ACO based Adaptive RBFN Control for Robot Manipulators

TL;DR

This paper introduces an adaptive control method for robot manipulators using an Ant Colony Optimization enhanced Radial Basis Function Network to improve inverse kinematics approximation accuracy and reduce model complexity.

Contribution

It presents a novel ACO-based training approach for RBF neural networks that enhances inverse kinematics solutions for robot manipulators.

Findings

Higher accuracy of RBF neural networks with ACO optimization.

Improved fitting capability of the neural network model.

Reduced number of hidden layer neurons.

Abstract

This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.