Intelligent Adaptive Control for Nonlinear Systems: From Theory to Industrial Processes

Dear Colleagues,

Nonlinear systems are ubiquitous in engineering, ranging from robotics and power systems to unmanned vehicles and industrial processes. The control of such systems is challenging due to inherent complexities like parameter variations, unmodeled dynamics, external disturbances and so on. Adaptive control has emerged as a powerful framework to address these challenges by enabling controllers to adjust themselves in response to changing system dynamics and uncertainties. Generally, adaptive control can be divided into direct adaptive control and indirect adaptive control. Recent advances in neural networks, fuzzy logic and reinforcement learning-based approaches have significantly enhanced the capabilities of adaptive control strategies for nonlinear systems. Moreover, other advanced control approaches such as robust control, prescribed performance control, preset time control, state constraint control, input constraint control, event-triggered control, optimal control, and so on have significantly enhanced the robustness, precision, and applicability of adaptive control strategies across various domains. The latest developments in adaptive control for nonlinear systems demonstrate a clear trend toward intelligent, learning-based approaches that combine traditional control theories with advanced computational intelligence techniques. 

This Special Issue explores the latest developments in this rapidly evolving field, highlighting innovative methodologies and their practical applications across various domains, including robotics, power systems, unmanned vehicles, and so on.

Topics include but are not limited to the following:

• Direct Adaptive Control
• Indirect Adaptive Control
• Neuroadaptive Control 
• Fuzzy Logic Control
• Reinforcement Learning
• Machine Learning
• Iterative Learning Control
• Multiple Model Adaptive Control
• Model Reference Adaptive Control
• Adaptive Signal Processing Theory and Algorithms
• Nonlinear Systems
• Robotic and Mechatronic Systems
• Power and Energy Systems
• Multi-Agent Systems

Dr. Guichao Yang
Dr. Zhenle Dong
Guest Editors

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