RBF network-based adaptive sliding mode control strategy for the tendon-sheath driven joint of a prosthetic hand

The complex in-hand manipulation puts forward higher requirements for the dexterity and joint control accuracy of the prosthetic hand. The tendon-sheath drive has important application potential in the fields of prosthetic hand to obtain higher dexterity. However, the existing control methods of tendon-sheath driven joint are mainly open-loop compensation based on friction model, which makes it difficult to achieve high-precision joint control.

The purpose of this work is to improve the position control accuracy of the tendon-sheath driven joint for the prosthetic hand.

The structure of the prosthetic hand is introduced, and the encoder and potentiometer are mounted on the driving motor and joint respectively. Then, the transfer function of the joint is established based on the dynamic model. The adaptive sliding mode control strategy based on RBF network is applied to realize the closed-loop feedback position control of the prosthetic hand joint. The stability of the system is demonstrated by Lyapunov theorem.

Under the condition of constant and variable sheath curvature, the effectiveness of the controller is demonstrated by simulation and joint motion experiments, respectively. The results show that the closed-loop control has better position tracking ability than the open-loop control, and the designed controller can reduce the tracking error more obviously than the traditional algorithm. The high-precision position control can be realized by designing the controller based on the joint angle feedback.

The research content has certain theoretical and practical significance for the development of joint high-precision control of tendon-sheath driven prosthetic hand. This is beneficial to the implementation of complex in-hand manipulation for prosthetic hand.