Tip tracking of a rigid-flexible manipulator based on deflection estimation using neural network observer: A backstepping approach

In this study, a backstepping design scheme is proposed for the tip tracking control problem of a rigid-flexible link manipulator. The proposed controller stabilizes the robot manipulator while driving the trajectory tracking error and the tip-deflection asymptotically to zero. As a case study, the needle insertion problem is considered where the manipulator-needle system is modeled as a rigid-flexible link system. Needle deflection and tissue deformation are major problems for accurate needle insertion. A neural network based observer is introduced to estimate the flexible modes of the flexible-link. Simulation results are given to illustrate the efficient performance of the proposed control approach.