Jul 2026· International Conferences on Human-Machine Systems· pp. 301-306· 0 citations· 18 references
Abstract
Passivity-based control theory has emerged as a promising framework for physical human-robot interaction by explicitly enforcing the energetically passive relation. However, maintaining passivity at all times may overly limit the task execution capability of the robot, potentially increasing human physical workload. Furthermore, interaction safety can be violated in conventional passivity-based control approaches due to ignoring stored energy level and energy rate constraints. In this paper, an adaptive energy-based robot control is proposed for physical human-robot interaction by extending the passivitybased control and integrating additional safety constraints. Specifically, this method alleviates the inherent conservatism of conventional passivity-based control by ensuring passivity in the closed-loop system only when the system's energy exceeds a predefined threshold, while allowing more flexible behaviors otherwise. Additionally, adaptive control parameter laws, stored energy level saturation, and energy rate constraints are integrated into the control method to enhance task performance and safe interaction. Numerical simulation and human-in-the-loop co-carrying experiment are conducted to validate the feasibility and effectiveness of the proposed approach.
A robust coupled cognitive - physical shared-control framework for human - robot collaboration that guarantees forward invariance of the safe set and bounded closed-loop signals is proposed.
Yana Yang, Zhijia Li, Huixin Jiang et al.· Intelligence & Robotics· 0 citations
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
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.
G. E. Sánchez-Valdés, C. Cruz-Villar, J. E. Chong-Quero· 2026 IEEE/ASME International...· 0 citations
The findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.
Effective physical human-robot interaction in space exploration is built on a foundation of admittance and impedance-controlled manipulators. Series-elastic actuators enable these manipulators to render compliant behaviors that are ideal for safe and responsive interaction. However, the cascaded control architectures commonly used in space robotics enforce a strict bandwidth hierarchy, requiring low-level position or torque control loops to operate significantly faster than high-level admittance or impedance loops. This constraint limits achievable performance by preventing the manipulator from expressing the full range of its potential impedance behaviors. Leveraging recent advancements in spaceflight computing, we present a non-cascaded, state-space full-state feedback control architecture that removes the bandwidth hierarchy requirement. This approach enables simultaneous realization of stiff, compliant, and natural impedance behaviors across multiple Cartesian degrees of freedom, a capability we call Full-Range Cartesian-Decoupled Impedance Control. The approach is validated in simulation on a three-degree-of-freedom NASA Valkyrie upper-arm testbed with series-elastic joints. Comparative experiments with a conventional cascaded controller demonstrate a wider stable impedance range, expanding the spectrum of safe human-robot collaboration behaviors for future space missions.
Samuel Sowell, Gray C. Thomas· 55th International Conferenc...· 0 citations
The concept of human-robot collaboration (HRC) is becoming a significant part of the contemporary industry as it aims at enhancing productivity, flexibility, and ergonomics. As opposed to conventional industrial robotics, where keeping physical distance between humans and robots guarantees safety, collaborative robots (cobots) work in the same work areas whereby humans and robots are in close and simultaneous contact. The above paradigm shift brings about huge safety issues because of unpredictable human behaviors, changing environments as well as balancing between safety and operation. Traditional reactive safety measures, e.g., emergency stops and fixed safety zones, tend to cause unjustifiable down-time and low productivity. In this paper, the paper builds up detailed research on improving the safety of the shared workspace of human and robot with predictive control methods the author emphasizes the idea of Model Predictive Control (MPC) and its variations. Predictive control provides the opportunity to predict upcoming human behaviors and environmental variations enabling the adjustment of the robot paths, its speed, and interaction forces in advance. This framework is suggested and combines human motion prediction, dynamic safety constraints, and optimal control formulation to realize safe, smooth, and efficient human-robot collaboration. An elaborate system architecture is created which includes sensor fusion, real time prediction models and constrained optimization. The mathematical formulations of the predictive control problem are offered, including cost functions and safety constraints that meet the international safety standards. The feasibility of the suggested solution is assessed by the means of simulation-based scenarios of the most common industrial processes, including cooperative assembly and handling of materials. Findings indicate that the involved safety measures, the lower probability of a collision, the ease of control of the behavior of a robot, as well as greater functionality in its tasks, are significantly better than those of the traditional reactive control schemes. The article is informative and gives a systematic guide to scholars and developers intending to implement predictive safety control in the collaborative robot system and the future research directions to adhere to robust, explainable, and standardized human-robot safety solutions.
Samuel O’ Brell, Kelvin Ling· International Journal of Int...· 0 citations