Neural-dynamics-enabled Jacobian inversion for model-based kinematic control of multi-section continuum manipulators

Continuum manipulators are a new generation of robotic systems that possess infinite number of degrees of freedom associated with inherent compliance, unlike traditional robotic manipulators which consist of a finite number of rigid links. Because of this characteristic, controlling continuum manipulators is more complicated and difficult based on only traditional control theory. Soft computing techniques are solid alternative for improving the control performance of such kinds of robots. In this paper, we employ two types of neural dynamic approaches, i.e., gradient neural dynamics and zeroing neural dynamics, to solve the real-time Jacobian matrix pseudo-inversion problem, thereby achieving model-based kinematic control of multi-section continuum manipulators. Different kinds of neural dynamic models are investigated and their performances in terms of tracking accuracy are shown with and without noise disturbances. Simulation validations with a two-section and a three-section continuum manipulator demonstrate the feasibility and robustness of the proposed models. (C) 2021 Elsevier B.V. All rights reserved.