Radial basis function neural network control for parallel spatial robot

Abstract

The derivation of motion equations of constrained spatial multibody system is an important problem of dynamics and control of parallel robots. The paper firstly presents an overview of the calculating the torque of the driving stages of the parallel robots using Kronecker product. The main content of this paper is to derive the inverse dynamics controllers based on the radial basis function (RBF) neural network control law for parallel robot manipulators. Finally, numerical simulation of the inverse dynamics controller for a 3-RRR delta robot manipulator is presented as an illustrative example.

Mathematical modeling and kinematic analysis of 5 degrees of freedom serial link manipulator for online real-time pick and place applications

“…Gaeid et al [3] used Denavit-Hartenberg (DH) parameters and matrix transformation method to perform kinematic modeling of 6 DOF manipulator whereas Sutyasadi and Wicaksono [12] used hybrid controller of iterative learning and H∞ controller to control the robot joint for trajectory tracking applications. Quang et al [13] used radial basis function to derive the inverse dynamics of delta manipulator and Mashhadany [14] used adaptive neuro fuzzy inference system (ANFIS) controller and fractional order proportional, integral, derivative (FOPID) controller to obtain optimal trajectory for PUMA 560 manipulator. Dewi et al [15] implemented fruit sorting robot for packaging industry using hue saturation value (HSV) and image processing techniques.…”