Advances in Control Theory and Optimizations

Dear Colleagues,

This Special Issue showcases cutting-edge advancements in both theoretical frameworks and practical applications, highlighting the ever-evolving nature of control systems and optimization techniques. As modern systems grow increasingly complex, new methodologies—including artificial intelligence (AI) and machine learning—have emerged as powerful tools to enhance control strategies, optimize performance, and enable adaptive decision-making.

The articles within this issue delve into a diverse array of topics, including robust control strategies, optimization algorithms, and AI-driven approaches applied to fields such as robotics, aerospace, and smart grid technology. Each paper not only presents novel methodologies but also demonstrates their effectiveness through rigorous simulations and real-world case studies, underscoring the significant impact of these advancements on industry and academia alike.

Furthermore, this issue emphasizes the interdisciplinary nature of control theory and optimization, fostering collaboration across different domains to tackle complex challenges in engineering and applied sciences. The integration of AI into traditional control methodologies exemplifies this cross-disciplinary innovation, allowing for more efficient, adaptive, and intelligent systems. We believe that the insights shared in this Special Issue will inspire future research directions and foster a deeper understanding of the intricate relationships between control systems, optimization processes, and AI-enhanced decision-making.

We invite readers to engage with these works, as they represent a significant contribution to the ongoing dialogue in the field, offering both established researchers and newcomers valuable perspectives on the latest innovations and future trends. Join us in exploring the remarkable journey of discovery and application that is defining the forefront of control theory and optimization.

Prof. Dr. Francisco Miranda
Dr. Helena Sofia Rodrigues
Guest Editors

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. Mathematics 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.

• nonlinear and optimal control methods
• robust control and stability analysis in AI-enhanced systems
• model predictive control and real-time optimization
• adaptive and intelligent control strategies
• reinforcement learning and data-driven control strategies
• model predictive control and real-time optimization
• AI-driven decision-making in control systems
• game theory and multi-agent control
• computational methods in control and optimization
• computational intelligence and evolutionary optimization