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Parameter-Learning Active Disturbance Rejection Controller for a Novel Variable Stiffness Gripper
Journal article   Peer reviewed

Parameter-Learning Active Disturbance Rejection Controller for a Novel Variable Stiffness Gripper

Ziqing Yu, Jiaming Fu, Fan Zhang, Jinfeng Chen and Dongming Gan
Journal of mechanisms and robotics, Vol.18(6), JMR-26-1019
04/06/2026
DOI: https://doi.org/10.1115/1.4071600
Web of Science ID: WOS:001756677200001

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Abstract

variable-stiffness gripper active disturbance rejection control adaptive parameter learning extended state observer force control collaborative robots and human-robot interaction grasping, fixturing, and multifinger hands kinematics, dynamics, and control of mechanical systems Biomechanics Control Systems Industrial Robotics
The growing demand for flexible robotic grasping in industry calls for adaptable solutions capable of handling diverse objects across stiffness regimes. We present a novel variable stiffness gripper with a parallel-guided beam and sliding-block mechanism, enabling continuous stiffness modulation (0.10–2.00 N/mm) without component replacement. However, transmission nonlinearities, friction, stiffness-dependent effects, and, in particular, frequency drift arising from stiffness variations significantly hinder precise force control. To overcome these challenges, we propose a Parameter-Learning Active Disturbance Rejection Control (PL-ADRC) framework, integrating online adaptive parameter identification with a model-based extended state observer for real-time estimation and rejection of disturbances arising from unmodeled dynamics and parametric uncertainties. Experimental results demonstrate the superior performance of PL-ADRC: PL-ADRC reduces the band settling time by 0.13 s compared to Model-Free Active Disturbance Rejection Control (MF-ADRC), limits the steady-state force error to 0.01 N, and exhibits robust adaptability in stiffness modes. It outperforms model-based and model-free methods in fragile-object manipulation (egg grasping without fracture) and high-noise scenarios, achieving faster stabilization and reduced overshoot. This framework bridges precision and adaptability, advancing safe human-robot collaboration in dynamic industrial tasks.

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