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Energies
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Optimal Control, Automation and Intelligent Energy Systems
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Optimal Control, Automation and Intelligent Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 4851

Special Issue Editor

Dr. Krzysztof Lalik

E-Mail Website
Guest Editor
Department of Process Automation, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology; al. Mickiewicza 30, 30-059 Krakow, Poland.
Interests: control systems; machine learning; predictive maintenance; artificial intelligence

Special Issue Information

Dear Colleagues,

The development of digitalization of processes and systems has been going on continuously for decades. New technologies such as artificial intelligence, cloud computing, and the Internet of Things, present in Industry 4.0 standards, have led to an unprecedented development of control and intelligent systems. Advancing automation of systems and processes is pushing the boundaries of what is possible in many technology areas through the use of artificial intelligence. Intelligence is also implemented within smart grid technology. Such an architecture simplifies the control of network operation, automates the grid, and provides the tools needed to manage energy demand and integrate distributed generation sources.

This Special Issue is devoted to intelligent systems that improve the control of nonlinear systems, including multidimensional systems. Topics covered in this Special Issue include intelligent sensing and actuation systems, intelligent system and process controllers, and energy distribution control systems. I would like to invite authors dealing with the subjects of this Issue to share the latest research, developments, and new trends they have observed.

Dr. Krzysztof Lalik
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

•    Intelligent Systems
•    Optimal control
•    IoT
•    Artificial Intelligence
•    Deep learning and Neural Computing
•    Smart grid
•    Energy sensing and measurement systems
•    Optimization algorithms
•    Control algorithms in renewable energy systems
•    Multi-agent Systems and Programming
•    Intelligent energy management systems
•    Smart appliance

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Published Papers (2 papers)

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Research

17 pages, 1269 KiB  
Article
Neural-Assisted Synthesis of a Linear Quadratic Controller for Applications in Active Suspension Systems of Wheeled Vehicles
by Mateusz Kozek, Adam Smoter and Krzysztof Lalik
Energies 2023, 16(4), 1677; https://doi.org/10.3390/en16041677 - 8 Feb 2023
Cited by 11 | Viewed by 1770
Abstract
This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such a target function that automatically leads to changes in the quality and resource matrix so that the target LQR regulator [...] Read more.
This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such a target function that automatically leads to changes in the quality and resource matrix so that the target LQR regulator achieves the desired performance. The solution’s stability and optimality are the target controller’s responsibility. However, the neural mechanism allows obtaining, without expert knowledge, the appropriate Q and R matrices, which will lead to such a gain matrix that will realise the control that will lead to the desired quality. The presented algorithm was tested for the derived quadrant model of the suspension system. Its application improved user comfort by 67% compared to the passive solution and 14% compared to non-optimised LQR. Full article
(This article belongs to the Special Issue Optimal Control, Automation and Intelligent Energy Systems)
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21 pages, 8443 KiB  
Article
An Experimental Study of Drift Caused by Partial Shading Using a Modified DC-(P&O) Technique for a Stand-Alone PV System
by Ashish Kumar Singhal, Narendra Singh Beniwal, Ruby Beniwal and Krzysztof Lalik
Energies 2022, 15(12), 4251; https://doi.org/10.3390/en15124251 - 9 Jun 2022
Cited by 11 | Viewed by 2622
Abstract
There is tremendous potential in solar energy to meet future electricity demands. Partial shading (PS) and drift are two major problems that must be addressed simultaneously to achieve the maximum power point (MPP) of a stand-alone PV system, which are discussed in this [...] Read more.
There is tremendous potential in solar energy to meet future electricity demands. Partial shading (PS) and drift are two major problems that must be addressed simultaneously to achieve the maximum power point (MPP) of a stand-alone PV system, which are discussed in this paper. Both of these factors contribute to the voltage drop due to heavy steady-state oscillation. The partial shading and drift problem are associated with severe rapid changes in the insolation. A modified drift-control perturbation and observation DC-(P&O) approach was investigated using a low-cost programmable hardware solution, i.e., the ARM Cortex M4 32-bit Microcontroller (MC) (STM32F407VGT6), with efficient embedded programming and Waijung block sets for real-time solutions. The experimental setup was accomplished on a 40-watt solar panel. It was found that the proposed method had a significant impact on drift control during abrupt changes in current and voltage caused by shading effects, with the controller conversion efficiency of 80.39% and 94.48% with percentage absolute errors of 7.3 and 7.2 for cases with and without PS and drift, respectively. Full article
(This article belongs to the Special Issue Optimal Control, Automation and Intelligent Energy Systems)
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