Neural network design 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 in this article. The training process of the neural-network controller is based on the genetic learning algorithm. The schemes imitates nature's cleansing phenomena of natural selection and survival of the fittest to generate individual controllers withe 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.