Accurate Automation will investigate a new control architecture in which a neural network is used to set the coefficients of the Youla parameter for the controller. By so doing one achieves all of the "design by learning" benefits associated with neural control while simultaneously guaranteeing that the resultant system will be stable. Moreover, the proposed network automatically updates the Youla parameter whenever a new reference input or performance criteria is specified. Specific research objectives include the: - development of neural network architectures and training methods applicable to the proposed system, - formulation of methods for updating the network training each time this system is operated, - implementation the controller as an auto- reconfigurable system, and - application of the proposed neural controller to flight control systems and robotics.
