Jun 2026· Italian National Conference on Sensors· Vol 26, pp. 4115· 0 citations· 55 references
Medicine
TL;DR
An adaptive variable damping admittance control framework driven by multi-dimensional force sensor feedback is constructed to eliminate steady-state tracking errors, integrated with a nonlinear adaptive damping law that sensitively responds to real-time force sensor measurements.
Abstract
Achieving safe physical interaction on the human back is challenging due to respiratory rhythms, complex topography, and varying tissue stiffness. To enable compliant force tracking within commercial closed position-control robot architectures, this paper presents an adaptive variable damping admittance control framework driven by multi-dimensional force sensor feedback. A stiffness-free admittance model is constructed to eliminate steady-state tracking errors, integrated with a nonlinear adaptive damping law that sensitively responds to real-time force sensor measurements. This mechanism rapidly dissipates dynamic impact energy during contacts while maintaining low impedance during steady state. Validated via a high-fidelity MATLAB R2024b-CoppeliaSim co-simulation platform replicating Traditional Chinese Medicine (TCM) manipulations, the proposed sensor-driven strategy significantly improves force tracking fidelity over traditional fixed-parameter control. Quantitative results demonstrate that across all complex therapeutic waveforms, the root mean square error (RMSE) remains below 0.42 N, the mean absolute error (MAE) is within 0.32 N, and the squared correlation coefficient (r2) exceeds 0.97. These findings confirm the high efficiency and clinical potential of the proposed framework.
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
Home-based rehabilitation exoskeletons often suffer from control instability due to low-cost force sensors. This paper presents a robust, sensorless Composite Variable Impedance Control architecture that separates trajectory tracking (virtual stiffness K) from active assistance (adaptive feedforward torque τassist). By eliminating high-frequency force feedback, the system ensures intrinsic stability. Experiments on the CURE platform demonstrate independent modulation of compliance (RMSE 2.64° to 15.90°) and effective assistance during simulated weakness, reducing tracking RMSE from 13.77° to 3.12°. Results show τassist contributes 59.6% of total torque, enabling "High-Assistance, High-Compliance" interaction without reactive stiffening. This provides a stable execution layer for advanced, bio-signal-driven "Assist-as-Needed" (AAN) therapies.
Jun Leng, Pengcheng Li, Hanze Wang et al.· 2026 IEEE International Conf...· 0 citations
Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control because of their highly nonlinear, distributed dynamics. We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model and a linear residual dynamics model identified using Extended Dynamic Mode Decomposition with control (EDMDc). This representation enables feedforward compensation, task-space feedback control, and local closed-loop stability analysis through an augmented linear model. Experiments achieve approximately 1 mm root mean square error (RMSE) during low-speed (approximately 10 mm/s) trajectory tracking and below 10 mm RMSE at higher speeds (approximately 100 mm/s). The framework further achieves stable tracking of highly dynamic user-generated references with peak accelerations exceeding 25 m/s^2 while simultaneously performing real-time obstacle avoidance. Finally, the proposed stability analysis is experimentally validated by accurately predicting stable, marginal, and unstable operating regimes. These results demonstrate that structured, control-oriented learning provides an accurate and practical framework for soft actuator control.
Mechanical compliance is essential for stable and safe human–machine interaction in force-rendering interfaces. Instability is particularly problematic when a low-inertia, highly backdrivable actuator with intrinsic delay interacts with a human or compliant environment. Pneumatic actuators offer a high power-to-weight ratio, low moving mass, and inherent backdrivability; however, their pressure dynamics introduce delays that can destabilize low-damping, compliant contacts. This study examines delay-induced oscillations during virtual-spring rendering using a pneumatic–electromagnetic fusion hybrid linear actuator (FHLA) with distributed macro–mini actuation. We demonstrate that, even under light-contact conditions, cross-coupling between the rendered virtual spring and physical compliance forms a feedback loop susceptible to oscillation in delayed, low-impedance systems. To enable this actuation in the integrated FHLA—where only the combined output force is measurable—the pneumatic force component is estimated from pressure measurements. Experiments with 10mm displacement and 2–4N/mm virtual stiffness under light-contact conditions demonstrate a reduction of more than 99% in oscillation amplitude compared with pneumatic-only actuation. These results highlight enhanced force-rendering behavior through delay-compensated hybrid actuation.
Trajectory-tracking metrics such as root-mean-square error (RMSE), overshoot, and settling time are widely used to evaluate control performance in mechatronic systems. However, these measures describe output tracking alone and do not account for the actuator effort required to produce the observed motion. This limitation becomes more pronounced in nonlinear systems, where stiffness and dissipation depend on the system state. This paper examines how these effects influence actuator energy under similar tracking conditions. An energy-aware evaluation framework is introduced that combines tracking error with cumulative actuator energy and enables comparison between systems with approximately matched performance. A simple index (EAPI) is used to capture both aspects in a single measure. Simulation results for linear and nonlinear systems under proportional-derivative control show that comparable tracking accuracy can correspond to significantly different actuator energy. The nonlinear system consistently requires more energy across matched operating points, reflecting the influence of nonlinear stiffness and friction. These results suggest that trajectory-based metrics alone may not fully capture differences in system effort, and that including energy provides a more informative basis for performance evaluation.
Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging due to strong nonlinearities, distributed deformation, and uncertainty in their dynamics. This paper presents a real-time dynamic-model-based task-space feedback and estimation framework based on a non-minimal coordinate discrete elastic rod model formulated in absolute coordinates with holonomic constraints. The resulting structure preserves distributed mechanics while maintaining computational efficiency through sparse system matrices, enabling real-time control with up to 10 discretized rods. A quasi-static feedforward inverse model is combined with a task-space PI controller and a dynamic observer that fuses measurement residuals as virtual forces, enabling full-state estimation from sparse sensing. The approach is experimentally validated on three planar pneumatic soft actuators with varying geometries. Across five tasks, including drawing the digits 0-9 across the workspace (3-18 mm/s tip speed), tracking periodic motion (up to 37 cm/s), cross-platform generalization, reduced sensing conditions, and real-time user-defined references, our method achieves 1.5-2.3 mm root mean square error (RMSE) for precision motions and 5.5-12.4 mm RMSE at 1-2 Hz. Results demonstrate that structured, non-minimal dynamic models can enable real-time, high-precision, moderate-bandwidth task-space control of planar soft pneumatic actuators in free space.
Nithin S. Kumar, Joshua Gaston, D. Rucker et al.· 0 citations