Neuro-evolutionary based controller design for linear and non-linear systems

In the present work a Neuro-Evolution based approach has been used to train a neural network for control of some sample systems. This method makes use of Genetic algorithm, here it is generating a population of neural networks and introduces mutation for producing better off-springs for the next generation. The approach is kind of black box optimization and do not require any back propagation for training. It makes use of fitness function to evaluate performance of off-springs, this fitness function is based on a novel reward function which allows for quick and smooth settling of the sample system towards set point. In order to address dynamics of the system's time sequenced error has been taken as exogenous input for the neural network. The method has been tested on a linear first order system and a system having non linearity.