Evolutionary Optimized Neural Network (EONN) Based Motion Control of Manipulator

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Neha Kapoor 1,* Jyoti Ohri 1

1. National Institute of Technology, Kurukshetra, Haryana, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2014.12.02

Received: 27 Feb. 2014 / Revised: 11 Jun. 2014 / Accepted: 6 Sep. 2014 / Published: 8 Nov. 2014

Index Terms

Radial Bias Neural Network (RBNN), Particle Swarm Optimization (PSO), Evolutionary Neural Network (ENN), Hybrid Intelligent Controller, Non-Linear Control Systems


In this paper, an Evolutionary Optimized Neural Network (EONN) based control scheme is proposed. This control scheme is based on the fact that optimizing values of a few parameters of neural network can enhance its control performance. Radial Biased Neural Network (RBNN) is chosen here and PSO, one of the most emerging global optimizing techniques, is used to optimize the parameters of a RBNN. From hidden to output layer RBNN uses Gaussian function for mapping. Spread factor (s) of this intelligent RBNN is then optimized by a modified PSO to improvise its performance. The proposed controller has been verified by implementing it for position control of a robotic manipulator. For comparison purpose, proposed scheme has been verified with RBNN and the classical PD controller. MATLAB environment has been chosen for simulation study carried out. Robustness of the proposed controller has been checked by applying it to the manipulator for three different paths.

Cite This Paper

Neha Kapoor, Jyoti Ohri, "Evolutionary Optimized Neural Network (EONN) Based Motion Control of Manipulator", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.12, pp.10-16, 2014. DOI:10.5815/ijisa.2014.12.02


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