Research Planning Grants (RPG): Intelligent Robot Control

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9407363 Tang A Neural network's ability to utilize large amounts of sensory information, parallel processing, learning and generalization has made it an attractive candidate for intelligent system control. The objective of this research is to design and implement an intelligent neuro-controller for industrial robots. Five basic schemes have been used for robot control based on neural networks supervised control, direct inverse, neural adaptive, backpropagation of utility, and adaptive critics. While the first three methods are good for tracking control, a truly intelligent control system should consider the system as a whole and should include planning and performance optimization. Towards this end, the backpropagation of utility and adaptive critic schemes have the greatest potential because they maximize performance based on models or predictions of future utilization. This research will investigate the strengths and weakness of the backpropagation of utility and adaptive critic schemes for three robotics systems, 2-D planar robot, 3-D Stanford arm, and the 6-D Scorbot ER-III robot. These include simulation, evaluation, and ultimately the establishment of design guidelines for robot control. With the integration of these three systems, from planar motion to complex six-degree motion, this project will attempt to provide a complete and systematic study of neuro-control for robotics systems.