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Applied Sciences
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Technical Advances in Optimal Control and Controller Design
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Technical Advances in Optimal Control and Controller Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1860

Special Issue Editor

Dr. António José Ramos Silva

E-Mail Website
Guest Editor
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Porto, Portugal
Interests: control; optimal control; controller design

Special Issue Information

Dear Colleagues,

This Special Issue focuses on optimal control and controller design. The goal is to present new trends in the field of automatic control that use recent evolutionary or neural techniques, either as a standalone approach or combined with state-of-the-art controllers. The findings of this Special Issue include exploratory studies (even if they are computational), theoretical studies, and experimental studies.

Full-length articles containing the latest relevant findings are encouraged. Review articles can be considered in exceptional circumstances. Studies that focus on swarm controllers, hardware/software development for controllers’ performance improvements, evolutionary controllers, and high-performance hardware/firmware for control applications, among others, are welcome. The target systems could be single-input single-output (SISO), and multi-input multi-output (MIMO).

Dr. António José Ramos Silva
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. Applied Sciences 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 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

  • adaptive control systems
  • stochastic systems
  • optimization method
  • artificial intelligence
  • network architecture
  • reinforcement learning

Published Papers (2 papers)

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Research

19 pages, 1278 KiB  
Article
A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller
by Raúl López-Muñoz, Daniel Molina-Pérez, Eduardo Vega-Alvarado, Pino Duran-Medina and Mario C. Maya-Rodriguez
Appl. Sci. 2024, 14(12), 5078; https://doi.org/10.3390/app14125078 - 11 Jun 2024
Viewed by 366
Abstract
This work presents a methodology to solve optimization problems with dynamic-size solution vectors containing continuous and integer variables. It is achieved by reformulating the original problem through a bilevel optimization approach and implementing metaheuristic techniques to solve it. In the selected case study, [...] Read more.
This work presents a methodology to solve optimization problems with dynamic-size solution vectors containing continuous and integer variables. It is achieved by reformulating the original problem through a bilevel optimization approach and implementing metaheuristic techniques to solve it. In the selected case study, the optimization problem corresponds to tuning a neuro-fuzzy controller (NFC) that operates in a biodiesel production system for controlling temperature. The NFC performs well and is especially robust to disturbances, but due to its complexity, it is difficult to determine the best set of parameters for its use. This has led to biased searches based on criteria such as the experiences of designers. With the proposed method, it was possible to obtain a tuning that—when implemented in a simulation—led to results that surpassed those documented in the literature. Finally, the proposal offers flexibility for implementation with other controllers that have similar architectures and can be integrated into various other plants or processes. Full article
(This article belongs to the Special Issue Technical Advances in Optimal Control and Controller Design)
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21 pages, 661 KiB  
Article
Robust Control of An Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning
by Yan Ma, Dengguo Xu, Jiashun Huang and Yahui Li
Appl. Sci. 2023, 13(24), 13181; https://doi.org/10.3390/app132413181 - 12 Dec 2023
Cited by 1 | Viewed by 1087
Abstract
This paper is primarily focused on the robust control of an inverted pendulum system based on policy iteration in reinforcement learning. First, a mathematical model of the single inverted pendulum system is established through a force analysis of the pendulum and trolley. Second, [...] Read more.
This paper is primarily focused on the robust control of an inverted pendulum system based on policy iteration in reinforcement learning. First, a mathematical model of the single inverted pendulum system is established through a force analysis of the pendulum and trolley. Second, based on the theory of robust optimal control, the robust control of the uncertain linear inverted pendulum system is transformed into an optimal control problem with an appropriate performance index. Moreover, for the uncertain linear and nonlinear systems, two reinforcement-learning control algorithms are proposed using the policy iteration method. Finally, two numerical examples are provided to validate the reinforcement learning algorithms for the robust control of the inverted pendulum systems. Full article
(This article belongs to the Special Issue Technical Advances in Optimal Control and Controller Design)
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