CAREER: Sensor-Based Adaptive Control and Prognosis of Complex Distributed Systems

University Of Texas At San Antonio, San Antonio TX

Investigators

Abstract

9985739 Sarangapani New developments in telecommunication systems, wireless networks, manufacturing, and elsewhere place severe demands on the design of advanced discrete-time adaptive controllers. Therefore, discrete-time version of controller techniques is of importance since all the controller implementations are carried out on a digital computer. Further, the performance requirements for complex nonlinear systems including industrial, DoD are becoming more stringent due to rapid advances in sensors, hardware, and more importantly the regulations imposed on these systems due to environmental concerns. The result is a complex large-scale distributed modern day system. Adaptive and neural network (AN) control tools and sensor technologies hold out promise of improved learning under uncertainty, vibration suppression, precise motion control and micro object manipulation, microsensor development, deadzone compensation, diagnostics/prognostics and more. The goal of this project is to provide a next generation controller, which should be able to interface to a wide variety of sensors, allowing reconfiguration and rapid prototyping capabilities, has guaranteed performance, and is supported by a rigorous and repeatable design and mathematical framework, to include: Development and Implementation of Advanced Real-Time Feedback Controllers for Machine Level Operations. Development and Implementation of Microsensor-based Assembly/Control of MEMS devices and Systems. Development and Implementation of Sensor-based Adaptive Controllers for Diagnostic/Prognostic Applications. Development and Implementation of Real-Time Adaptive Controllers for Communication Networks. ***