Adaptive RBFNN control of robot manipulators with finite time convergence
In this paper, the position tracking control with finite time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.
IECON 2016 42nd Annual Conference of the IEEE Industrial Electronics Society