Neural network designs with genetic learning for control of a single link flexible manipulator

The application of neural networks for active control of lightly damped systems is considered. The training process of the neural-network controller is based on the genetic learning algorithm. The scheme imitates nature´s cleansing phenomena of natural selection and survival of the fittest to generate individual controllers with the best fitness values. It essentially incorporates an exhaustive search in the weight-space governed by the rituals of crossover and mutation to seek the optimum neural-network weights to satisfy certain performance criteria. Several appropriate modifications of the classical genetic algorithm for neural-network control purposes are discussed. The genetic-trained neural-network controller is applied for tip position tracking and vibration suppression of a single-link flexible arm. Simulation studies are presented to validate the effectiveness of the advocated algorithms.