Dear Colleagues,

The design and analysis of traditional control systems is based on accurate system mathematical models. However, practical processes are complex, nonlinear, and time varying. It is generally impossible to obtain an accurate mathematical model due to the uncertainties. For the actual system, it is often necessary to put forward and follow certain harsh linearization assumptions, which are often inconsistent with the reality in application. Moreover, some complex and uncertain control objects cannot be described by the traditional mathematical model, which makes traditional model-based control methods invalid. There is a paradox in that the actual control task is complex, while the traditional control task has low requirements and cannot reduce complexity.

In recent years, with the rapid development of artificial intelligence, robotics, advanced manufacturing, power systems, aerospace, and other fields, traditional control methods cannot meet their requirements of complex dynamic processes. Therefore, a variety of advanced intelligent control methods, such as fuzzy control, data-driven control, neural network control, and learning control, have emerged and achieved successful applications.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging theories and applications with advanced nonlinear intelligent control and address recent breakthroughs from theoretical and practical aspects.

The topics of interest include but are not limited to:

• Fuzzy control;
• Neural network control;
• Reinforcement learning;
• Model-free control;
• Data-driven control;
• Nonlinear intelligent control: theory and applications;
• Intelligent control algorithms and their applications in power system, robotics, unmanned vehicles, etc.

Prof. Dr. Na Dong
Prof. Dr. Xing Fang
Guest Editors

Manuscript Submission Information

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• intelligent control
• neural network control
• data-driven control
• fuzzy control
• reinforcement learning