Unsupervising adaption neural-network control

Unsupervising learning control systems based on neural networks are discussed. The tasks are carried out by two neural networks which act as the plant identifier and system controller, respectively. A novel learning algorithm that can adapt the controller´s control action by using information stores in the identifying network has been developed. This learning control system can learn without supervising to perform the dynamic control of a difficult-learning control problem such as the inverted pendulum. Robustness can be seen from its ability to adapt large parameter changes and from its high fault tolerance. Simulation results are encouraging