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Abstract

Smart control systems have formed a foundation of new automation, spurring new developments in fields like autonomous systems, robotics, and energy management. Artificial intelligence (AI) integration with conventional methods of control holds potential increases adaptability, robustness, and efficiency in complex and fully real-time applications. Nevertheless, such issues as managing disturbances, energy reduction, and fault resilience are of importance. This paper will lower these hurdles by comparatively analyzing three developed control techniques: Model Reference Adaptive Control (MRAC), Deep Reinforcement Learning (DRL), and Neural Network Model Predictive Control (NN-MPC). The methods are assessed on important performance measures such as energy-efficiency, resistant to disturbances, and able to adapt to dynamic settings. MRAC algorithm is evaluated in terms of its adaptability in a predefined system and DRL investigates the capacity of the system to learn the best control policies by interacting with the environment. NN-MPC is tested based on predictive as well as energy optimization of real-time control. All of the methods were applied to a Python-based simulation platform, where it was observed that MRAC was more robust, DRL was more adaptable, and NN-MPC was more energy efficient. The major conclusions are that MRAC is more robust and stable whereas DRL can be more adapted to the unknown or dynamic environment. NN-MPC, however, is the most energy-saving and the most accurate system. Such findings emphasize the merits and demerits of both approaches and provide an insight into the idea of choosing the best control approach depending on the needs of the application. The research highlights the prospect of combining AI solutions with conventional control approaches, which bring about a holistic approach to building adaptive, robust, and energy-saving control systems in the upcoming cyber-physical implementations.
Keywords: Intelligent Control Systems, MRAC, Deep Reinforcement Learning, Neural Network Model Predictive Control, Energy Efficiency.