Control of a robot interacting with an uncertain viscoelastic environment with adjustable force bounds

This paper focuses on developing a neural network (NN) based force limiting controller for a robot interacting with an uncertain Hunt-Crossley viscoelastic environment. The proposed controller consists of a bounded NN term and saturated feedback which limits the control force to a known bound, which can be changed by adjusting the feedback gains. The Lyapunov-based controller, dependent only on position and velocity measurements, is shown to guarantee global uniformly ultimately bounded (GUUB) stability of the system despite uncertainties in the robot and the viscoelastic environment.