Sliding Muscle Surface Control of the Muscle-Driven Musculoskeletal System

Fan, Yerui; Yuan, Jianbo; Wu, Yaxiong; Gan, Shuai

doi:10.1109/icarce55724.2022.10046635

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Tahara proposed a proportional controller based on task space error feedback and demonstrated the stability through passivity condition, which provided a more feasible method in hardware system (Tahara et al , 2009). Then, some adaptive control (Dong et al , 2013; Fan et al , 2023a), slide-mode control (Fan et al , 2023b) and equilibrium-point control (Wu et al , 2023) were proposed to improve the accuracy and robustness. However, the performance of these methods mentioned above relies on an accurate and explicit model, which is challenging to establish due to the sophisticated structure and high-coupled, nonlinear dynamics of musculoskeletal systems.…”

Section: Introductionmentioning

confidence: 99%

Generalizable and precise control based on equilibrium-point hypothesis for musculoskeletal robotic system

Wu,

Chen,

Qiao

2024

RIA

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Purpose The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots. Design/methodology/approach In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics. Findings First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results. Originality/value Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

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