Multiple-Input Multiple-Output Robust Control for Independent-Setup Variable Stiffness Actuator

This paper presents the development of a multiple-input multiple-output (MIMO) coupling controller for the independent-setup variable stiffness actuator (VSA). The dynamic model of the independent-setup VSA is typically considered decoupled due to the slight coupling between the joint and stiffness actuation. However, the model can be formulated in a coupled manner by taking into account the various uncertainties arising from physical stiffness variations. To describe the coupling, we present a linear parameter-varying (LPV) model. In this LPV model, the time-varying parameters are defined to capture the interaction between the two actuation components. Then, we introduce a robust MIMO coupling controller designed to ensure reliable operation. The controller is synthesized using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> mixed-sensitivity loop-shaping and LPV technique. This synthesis ensures the quadratic stability of the closed-loop system in the presence of time-varying parameters, along with effective tracking of joint and stiffness trajectories. Additionally, the generalized extended state observer is employed as a design paradigm to verify whether the proposed baseline LPV controller can be integrated with other advanced control strategies previously explored in VSA control. The effectiveness of all control schemes is experimentally validated on an updated mechanical-rotary variable impedance actuator prototype.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-08-11
DOI
https://doi.org/10.1109/lra.2025.3597858
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Multiple-Input Multiple-Output Robust Control for Independent-Setup Variable Stiffness Actuator

Lin Liu, Yongping Pan, Xinyu Wu, Chunjie Chen et al.
IEEE Robotics and Automation Letters
Iterative Learning Control Systems
article

Multiple-Input Multiple-Output Robust Control for Independent-Setup Variable Stiffness Actuator

Lin Liu, Yongping Pan, Xinyu Wu, Chunjie Chen, Jianqing Peng, Yang Wang, Geok Soon Hong
article en

Abstract

This paper presents the development of a multiple-input multiple-output (MIMO) coupling controller for the independent-setup variable stiffness actuator (VSA). The dynamic model of the independent-setup VSA is typically considered decoupled due to the slight coupling between the joint and stiffness actuation. However, the model can be formulated in a coupled manner by taking into account the various uncertainties arising from physical stiffness variations. To describe the coupling, we present a linear parameter-varying (LPV) model. In this LPV model, the time-varying parameters are defined to capture the interaction between the two actuation components. Then, we introduce a robust MIMO coupling controller designed to ensure reliable operation. The controller is synthesized using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> mixed-sensitivity loop-shaping and LPV technique. This synthesis ensures the quadratic stability of the closed-loop system in the presence of time-varying parameters, along with effective tracking of joint and stiffness trajectories. Additionally, the generalized extended state observer is employed as a design paradigm to verify whether the proposed baseline LPV controller can be integrated with other advanced control strategies previously explored in VSA control. The effectiveness of all control schemes is experimentally validated on an updated mechanical-rotary variable impedance actuator prototype.

IEEE Robotics and Automation LettersVol. 10(10)
Sun Yat-sen University (CN), Chinese Academy of Sciences (CN), ShanghaiTech University (CN), Peng Cheng Laboratory (CN), Shenzhen Institutes of Advanced Technology (CN), Southeast University (CN)
Affordable and clean energy
Openalex Percentile: Top 76%
Iterative Learning Control Systems
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