Latest Applications of Artificial Intelligence in Control Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 120
Special Issue Editor
Interests: AI; neural networks; machine learning; big data; SMC; orthogonal polynomials/filters; autonomous driving technology; cyber-physical systems
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Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence (AI) have significantly influenced the analysis and design of modern control systems, particularly in the presence of nonlinear dynamics, uncertainty, and large-scale complexity. These developments create new opportunities, but also demand sound mathematical modeling, analysis, and validation to ensure reliability and performance. This Special Issue aims to bring together contributions that explore mathematically grounded AI methods for control systems, covering both theoretical developments and application-oriented studies supported by solid analytical frameworks.
Topics of interest include learning-based and adaptive control, data-driven system identification, reinforcement learning for control, neural network approximation, optimization-based control, model predictive control with learning components, and distributed or networked control systems. Contributions are encouraged to employ mathematical tools such as Lyapunov methods, optimization theory, stochastic analysis, or approximation theory to analyze stability, convergence, robustness, or performance. Application-driven papers are welcome when they clearly demonstrate the role of underlying mathematical methods and provide analytical insight into AI-enabled control strategies. This Special Issue seeks to promote constructive interaction between AI techniques and control theory within a rigorous mathematical context.
Dr. Staniša Perić
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence in control systems
- learning-based and data-driven control
- stability and robustness analysis
- adaptive and intelligent control
- mathematical methods for dynamical systems
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