Software and Neural Architectures for Real-Time Adaptive Learning and Control

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9412430 Peterson This project will develop the software and neurobiological connections that are important to making progress in the areas of real-time learning and control. The research will focus on methods using the Pontryagin optimality and Hamilton-Jacob-Bellman (HJB) equation in conjunction with on-line ( or real-time) measurements of state data to build local models of control law. These techniques share the spirit of the adaptive-critic methodologies for construction of models of the control law, but are tightly bound to the premise of real-time problems. The focus here is on pertinent refinements and extensions of these techniques in three major arenas: theoretical analysis, locally scoped artificial neural architecture and object oriented software architecture design. General software code using C++ language will be developed to support these research efforts. The graphical interface will be designed using X windows with the Motif widget set. The software environment will then be tested on problem domains of interest, e.g. reduced order model of Large Space Structures (LSS) and flexible robotic arms.