Model Predictive Control: Advances in Sensor Technologies and Applications

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1637

Special Issue Editor


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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
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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

Published Papers (1 paper)

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Research

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
Viewed by 1102
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
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