Dynamics and Intelligent Control of Complex and Switched Systems

Dear Colleagues,

Complex Systems processes can be described by nonlinear models of processes by using, for example, differential equations, and find key control applications in several industries such as energy systems, the automotive industry, aerospace engineering, biotechnology and so on.

A switched linear system is a hybrid/nonlinear system which consists of several linear subsystems and a switching rule that decides which of the subsystems are active at each moment in time. They are dynamical systems formed by a collection of linear continuous state models switching among them according to a discrete signal. These systems have shown to be useful for representing complex behaviors of physical systems interacting with logical rules or controllers. A switched system may be obtained from a hybrid system by neglecting the details of the discrete behavior and instead considering all possible switching patterns from a certain class.

Nowadays, it is a huge expectation concerning the use of various intelligent control techniques in order to control nonlinear and complex processes, in many cases with the emergence of advanced artificial intelligence. Intelligent control is in fact a computationally efficient procedure of guiding a complex system (uncertain, eventually with incomplete specifications) in an uncertain environment toward a certain goal. Therefore, an intelligent control technique needs learning of both the process and the environment to be a part of the control system. There are many definitions and classifications of intelligent control. However, it can be defined as a class of control techniques that use various artificial intelligence computing approaches such as neural networks, fuzzy logic, machine learning, evolutionary computation, genetic algorithms, etc.

This Special Issue proposes to bring together researchers, scientists and engineers from academia and industry so as to disseminate ideas and results related to the use of advanced techniques in the field of intelligent control of complex, nonlinear processes and switched systems.

Potential topics pertain, but are not limited, to intelligent control applied to Complex and Switched Systems:

• Neural networks and fuzzy logic control;
• Reinforcement learning-based control of nonlinear processes;
• Deep-learning-based intelligent control;
• Intelligent optimization and applications to nonlinear processes;
• Intelligent modeling, identification and estimation of nonlinear processes;
• Stability and robustness analysis of intelligent control systems;
• New trends in intelligent control.

Theoretical and practical studies are equally encouraged. Application areas include energy systems (renewable energy, smart grids, electric drives), the automotive industry, aerospace engineering and biotechnology (including environmental systems and biomedical systems).

Dr. Marcel Nicola
Prof. Dr. Dan Selisteanu
Prof. Dr. Cosmin Ionete
Guest Editors

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• identification and estimation of complex processes
• optimal control
• distributed control
• agent-based control
• switched systems
• reinforcement learning
• neural networks and fuzzy logic control
• stability and robustness