Search results

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.

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.

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.

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.

This study aims to propose a novel lightweight tendon-driven musculoskeletal arm (LTDM-arm) robot with a flexible series–parallel mixed skeletal joint structure and modularized artificial muscle system (MAMS). The proposed LTDM-arm exhibits human-like flexibility, safety and operational accuracy. In addition, to improve the safety and stability of the LTDM-arm, a control method is proposed to solve local artificial muscle overload accidents.

The proposed LTDM-arm comprises seven degrees of freedom skeletons, 15 MAMSs and various sensor systems (joint sensing, muscle tension sensing, visual sensing, etc.). It retains the morphology of a human skeleton (humerus, ulna and radius) and a simplified muscle configuration. This study proposes an input saturation control with full-state constraints to reduce local artificial muscle overload accidents caused by redundant muscle tension calculations.

3D circular trajectory experiments were conducted to verify the stability of the control method and the flexibility of the LTDM-arm. The results showed that the average error of the muscle length was approximately 0.35 mm (0.38%), which indicates that the proposed control scheme can make the output follow the target trajectory while ensuring constraint satisfaction.

The human arm is capable of performing compliant operations rapidly, flexibly and robustly in unstructured environments. Existing musculoskeletal arm robots lack simulations of the full morphology of the human arm and are insufficient in dexterity. However, the flexibility and safety features of the proposed LTDM-arm were consistent with that of the human arm. Therefore, this study offers a new approach for investigating the advantages of the musculoskeletal system and the concepts of muscle control.