Nonlinear Intelligent Control: Theory, Models, and Applications

Dear Colleagues,

Practical processes are usually complex, nonlinear and time-varying. It is generally impossible to obtain an accurate mathematical model due to the uncertainties. It is of vital importance to investigate control problems of nonlinear systems due to their wide existence in the practical world, for example, motion and electrical control systems. For the actual system, it is often necessary to follow some harsh linearization assumptions, which are often inconsistent with the application reality. Moreover, some complex and uncertain control objects cannot be described by the traditional mathematical model, which makes the traditional model-based control methods invalid. There exists the paradox that the actual control task is complex, while the traditional control task has low requirements and cannot do anything about the complexity. To solve the control problems for systems with unknown nonlinearities, intelligent modeling techniques such as fuzzy and neural network modeling methods were introduced.

In recent years, with the rapid development of artificial intelligence, robotics, advanced manufacturing, power systems, aerospace and other fields, traditional control methods are unable to meet the 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, addressing recent breakthroughs from theoretical and practical aspects.

The topics of interest include, but are not limited to, the following:

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

Prof. Dr. Na Dong
Guest Editor

Manuscript Submission Information

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• intelligent control
• neural network control
• fuzzy control
• reinforcement learning