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.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131045