NEURAL-NETWORK DESIGN WITH GENETIC LEARNING FOR CONTROL OF A SINGLE LINK FLEXIBLE MANIPULATOR

The application of neural networks for active control of lightly dampe d systems is considered in this article. The training process of the n eural-network controller is based on the genetic learning algorithm. T he schemes imitates nature's cleansing phenomena of natural selection and survival of the fittest to generate individual controllers with th e best fitness values. It essentially incorporates an exhaustive searc h in the weight-space governed by the rituals of crossover and mutatio n to seek the optimum neural-network weights to satisfy certain perfor mance criteria. Several appropriate modifications of the classical gen etic 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 sing le-link flexible arm. Sim ulation studies are presented to validate the effectiveness of the adv ocated algorithms.