Design and Analysis of Adaptive Control Systems for Collaborative Robotics in Industrial Environments 4.0

Abstract

The development of Industry 4.0 demands a more flexible, adaptive, and safe production system, especially in the context of collaboration between humans and robots. One of the main challenges is to develop a control system that is able to adapt in real-time to the dynamics of a complex work environment. This research aims to design and analyze machine learning-based adaptive control systems for collaborative robotics in modern industrial environments. The research approach used is a qualitative method with case studies in five manufacturing companies that have implemented cobots technology. Data collection was carried out through in-depth interviews, participatory observations, and technical documentation, then analyzed using thematic analysis techniques. The results show that adaptive control systems significantly improve production flexibility by accelerating adaptation to task changes, improve the safety of human-robot interactions by reducing work incidents, and improve operational efficiency through reduced downtime and production line optimization. The human cognition-based control system has also been shown to reinforce the fit between operator expectations and robot behavior. In conclusion, the adaptive control system plays a crucial role in realizing a smart factory based on harmonious collaboration between humans and machines. The practical implications of this research are the importance of integrating adaptive controls in the design of future industries to improve productivity, occupational safety, and operational sustainability.