AN EFFICIENT NEURO-GENETIC TECHNIQUE FOR NONLINEAR SYSTEM CONTROL

للكاتبين :

A.Y. Haikal, M.S.M. KSASY, S.F. SARAYA, F.F. AREED

Computers & Systems Dept., Faculty of Engineering, Mansoura University

ABSTRACT:

This work is concerned with studying an effective evolutionary algorithm based on neuron level

beside blueprint level search algorithm. This algorithm maintains diversity in the population due to

the evolution of partial solutions (neurons) instead of full solutions (networks). Each neuron

chromosome is composed of the incoming & outgoing connections & weights of the hidden-layer

neurons. Algorithm was investigated on an inverted pendulum system which is a common classical

control problem. It is a suitable process to test prototype controllers due to its high non-linearities and

lack of stability. The developed controller was proved to be both robust and of high performance for

controlling highly nonlinear and unstable plant. Also, after the investigation of the developed

controller’s ability to generalize, it displayed a high level of robustness to parameter uncertainty in

the model.

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