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Automation
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Model Predictive Control: Advances in Sensor Technologies and Applications
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Model Predictive Control: Advances in Sensor Technologies and Applications

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4094

Special Issue Editor

Prof. Dr. Simon Tomažič

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: soft sensors; Raman spectroscopy; fuzzy model identification; machine learning with big data; predictive control of dynamic systems; sensor fusion; data mining; indoor positioning; autonomous mobile systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The synergy between model predictive control (MPC) and evolving sensor technologies represents a new era of intelligent control. This Special Issue, “Model Predictive Control: Advances in Sensor Technologies and Applications”, explores the multi-faceted relationship between these two fields.

The depth of sensor feedback loops, revealing the crucial role of sensors in MPC, highlights their integral function in feedback control. The fusion of different sensor data provides a broader perspective on MPC and enriches decision-making processes. In the era of data overload, techniques to control inconsistent or unreliable sensor data are becoming increasingly important in MPC. Moreover, the real-time applicability of MPC, when tested via the integration of wireless sensor networks, is both a challenge and a breakthrough. The introduction of soft sensors that can either complement or potentially replace traditional hardware is exciting. Finally, the transformative impact of self-calibrating sensors that redefine the adaptability of MPC is being explored.

This Special Issue aims to shed light on these intersections and foster a deeper understanding of this transformative technology. The authors' insights, research and innovations are invaluable to this discourse.

You may choose our Joint Special Issue in Sensors.

Yours sincerely,
Prof. Dr. Simon Tomažič
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. Automation is an international peer-reviewed open access quarterly 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 1000 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

  • model predictive control (MPC)
  • neural network control system
  • evolving control
  • nonlinear control
  • advanced process control
  • adaptive control
  • dynamic matrix control
  • intelligent soft sensor
  • fuzzy logic control
  • self-calibrating sensor

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Published Papers (4 papers)

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Research

19 pages, 895 KiB  
Article
Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation
by Alessandra Elisa Sindi Morando, Alessandro Bozzi, Simone Graffione, Roberto Sacile and Enrico Zero
Automation 2024, 5(3), 324-342; https://doi.org/10.3390/automation5030020 - 30 Jul 2024
Viewed by 377
Abstract
In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the [...] Read more.
In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader’s NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents’ state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions. Full article
(This article belongs to the Special Issue Model Predictive Control: Advances in Sensor Technologies and Applications)
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13 pages, 9197 KiB  
Article
Development of a Programmable System Used for the Preparation of a Mixture of Flammable/Explosive Gases
by Adrian Bogdan Simon Marinica, George Artur Gaman, Daniel Pupazan, Emilian Ghicioi, Florin Manea, Marius Cornel Suvar, Maria Prodan, Niculina Sonia Suvar, Gheorghe Daniel Florea and Robert Laszlo
Automation 2024, 5(3), 246-258; https://doi.org/10.3390/automation5030015 - 1 Jul 2024
Viewed by 511
Abstract
In the present paper, the use of programmable microprocessors to develop a computerized stand for the preparation of a mixture of flammable/toxic/explosive gases in order to obtain mixtures at concentrations in explosive range is presented. The operating principle of the stand is based [...] Read more.
In the present paper, the use of programmable microprocessors to develop a computerized stand for the preparation of a mixture of flammable/toxic/explosive gases in order to obtain mixtures at concentrations in explosive range is presented. The operating principle of the stand is based on the mixing of two volumetric flows, controlled with the help of microprocessors, where gases are stored and circulated at atmospheric pressure through cylindrical injectors, driven by stepper motors so that the gas circuit does not require valves. The exit of the stand is a homogenization chamber, with agitator and sensor to confirm the desired concentration of the mixture. This automated stand eliminates mechanical, electric or pneumatic valves from the gas circuits, avoiding elements with high mechanical resistance suitable for high pressures/depressions, removing the possibility of the return of the gas flow, without sensitivity to sudden pressures variations. Full article
(This article belongs to the Special Issue Model Predictive Control: Advances in Sensor Technologies and Applications)
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22 pages, 1580 KiB  
Article
A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems
by Gabryel M. Raposo de Alencar, Fernanda M. Lima Fernandes, Rafael Moura Duarte, Petrônio Ferreira de Melo, Altamar Alencar Cardoso, Heber Pimentel Gomes and Juan M. Mauricio Villanueva
Automation 2024, 5(2), 106-127; https://doi.org/10.3390/automation5020008 - 29 May 2024
Viewed by 635
Abstract
The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical [...] Read more.
The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical sensors in order to reduce costs and enhance efficiency. This study explores the implementation of an artificial neural network (ANN) based soft sensor model in a water supply system to predict flow rates within the system. The soft sensor is centered on a Long Short-Term Memory (LSTM) artificial neural network model using Monte Carlo dropout to reduce uncertainty and improve estimation performance. Based on the results of this work, it is concluded that the proposed soft sensor (with Monte Carlo dropout) can predict flow rates more precisely, contributing to the reduction in water losses, as well as cost savings. This approach offers a valuable solution for minimizing water losses and ensuring the efficient use of this vital resource. Regarding the use of soft sensors based on LSTM neural networks with a careful choice of Monte Carlo dropout parameters, when compared to the multilayer perceptron model, the LSTM model with Monte Carlo dropout showed better mean absolute error, root mean square error, and coefficient of determination: 0.2450, 0.3121, and 0.996437 versus 0.2556, 0.3522, and 0.9954. Furthermore, this choice of Monte Carlo dropout parameters allowed us to achieve an LSTM network model capable of reducing uncertainty to 1.8290, keeping the error metrics also at low levels. Full article
(This article belongs to the Special Issue Model Predictive Control: Advances in Sensor Technologies and Applications)
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18 pages, 4352 KiB  
Article
A Simplified One-Parallel-Element Automatic Impedance-Matching Network Applied to Electromagnetic Acoustic Transducers Driving
by João Pedro T. Andrade, Pedro Leon F. C. Bazan, Vivian S. Medeiros and Alan C. Kubrusly
Automation 2023, 4(4), 378-395; https://doi.org/10.3390/automation4040022 - 1 Dec 2023
Cited by 2 | Viewed by 1541
Abstract
Ultrasonic waves generated and received by electromagnetic acoustic transducers (EMATs) are advantageous in non-destructive testing, mainly due to the ability to operate without physical contact with the medium under test. Nevertheless, they present a main drawback of less efficiency, which leads to a [...] Read more.
Ultrasonic waves generated and received by electromagnetic acoustic transducers (EMATs) are advantageous in non-destructive testing, mainly due to the ability to operate without physical contact with the medium under test. Nevertheless, they present a main drawback of less efficiency, which leads to a lower signal-to-noise ratio. To overcome this, the L-network impedance-matching network is often used in order to ensure maximum power transfer to the EMAT from the excitation electronics. There is a wide range of factors that affect an EMAT’s impedance, apart from the transducer itself; namely, the properties of the specimen material, temperature, and frequency. Therefore, to ensure optimal power transfer, the matching network’s configuration needs to be fine-tuned often. Therefore, the automation of the laborious process of manually adjusting the network is of great benefit to the use of EMAT transducers. In this work, a simplified one-parallel-element automatic matching network is proposed and its theoretical optimal value is derived. Next, an automatic matching network was designed and fabricated. Experiments were performed with two different EMATs at several frequencies obtaining good agreement with theoretical predictions. The automatic system was able to determine the best configuration for the one-element matching network and provided up to 5.6 dB gain, similar to a standard manual solution and considerably faster. Full article
(This article belongs to the Special Issue Model Predictive Control: Advances in Sensor Technologies and Applications)
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