Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 22 references
Abstract
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-DoF force sensor, a continuous transition function is synthesized to discriminate between intentional human contact and accidental impacts. The method enables the system to switch between admittance and hybrid control modes, eliminating control discontinuities. Uniform Ultimate Boundedness (UUB) of the closed-loop system is proven via Lyapunov analysis. Validation on an xArm-5 manipulator confirms that the transition bounds the error energy during impacts, enhancing safety and versatility during execution.
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 these issues, this paper proposes a guaranteed-performance neural adaptive admittance control framework. First, the damping coefficient is dynamically tuned using real-time interaction force feedback, while a neural network (NN) is employed to estimate HMI-induced uncertainties in the coupled human-robot system. These two components are then integrated into the admittance model to construct a high-level interaction strategy that generates compliant reference trajectories for smooth and stable collaboration. Subsequently, low-level motion control with error transformation is developed to enforce prescribed output constraints, thereby ensuring unified regulation of transient and steady-state performance. Moreover, another NN is introduced to approximate the lumped robot dynamics for improved tracking accuracy. Finally, the effectiveness and superiority of the proposed method are validated through trajectory tracking, circle drawing, and obstacle avoidance tasks. Note to Practitioners—This paper focuses on developing an active interaction control approach that enables high-performance tracking for robots subject to model uncertainties while providing high-quality assistance to operators with unknown motion intention. The proposed framework is well-suited to industrial applications such as human-robot cooperative assembly and co-transportation. By incorporating output-constraint-based neural adaptive admittance control, safe, reliable, and compliant physical interaction can be achieved. Consequently, the controller supports further extension to medical rehabilitation and exoskeleton systems, demonstrating broad promise across a wide range of interaction-intensive scenarios.
Chengguo Liu, Hefu Ye, Kai Zhao· IEEE Transactions on Automat...· 0 citations
This paper presents a human–robot interaction (HRI) scheme by using an adaptive admittance control, which helps stroke patients perform rehabilitation training tasks and optimizes their performance. Considering the impact of human factors, the control structure is designed to have two control loops. In the inner loop design, a model‐free adaptive control (MFAC) method is proposed to handle the unmodeled dynamics and unknown disturbances for the desired trajectory tracking, and the convergence and boundedness of this method are strictly proved by using the compression mapping principle. Then, a task‐specific outer loop is developed to find the optimal parameters of the admittance model and transformed into an LQR problem, and a learning algorithm is utilized to solve the given problem without requiring knowledge of the human arm model. Considering the safety of HRI, the constraint of the end‐effector orientation is designed. Simulation studies indicate that the proposed strategy effectively enables stroke patients to execute active training tasks on the robotic exoskeleton.
Unknown authors· International Journal of Rob...· 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 unstructured or collaborative environments. This paper presents an AI-enabled force and torque control framework that integrates machine learning techniques with conventional control methods to enhance adaptability, compliance, and safety in physical human robot interaction. The proposed approach employs deep neural networks and reinforcement learning to learn human intent and interaction dynamics directly from multi-modal sensor data, including force torque sensors, joint encoders, and inertial measurements. By continuously adjusting control gains in real time, the system achieves stable interaction while minimizing excessive contact forces and undesired torques. Experimental evaluations conducted on a collaborative robotic platform demonstrate significant improvements over classical control schemes, including reduced interaction force peaks, smoother torque profiles, and improved task execution efficiency during cooperative manipulation tasks. The results indicate that AI-driven force and torque control can substantially improve robustness, adaptability, and user comfort in human robot collaboration, making it a promising solution for applications in rehabilitation robotics, assistive devices, and industrial cobots.
Vishal Khanna· i-manager's Journal on Augme...· 0 citations
This paper aims to propose an adaptive control framework for mobile manipulators to seamlessly balance autonomous task execution with human intervention during physical human–robot interaction. The objective is to coordinate a nonholonomic mobile base and a redundant manipulator in response to force-amplitude-based human guidance.
The approach integrates state-dependent dynamical systems for autonomous task encoding with a variable admittance control scheme for reactive interactions. To address the distinct kinematic constraints of the mobile platform and the end-effector, a configuration-dependent damping adjustment method and an external-force-based velocity arbitration mechanism are developed. These methods dynamically modulate the reference velocity generated by the dynamical system and the force-induced admittance velocity, enabling adaptive velocity fusion based on real-time force signals.
Simulation and real-robot experiments using an xArm7 manipulator mounted on a SMART mobile platform demonstrate the efficacy of the proposed framework. The results indicate that the mobile manipulator exhibits bounded and responsive behavior under the tested conditions, effectively reconfiguring to accommodate human guidance while maintaining task velocity.
This work treats the mobile manipulator as a unified redundant system rather than two separate entities. By combining dynamical-system-based trajectory generation with a novel velocity arbitration law, the proposed method realizes real-time, force-mediated coordination of the entire system. This offers a possible solution for high-degree-of-freedom mobile manipulators performing collaborative tasks in industrial environments.
This paper develops a contact-control scheme for multi-degree-of-freedom (DOF) robot arms that relies on dynamic impedance to jointly improve precision, stability, and robustness. Classical impedance controllers keep stiffness, damping, and inertia fixed, which limits how well they adapt as operating conditions change. Here an impedance model is built specifically around the end-effector, and position control is refined by adding force-based corrections to the desired position along the force- controlled axes. Dynamic impedance is realized by tying the impedance parameters to the joint angles and angular velocities, using the final value theorem together with quadratic-form transient-response analysis. The resulting method is validated in MATLAB/Simulink through wall-wiping simulations performed with a 6-DOF RPY-type robot.
Kaisei Hosoyama, Qingjiu Huang· 2026 IEEE International Conf...· 0 citations
Artificial potential fields (APFs) are widely used for collision avoidance in robotic systems due to their simplicity and real-time performance. However, in cooperative environments, robots may undergo unnecessary displacements caused by repulsive forces from neighboring robots, even after reaching their target positions. This paper presents a formation-preserving control strategy that suppresses such unnecessary motion while retaining the standard APF behavior when robots are far from their desired positions. The stability of the proposed controller is proven through Lyapunov stability analysis. Furthermore, the theoretical analysis establishes local exponential stability and positive invariance of a neighborhood of the desired configuration. A formal proof of collision avoidance is also provided, ensuring that inter-robot safety constraints are preserved. The approach is validated through two simulations and two experiments: the first illustrates APF-induced fluctuations, whereas the second demonstrates that the proposed controller enables robots to maintain their desired target positions.
Wojciech Kowalczyk, Arpit Joon, P. Herman· Applied Sciences· 0 citations
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