Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems

Dear Colleagues,

Adaptive control serves as a foundational element of modern control engineering, enabling systems to autonomously maintain performance, stability, and robustness in the face of dynamic uncertainties and changing operating conditions. Its importance extends beyond theoretical innovation to mission-critical applications, effectively bridging the gap between idealized models and practical implementation.

However, the adaptive control of nonlinear systems involves significant challenges. These include establishing mathematically rigorous stability guarantees, ensuring robustness against unmodeled dynamics and external disturbances, and achieving computational efficiency sufficient for real-time operation.

This Special Issue, titled “Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems”, aims to highlight recent advances in the adaptive control theory and practical implementations. In response to feedback from the Editorial Board, we explicitly welcome contributions that also cover advanced topics in robust adaptive control with fixed/predefined-time convergence, data-driven adaptive dynamic programming and learning, adaptive control for multi-agent systems, adaptive model predictive control, adaptive control under multi-constraints, and hybrid learning-adaptive control fusion.

We encourage works that combine theoretical developments with real-world applications of control techniques, including, but not limited to, sliding mode control, intelligent observers, fractional-order controllers, high-gain estimation, machine learning-based adaptive schemes, adaptive graph neural networks, semi-supervised multi-modal fusion, permanent magnet motors, nonlinear modeling, optimal control, aircraft control, synchronization, system identification, and defect detection. Contributions exploring the convergence between robust linear theory and nonlinear dynamics are especially welcome.

Of particular interest are studies that demonstrate the interplay between mathematics and engineering, showcasing innovative uses of nonlinear models, optimal feedback strategies, and data-driven approaches applied to electrical, biological, or mechanical systems. Cross-disciplinary research combining control theory with robotics, biotechnology, computer vision, artificial intelligence, and process industries is highly encouraged.

We invite researchers from academia and industry to submit original papers that contribute to this vibrant and evolving field, strengthening the connection between mathematical theory and technological innovation.

Dr. Jianhua Zhang
Dr. Weicun Zhang
Prof. Dr. Jing Na
Prof. Dr. Qiang Chen
Guest Editors

Manuscript Submission Information

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