Hybrid Control Systems

Abstract

In recent years, hybrid control systems have been used in many practical applications because they are an adequate method capable of addressing this kind complexity and vagueness, which is characteristic of today's business and scientific environment. It provides the evaluation of the fundamental concepts and solutions for the incorporation of AI in conventional control schemes. The chapter also examines other fields including robotics, aeroplane, industrial processes and power systems as other areas that have benefited from hybrid control methods. To solve the difficulties posed by nonlinear, time varying, and stochastic systems, the interaction of machine learning approaches such as artificial neural networks, fuzzy logic, and adaptive control with PID, optimal and robust control approaches is explored. Possible research topics bring concerns the analysis of hybrid control architectures for large-scale, contingent, safety-relevant systems, the extension of sensing and communication functionalities, and design of proper planning and decision-making strategies.