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Applied Optimization in Automatic Control and Systems Engineering
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Applied Optimization in Automatic Control and Systems Engineering

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1549

Special Issue Editor

Prof. Dr. Guillermo Valencia-Palomo

E-Mail Website
Guest Editor
Tecnológico Nacional de México/Instituto Tecnológico de Hermosillo, Ave. Tecnológico y Periférico Poniente SN, Hermosillo 83170, Mexico
Interests: predictive control; optimization; LPV systems; fault detection and isolation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applied optimization in automatic control and systems engineering involves the development and implementation of mathematical strategies to improve the performance and efficiency of automated systems. This interdisciplinary field combines principles from control theory, operations research, and computational algorithms to design and fine-tune systems for optimal operation. Techniques such as linear programming, nonlinear optimization, and dynamic programming are employed to solve complex problems in real-time, ensuring systems respond effectively to changing conditions and constraints. Applications range from industrial automation and robotics to aerospace and energy systems, where optimizing parameters like speed, accuracy, and resource usage is crucial for achieving desired outcomes. Through continuous advancements, applied optimization in automatic control and systems engineering drives innovation and enhances the capabilities of modern automated systems.

In this Special Issue, the aim is to discuss the state of the art of the most advanced optimization techniques (online and offline) and their applications in automatic control and systems engineering. Potential topics include (but are not limited to) the following:

  • Optimal control of nonlinear systems;
  • Optimal control of complex systems;
  • Optimal observer design;
  • Predictive control;
  • Neuronal networks;
  • Control systems;
  • Fault detection;
  • Fault tolerant control.

Prof. Dr. Guillermo Valencia-Palomo
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. Mathematical and Computational Applications 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 1400 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

  • LMIs
  • optimal control
  • systems engineering

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Published Papers (2 papers)

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Research

30 pages, 858 KiB  
Article
Sliding Mode Fault-Tolerant Control for Nonlinear LPV Systems with Variable Time-Delay
by Omayma Mansouri, Ali Ben Brahim, Fayçal Ben Hmida and Anis Sellami
Math. Comput. Appl. 2024, 29(6), 96; https://doi.org/10.3390/mca29060096 - 26 Oct 2024
Viewed by 534
Abstract
This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states [...] Read more.
This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states and actuator faults of LPV polytopic delayed systems. Second, a sliding mode FTC is developed, ensuring all states of the closed-loop system converge to the origin. This paper presents an integrated sliding mode FTC strategy to achieve optimal robustness between the observer and controller models. The integrated design approach offers several advantages over traditional separated FTC methods. Our novel approach is based on incorporating adaptive law into the design of the Lyapunov–Krasovskii functional to improve both robustness and performance. This is achieved by combining the concept of sliding mode control (SMC) with the Lyapunov–Krasovskii function under the H∞ criteria, which plays a key role in guaranteeing the stability of this class of system. The effectiveness of the proposed method is demonstrated through a diesel engine example, which highlights the validity and benefits of the integrated and separated FTC strategy for uncertain nonlinear systems with time delays and the sliding mode control. Full article
(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)
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14 pages, 3165 KiB  
Article
Optimized Nonlinear PID Control for Maximum Power Point Tracking in PV Systems Using Particle Swarm Optimization
by Maeva Cybelle Zoleko Zambou, Alain Soup Tewa Kammogne, Martin Siewe Siewe, Ahmad Taher Azar, Saim Ahmed and Ibrahim A. Hameed
Math. Comput. Appl. 2024, 29(5), 88; https://doi.org/10.3390/mca29050088 - 2 Oct 2024
Cited by 1 | Viewed by 770
Abstract
This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by [...] Read more.
This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by a metaheuristic algorithm called Particle Swarm Optimization (PSO). The proposed methods appear to present adequate solutions to overcome the drawbacks of existing methods despite various weather conditions considered in the analysis, providing a robust solution for dynamic environmental conditions. The results showed better performance and accuracy compared to those encountered in the literature. We also recall that this technique provides a systematic design procedure in the search for the MPPT in photovoltaic (PV) systems that has not yet been documented in the literature to the best of our knowledge. Full article
(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)
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