A design of prefilter type compensatory controller for robot manipulator using modified chaotic neural networks
Sang-Hee Kim, Su-Dong Hong, Chang-Hyun Chai, Won-Woo-Park · 2003
This paper presents a prefilter type compensatory controller for robotic manipulator using modified chaotic neural networks (MCNNs). The structure of the proposed prefilter type compensatory controller consists of two MCNNs that compensate position and velocity error of the proportional-derivative (PD) controller. The simulation results show the excellent performance on convergence and fast learning comparing with adaptive recurrent neural networks (RNNs) controller that is composed of a PD controller and RNNs in parallel.