Artificial Intelligence (AI) Techniques for Intelligent Control Systems in Mechanical Engineering

Abstract

In mechanical engineering, control systems are essential to the dependable and effective operation of mechanical equipment and processes. The advantages of integrating AI into control systems include energy savings, greater product quality, increased process effectiveness, and adaptive and predictive control. Various AI approaches, including machine learning methods like decision trees, neural networks, and support vector machines, are extensively studied for system identification, modelling, and adaptive control. AI techniques enhance these applications' control performance, energy efficiency, problem discovery, and maintenance. The methodology uses metaheuristic techniques for creating intelligent control systems, such as simulated annealing, ant colony optimisation, and harmony search. With the help of these algorithms, the solution space is efficiently searched for ideal control strategies while avoiding local optima. The importance of evaluating the learned control algorithm on a different dataset or running experiments to determine its performance is also underlined. Performance assessment benchmarks for tracking error, settling time, overshoot, and energy efficiency are provided in the results section. This paper investigates the use of AI techniques in mechanical engineering intelligent control systems. The paper also offers areas for additional research in intelligent control systems and identifies research directions for future advancements.