ARTIFICIAL INTELLIGENCE BASED CONTROL SYSTEMS FOR ROBOTICS AND RENEWABLE ENERGY APPLICATIONS

Abstract

The high development rate and growing complexity of current systems in electrical engineering prompted the incorporation of artificial intelligence-oriented approaches to control mechanisms, which have the potential to manage non-linear dynamics, uncertainties of the system, and changing operating environments in an effective manner. In this manuscript, we present a detailed narrative review of recent progress in AI-based methods of control with a specific emphasis on the implementation of the methods in the context of robotics and renewable energy systems. The review discusses major intelligent control paradigms among them artificial neural networks, fuzzy logic control, reinforcement learning, evolutionary optimization techniques and hybrid intelligent control structure. The literature survey is organized by the main areas of applications including robotic motion and trajectory control, renewable energy conversion systems, microgrid operation, and power electronic applications. Moreover, the paper gives a critical comparison of AI-based controllers against the traditional control methods including Proportional Integral Derivative, Linear Quadratic Regulator, H∞ control highlighting the variations of adaptability, robustness, computational load, and model transparency. Significant constraints associated with data accessibility, operational safety, explainability as well as real-life use are also considered in more detail. Lastly, the we provide practical implications and the prospective areas of research that can be used to support the concept of creating dependable, effective, and scalable AI-based control solutions. The work is also meant to have a good reference to researchers and practitioners involved in intelligent control application in the field of robotics and renewable energy.