International Journal of Mechatronics and Manufacturing Technology ISSN: 3139-5058 (Online)

Authors: R. K. Sharma, P. Tiwari, N. A. Khan, S. Verma

Abstract: Industrial robots are widely used in manufacturing environments to perform repetitive and precise operations. However, traditional robot control systems rely on fixed models and pre-programmed trajectories which are not suitable for dynamic and uncertain industrial conditions. Recently, artificial intelligence (AI) based adaptive control strategies have emerged as an effective approach to enhance flexibility, robustness, and autonomy in robotic systems. This paper presents a comprehensive review on AI-driven adaptive control for industrial robots, focusing on machine learning, reinforcement learning, neural networks, and hybrid intelligent control frameworks. The study explains how AI enables robots to adjust control parameters in real time based on environmental variations, payload changes, and task uncertainties. Different adaptive control architectures, learning methods, and industrial applications such as assembly, welding, and material handling are discussed. Advantages, limitations, and practical challenges in industrial deployment are also analysed. The paper concludes that AI-based adaptive control significantly improves accuracy, safety, and productivity of industrial robots, though issues like training data requirements and computational cost remain important considerations.

Keywords: Industrial robots, adaptive control, artificial intelligence, reinforcement learning, neural networks, intelligent robotics, automation.