Adaptive Motion Intention Estimation and Impedance Learning for Human–Robot Interaction

Neural Adaptive Admittance Control With Guaranteed Performance for Physical Human–Robot Interaction

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.

Adaptive SMC for Lower Limb Rehabilitation Robots Using a Sliding Mode Hyperbolic ESO

AI-Enabled Force and Torque Control for Human Robot Interaction

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.

Research on adaptive robust control of lower limb rehabilitation exoskeleton

In order to improve the trajectory tracking accuracy of the lower limb rehabilitation exoskeleton robot, this paper proposes an adaptive robust error compensation control method (ARCEC) based on RBF neural network. First, the dynamics of the single-leg swing phase of the lower-limb exoskeleton are modeled using Lagrange’s equations, taking into account factors such as joint friction, flexible transmission, and the torque arising from human–robot interaction. Subsequently, nominal model compensation, online approximation via RBF neural networks, and nonlinear error feedback are integrated to mitigate the impact of model uncertainty and external disturbances on trajectory tracking performance. 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. It achieves up to a 30% reduction in tracking error, enabling the lower-limb exoskeleton to precisely track human gait curves.

Multimodal Perception-Based Adaptive Admittance Control for Cable-Driven Lower Limb Rehabilitation Robot

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-effector position, coupling force, velocity, joint motion, and sEMG, were fused to estimate active participation, passive/flaccid tracking, and spasticity risk. The identified state was then used to adjust admittance parameters online, while an RBFNN-SMC inner loop improved trajectory tracking. Experiments showed high-damping, low-stiffness protection during spasticity bursts and milder responses during passive conditions. Compared with PID, RBFNN-SMC reduced tracking errors, supporting safer adaptive rehabilitation control.

Physical Human-Robot Interaction: A Dynamic Transition Scheme Between Admittance and Hybrid Control Modes

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.