Adaptive H>inf<∞>/inf<Tracking Control Design via Neural Networks of a Constrained Robot System

摘要

In this paper, a nonlinear adaptive neural network tracking control with a guaranteed H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance is proposed for a constrained robot manipulator with plant uncertainties. The neural network is used to learn the unknown dynamics by an adaptive algorithm. Moreover, a force sensor is built to measure the forces and torques between the experimental robot UArm II end-effector and the environment. Finally, results obtained from the implementation of the proposed controller in the manipulator UArm II, under a constrained movement, are presented.