Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant Tendon-driven Structures of Musculoskeletal Humanoids

Kawaharazuka, Kento; Koga, Yuya; Tsuzuki, K.; Onitsuka, Moritaka; Asano, Yuki; Okada, Kei; Kawasaki, Koji; Inaba, Masayuki

doi:10.1109/iros45743.2020.9341510

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…In recent years, tendon-driven musculoskeletal systems (TDMSs) have attracted considerable attention owing to their superior robustness, better compliance and high precision (Li et al , 2021; Trendel et al , 2018; Asano et al , 2017; Yun et al , 2019). Compared with traditional robots, TDMSs may be safer when interacting with humans and the environment because they possess redundant muscles that provide robustness and flexibility under variable stiffness control (Kawaharazuka et al , 2020). Although such redundant muscles have some advantages, they present difficulties in establishing accurate models due to issues such as strong coupling, model uncertainty and environmental interference (Wu et al , 2021; Thelen, 2003).…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuromuscular control of a simplified muscle tendon-driven musculoskeletal system

Fan,

Wu,

Yuan

2023

RIA

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Purpose This study aims to improve the muscle model control performance of a tendon-driven musculoskeletal system (TDMS) to overcome disadvantages such as multisegmentation and strong coupling. An adaptive network controller (ANC) with a disturbance observer is established to reduce the modeling error of the musculoskeletal model and improve its antidisturbance ability. Design/methodology/approach In contrast to other control technologies adopted for musculoskeletal humanoids, which use geometric relationships and antagonist inhibition control, this study develops a method comprising of three parts. (1) First, a simplified musculoskeletal model is constructed based on the Taylor expansion, mean value theorem and Lagrange–d’Alembert principle to complete the decoupling of the muscle model. (2) Next, for this simplified musculoskeletal model, an adaptive neuromuscular controller is designed to acquire the muscle-activation signal and realize stable tracking of the endpoint of the muscle-driven robot relative to the desired trajectory in the TDMS. For the ANC, an adaptive neural network controller with a disturbance observer is used to approximate dynamical uncertainties. (3) Using the Lyapunov method, uniform boundedness of the signals in the closed-loop system is proved. In addition, a tracking experiment is performed to validate the effectiveness of the adaptive neuromuscular controller. Findings The experimental results reveal that compared with other control technologies, the proposed design techniques can effectively improve control accuracy. Moreover, the proposed controller does not require extensive considerations of the geometric and antagonistic inhibition relationships, and it demonstrates anti-interference ability. Originality/value Musculoskeletal robots with humanoid structures have attracted considerable attention from numerous researchers owing to their potential to avoid danger for humans and the environment. The controller based on bio-muscle models has shown great performance in coordinating the redundant internal forces of TDMS. Therefore, adaptive controllers with disturbance observers are designed to improve the immunity of the system and thus directly regulate the internal forces between the bio-muscle models.

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Section: Introductionmentioning

confidence: 99%

Adaptive neuromuscular control of a simplified muscle tendon-driven musculoskeletal system

Fan,

Wu,

Yuan

2023

RIA

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“…In biology, due to the complexity of bones, the redundancy of muscle distribution, the nonlinearity of muscles, and the uniqueness of muscle control of neural mechanisms, the human arm can dexterously, flexibly, and safely complete complex manipulation tasks [1][2][3]. The operation of musculoskeletal robots with humanoid structures can be considered to avoid danger to humans and the environment, possibly because they have redundant muscles that provide robustness and flexibility during variable stiffness control [4][5][6]. In recent years, the structural design, simulation modeling, and muscle control of anthropomorphic robots have been widely studied by scholars [1,7,8].…”

Section: Introductionmentioning

confidence: 99%

A feedforward compensation approach for cable-driven musculoskeletal systems

Fan

Yuan

et al. 2022

Robotica

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This paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

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Sliding Muscle Surface Control of the Muscle-Driven Musculoskeletal System

Fan

Yuan

et al. 2022

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)

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Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant Tendon-driven Structures of Musculoskeletal Humanoids

Cited by 4 publications

References 19 publications

Adaptive neuromuscular control of a simplified muscle tendon-driven musculoskeletal system

Adaptive neuromuscular control of a simplified muscle tendon-driven musculoskeletal system

A feedforward compensation approach for cable-driven musculoskeletal systems

Sliding Muscle Surface Control of the Muscle-Driven Musculoskeletal System

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