issue is to provide an opportunity for scientists, researchers, and practicing engineers to share and disseminate their latest discoveries and results in the aforementioned fields, indicating the future trends for machines predictive control.

Topics include, but are not limited to, the following research areas:

  • New MPC of electrical machines
  • Stability and robustness of MPC
  • Model-free predictive control approaches
  • MPC algorithms with reduced computational complexity
  • Implementation issues of MPC
  • Artificial intelligence and data-driven in predictive control

Prof. Dr. Antonio J. Marques Cardoso
Dr. Imed Jlassi
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. 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 1800 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.

Published Papers (3 papers)

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Research

Article
A Computationally Efficient Model Predictive Control of Six-Phase Induction Machines Based on Deadbeat Control
Machines 2021, 9(12), 306; https://doi.org/10.3390/machines9120306 - 23 Nov 2021
Viewed by 340
Abstract
Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In [...] Read more.
Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In that regard, this paper proposes a computationally efficient MPCC of an asymmetrical six-phase induction machine drive (ASIMD) that reduces the number of VVs used for prediction. By using the characteristics of the deadbeat control (DB), the proposed method obtains a reference voltage vector (RVV), where its position will serve as a reference and integrates the MPCC scheme. Only 4 out of 13 predictions are needed to determine the best VV, dramatically reducing the algorithm computation. Experimental results for a six-phase case study compare the standard MPCC with the suggested method, confirming that deadbeat model predictive current control (DB-MPCC) shows that the execution time can be shortened by 48.8% and successfully improve the motor performance and efficiency. Full article
(This article belongs to the Special Issue Machines Predictive Control)
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Article
Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications
Machines 2021, 9(10), 217; https://doi.org/10.3390/machines9100217 - 28 Sep 2021
Cited by 2 | Viewed by 661
Abstract
Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise [...] Read more.
Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user. Full article
(This article belongs to the Special Issue Machines Predictive Control)
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Article
Development and Implementation of an Anthropomorphic Underactuated Prosthesis with Adaptive Grip
Machines 2021, 9(10), 209; https://doi.org/10.3390/machines9100209 - 24 Sep 2021
Viewed by 478
Abstract
This paper describes the design of a prosthetic hand for wrist amputations. The mechanism considers the use of three actuators: one each for the movement of the little finger, annular finger, and middle finger. The second actuator controls the index finger, and the [...] Read more.
This paper describes the design of a prosthetic hand for wrist amputations. The mechanism considers the use of three actuators: one each for the movement of the little finger, annular finger, and middle finger. The second actuator controls the index finger, and the third controls the thumb. The prototype is considered relevant as it is able to move the distal phalanx in all fingers; the little, annular, and middle fingers are able to adapt to the shape of the object being gripped (adaptive grip). The sequence of movements achieved with the thumb emulate the opposition/reposition and flexion/extension movements, commanded by a single actuator. The proposed design was built by additive manufacturing and effortlessly achieves a large number of grips. Additionally, the prosthesis could perform specific movements, such as holding a needle, although this grip demands higher precision in the control of the fingers. Due to the manufacturing method, the prosthesis weighs only 200 g, increasing to 450 g when the actuators are included, therefore weighing less than an average adult’s hand. Full article
(This article belongs to the Special Issue Machines Predictive Control)
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