SBIR Phase II: Neural Networks, Youla Parameterization and Reconfigurable Control Systems

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9321264 Saeks This project will develop a hybrid frequency domain/neurocontrol algorithm by using neural network techniques to adjust the "Youla design parameter." This yields a controller which is guaranteed to be stable and can implement any of and /or distrubance rejection, pole placement, robust tracking and/or disturbance rejection, simultaneous design, initial value design, etc. The adaptivity and robustness intrinsic to neurocontrol is integerated into the frequency domain controller. Powerful (ontogenic) neural network training techniques may be brought to bare on the control system design problem allowing one to design a controller without a prior restrictions on its order. A reconfigurable control architecture is developed where the controller is trained to deal with multiple scenarios; alternative trajectories for a flight controller, varying loads on a robot arm, alternative current waveshapes in a power supply, etc; generaliziang to new scencarios byond the training set where necessary. The approach is directly applicable to linear multivariate systems and extendable, via the vairous generalizations of the Youla parameterization to the nonlinear case. *** v s t disturbanceintegratedetc.generalizingscenariosbeyondextendiblevariousB C D E F G I N O P Q R S T U V W X Y Z \ ^ _ ` a b g h S j k l m n r s t u v w x y z { } ~ 9321264 Saeks This project will develop a hybrid frequency domain/neurocontrol algorithm by using neural net work techniques to . = H R V b k q { ) . $ ( F / . $ . 1 Times Symbol " Helvetica 5 Courier New E O Y ` = H N R 4 V C b U k q { , " h j2 % 9 E ( S R:\WW20USER\ABSTRACT.DOT abstract Alicia E. Harris Alicia E. Harris