Intelligent control of robotic manipulators: a comprehensive review

Technological advancements in robotics significantly impact the design of robotic manipulators and their control. Manipulators find applications in electrical, mechanical, and process industries to reduce labor and improve accuracy. Controlling manipulators is challenging because of their complex dynamics and nonlinear properties. Researchers are exploring many ways to implement the effective control method, including classical and modern techniques. In all the applications, robotic manipulators interact with the real world. Therefore, they require an understanding of input-output relations, which raises the need for intelligent control methods. The revolutionized growth in artificial intelligence has significantly influenced robotic manipulators' control. This paper presents a detailed review of intelligent control techniques implemented on robotic manipulator systems. These intelligent control methods include artificial neural networks, fuzzy logic control, expert systems, metaheuristic algorithm, machine learning control, etc. These intelligent methods for robotic manipulators have gained more attention because they emulate human intelligence, which finds application in diverse fields of science and engineering. In this paper, authors have investigated and compared metaheuristic algorithm and their application in robotic manipulators.