Adaptive Control Strategies for Human-Robot Interaction in Industrial Setting

Abstract: Human–Robot Interaction (HRI) represents a crucial frontier in modern robotics, enabling robots to collaborate intelligently and safely with humans across industrial, medical, and service environments. However, dynamic human behaviour, unpredictable environmental conditions, and task variability pose significant challenges to achieving seamless interaction. This study introduces a novel Adaptive Control Strategy (ACS) framework designed to enhance the responsiveness, safety, and efficiency of HRI systems. The proposed approach integrates reinforcement learning, fuzzy logic control, and model predictive control (MPC) to enable robots to dynamically adjust their motion, force, and communication behaviour based on continuous feedback from human partners and environmental sensors.

The adaptive framework allows the robot to learn and anticipate human intentions in real time through multimodal sensory fusion, combining vision, force, and voice data streams. By employing online learning and parameter tuning, the control system ensures smooth trajectory tracking, minimizes physical and cognitive workload on humans, and prevents unsafe interactions. Experimental evaluations were conducted using both simulated and real-world HRI scenarios involving cooperative manipulation and shared workspace tasks. The results demonstrate that the proposed adaptive control model achieves significant performance improvements, including faster response adaptation, reduced interaction delays, and enhanced stability compared to conventional fixed-gain and non-adaptive controllers.

Furthermore, statistical analysis indicates that the system achieves interaction accuracy above 96%, maintaining robust performance under uncertain human motions and external disturbances. The adaptive nature of the controller allows it to generalize across diverse human behaviour without explicit reprogramming, thereby improving scalability and usability. This research contributes to the advancement of intelligent robotic systems by presenting a human-centered, data-driven control architecture that evolves continuously, fostering safe, natural, and efficient collaboration between humans and robots in real-world settings.

Keywords: Adaptive Control, Human–Robot Interaction, Reinforcement Learning, Fuzzy Logic, Model Predictive Control, Intelligent Robotics.