Adaptive finite‐time tracking control for robotic manipulators with funnel boundary
Jialei Bao, Huanqing Wang, Peter Liu
Abstract
Summary The finite‐time tracking control problem with the output‐constraint property of robotic manipulators subjected to system uncertainties is addressed. Specifically, the radial basis function neural network is employed to compensate for system uncertainties. The finite‐time stability theorem is used for the backstepping design process, by which the limit of the settling time is set. A funnel boundary is used to limit the output overshoot. The proposed controller guarantees that all the signals are semi‐globally practically finite‐time bounded, while the tracking errors are enveloped by the funnel boundary. The performance of the proposed control method is illustrated by a numerical simulation of a 3‐DOF manipulator. It is shown that the tracking errors are bounded by prescribed funnel boundaries. In the meantime, the manipulator is stabilized within a finite period of time.