This paper proposes a safe and effective human–robot physical interaction control framework for exoskeleton robots that enhances system compliance and safety while enabling the robot to adapt to human motion. The framework is designed around two primary objectives: first, a model-free adaptive control method is employed for reference trajectory estimation to achieve real-time estimation of human motion intention; second, the Forgetting Factor Recursive Least Squares (FFRLS) method is utilized for online estimation and the learning of human impedance parameters, considering their time-varying nature. In addition, a model-free adaptive trajectory tracking control strategy is proposed to optimize control performance during human–robot physical interaction. Simulation results demonstrate that the proposed control framework outperforms conventional methods significantly in terms of safety and compliance.
Physical human-robot interaction (pHRI) offers considerable potential for improving task efficiency and alleviating operator workload. Nevertheless, the intrinsic variability of human motion intention (HMI) and robot model uncertainties pose substantial challenges to achieving accurate coordinated control. To address t...
Chengguo Liu, Hefu Ye, Kai Zhao· IEEE Transactions on Automat...· 0 citations
Safe and intuitive human robot interaction (HRI) requires precise regulation of contact forces and torques while adapting to dynamic and uncertain human behavior. Traditional impedance and admittance control strategies rely on fixed parameters and accurate system modeling, which often limit their performance in unstruc...
Vishal Khanna· i-manager's Journal on Augme...· 0 citations
Simulation and experimental results demonstrate that compared to traditional PID control and sliding mode control (SMC), the ARCEC method exhibits significant advantages in trajectory tracking accuracy, enabling the lower-limb exoskeleton to precisely track human gait curves.
Chao Yang, Xin Han, Zhijue Huang et al.· International Conference on...· 0 citations
To address the coupling between ambiguous sEMG-based intention recognition and mechanical safety constraints in cable-driven lower-limb rehabilitation, this study proposes a multimodal perception-based adaptive admittance control framework. Mechanical, interaction, and physiological indicators, including stiffness, end...
Yanzhuo Wang, Keyi Wang, Lan Wang et al.· 2026 IEEE International Conf...· 0 citations
The present work introduces a transition strategy to implement a hybrid force/admittance control scheme for collaborative robots in physical Human-Robot Interaction (pHRI). The architecture utilizes orthogonal projections to decouple force and motion subspaces. By evaluating the magnitude and time derivatives of a 6-Do...
G. E. Sánchez-Valdés, C. Cruz-Villar, J. E. Chong-Quero· 2026 IEEE/ASME International...· 0 citations
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