Stable task space neuro controller for robot manipulators without velocity measurements

Abstract

In this work a stable task space neuro algorithm for set-point control of robot manipulators with uncertain parameters is proposed. A depart from current approaches is the fact that a Wavelet Neural Network with on-line real-time learning seeks to explicitly compensate both the unknown gravity terms and the mismatch between the true and the estimated lacobian matrix and the fact that it does not need velocity measurements. Linear position filtering is used to estimated the robot joint velocity in the control law and the properties of the Wavelet Neural Network are employed for avoiding velocity measurements in the learning rule. It is shown that all the closed loop signals are uniformly ultimately bounded. Experimental results in a two degrees of freedom robot are presented to evaluate the proposed controller. © Springer-Verlag Berlin Heidelberg 2005.