Jul 2026· ISA transactions· 0 citations· 47 references
Medicine
TL;DR
Simulation and experimental results demonstrate that the proposed mode-selective adaptive admittance force-position control framework can reduce force overshoot, improve steady-state force tracking accuracy, and enhance safe interaction in unknown time-varying environments.
Abstract
With the increasing complexity of robotic interaction tasks, robotic manipulators are required to safely interact with unknown and time-varying environments while maintaining accurate trajectory tracking. Such tasks involve frequent switching between free-motion and contact-motion phases, uncertain environmental parameters, and transient impact-induced force oscillations. To address these issues, this paper proposes a mode-selective adaptive admittance force-position control framework in task space. First, an activation matrix is introduced to select the force-controlled and position-controlled subspaces according to contact and tracking conditions. Second, an environment-aware adaptive admittance outer loop is developed by integrating Gaussian Process Regression-enhanced Extended Kalman Filter (GPR-EKF) estimation, Lyapunov-based parameter adaptation, and radial basis function neural network (RBFNN) compensation. Third, a mode-gated self-tuning PID inner loop is employed to improve trajectory tracking accuracy under different interaction modes. Simulation and experimental results demonstrate that the proposed method can reduce force overshoot, improve steady-state force tracking accuracy, and enhance safe interaction in unknown time-varying environments.
The primary control objective in robotic interaction tasks has shifted from trajectory tracking accuracy to interaction force regulation. Impedance control enables compliant interaction by shaping the robot’s force response through a target impedance model. However, the dynamic response of conventional impedance control is fixed during parameter design, limiting its adaptability to varying interaction environments. To address this issue, a direct force compensation–based adaptive hybrid impedance control (DAHIC) method is proposed. By introducing an adaptive force compensation factor based on the force tracking error, the proposed controller achieves faster response speed while suppressing overshoot and reducing steady-state error, especially in tracking higher-order signals. The allowable range of controller parameters is derived via closed-loop stability analysis, and an adaptive update law is designed accordingly. Furthermore, the proposed adaptive force compensation is integrated with hybrid impedance control to form a six-dimensional task-space compliance controller. The numerical simulations conducted in the single-force subspace, together with the comparative experiments performed in the six-dimensional task space involving constant- and variable-curvature contact surfaces as well as abrupt variations in contact stiffness, collectively validate the improvement in force tracking performance achieved by the proposed algorithm. Note to Practitioners—In robotic interaction tasks, such as assembly, surface contact, and human–robot collaboration, achieving stable and accurate force regulation in uncertain environments remains challenging for conventional impedance controllers with fixed parameters. This paper proposes a direct force compensation–based adaptive hybrid impedance control method to improve force tracking performance under varying interaction conditions. By adaptively adjusting the force compensation according to tracking error, the controller achieves faster response, reduced overshoot, and improved steady-state accuracy, and can be naturally extended to six-dimensional task-space compliance control. The stability of the closed-loop system is ensured through analytical parameter constraints, providing practical guidance for controller design. Practitioners may find this approach useful for enhancing compliant interaction performance in real-world robotic applications, while practical implementation requires appropriate force sensing, real-time computation, and careful parameter tuning.
Hongjun Xing, Yu-Zhe Xu, Yi Xie et al.· IEEE Transactions on Automat...· 0 citations
Cable-driven manipulators exhibit strong nonlinearities and low structural stiffness, which make precise control challenging under time-varying uncertainties and external disturbances. This paper presents a time-delay-estimation (TDE)-based adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) control strategy for cable-driven robots. A robust controller is constructed within a TDE-based model-free framework by combining fractional-order nonsingular terminal sliding mode error dynamics with a fast terminal sliding mode reaching law. The main contribution is a new adaptive law that introduces an adaptive exponential term into the update gain to form a nonlinear adaptive mechanism. This design improves adaptive regulation under different operating conditions by suppressing noise-induced chattering during smooth tracking while preserving or enhancing the adaptive gain during trajectory reversal. Lyapunov analysis proves the ultimate uniform boundedness of the tracking error. Experimental results show that, compared with the baseline method, the proposed controller reduces RMSE by 34.52% and 31.11%, ITAE by 33.79% and 32.97%, and ISCT by 6.69% and 17.77% for the two joints, respectively. Further comparisons with recently reported adaptive laws demonstrate that the proposed law provides faster adaptive response, more stable gain evolution, and improved chattering suppression. Additional payload tests further verify the robustness and repeatability of the proposed method.
Wenbo Gao, Yao-yao Wang, Jiawang Chen et al.· 0 citations
This study presents an adaptive sliding mode control approach for trajectory tracking of a quadrotor unmanned aerial vehicle functioning under external interference and kinematic unpredictability. Linear and rotational motion equations formulated in inactive local coordinate system frames are developed with the aid of a nonlinear six-degrees-of-freedom quadrotor dynamic model. Through mitigating excessive switching activity, reliability is improved. Here, the proposed controller integrates sliding mode control with bounded adaptive switching gain factors and boundary-layer smoothing. The operational design is applied within a sequential outer-loop/inner-loop structure for linear and orientation control. The conventional sliding mode control, alongside the proportional derivative control, which employs MATLAB/Simulink R2024a simulations while being interference-affected with an unknown performance set-up, is deployed in this work to relatively appraise the proposed ASM controller. The assessment involves three-dimensional trajectory, control input characteristics, tracking error analysis, adaptive gain growth, and chattering analysis with quantitative performance metrics. The computational output revealed that the proposed ASMC attained superior tracking performance with limited oscillation and level control action. The controller achieves a total RMSE of approximately 0.38 m and a lower aggregate tracking error when using the conventional SMC and PD controllers under equivalent conditions. Furthermore, the adaptive gain mechanism successfully lowers chattering while maintaining robustness against interferences, a large amount of ambiguity, and inertial imbalance with signal noise. The results validate that the proposed ASMC delivers a functional balance between robustness, control smoothness, and tracking accuracy alongside execution homogeneity for autonomous quadrotor UAV trajectory tracking in unsettled and unstable environments.
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
Contact-based industrial inspection requires aerial platforms to maintain stable interaction while rejecting disturbances. Underactuated aerial manipulators present control challenges due to the dynamic coupling between vehicle attitude and force generation. This paper proposes a robust control framework for an underactuated hexarotor equipped with a 1-DoF manipulator to perform sustained contact inspection. The architecture integrates integral-augmented Sliding Mode Control (SMC) for trajectory tracking with an admittance control law for force regulation. The contact force is mapped to a feedforward attitude term, while the 1-DoF arm actively compensates for the tilt to maintain surface alignment. Software-in-the-loop simulations demonstrate that the SMC-based approach achieves superior tracking and coupling rejection compared to traditional PID. Furthermore, the interaction strategy achieved precise force regulation with an RMSE of 0.12N and was able to stably exert up to 20N force, confirming the system's efficacy for stable, reliable contact-based inspection.
Tareq Aziz Hasan Alqutami, Yvan R. Pétillot, M. Dunnigan et al.· 0 citations
This paper improved trajectory tracking control strategy for robotic manipulators, combining Time Delay Control (TDC), Integral Sliding Mode (ISM), and the Super-Twisting Algorithm (TDC-IS-STA). The Time Delay Control framework provides a model-free aspect by estimation of unknown nonlinear dynamics and external disturbances using previously measured system data, while the integral sliding surface eliminates the reaching phase and ensures robustness from the initial instant of operation. To further mitigate the chattering phenomenon inherent in classical sliding mode control, the Super-Twisting Algorithm is incorporated to generate a continuous control signal while preserving finite-time convergence and robust performance. The proposed controller is validated through numerical simulations performed in MATLAB/Simulink on a parallel Delta robot model under multiple operating scenarios .Comparative study against control approaches demonstrate that the proposed TDC-IS-STA strategy achieves superior tracking accuracy, reduced chattering in the actuator torque signals .