Evolutionary Optimized Neural Network (EONN) Based Motion Control of Manipulator
Автор: Neha Kapoor, Jyoti Ohri
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 12 vol.6, 2014 года.
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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.
Radial Bias Neural Network (RBNN), Particle Swarm Optimization (PSO), Evolutionary Neural Network (ENN), Hybrid Intelligent Controller, Non-Linear Control Systems
Короткий адрес: https://sciup.org/15010632
IDR: 15010632
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