Adaptive Control, Learning, and Cost-Effective Sensor Systems for Robotics or Advanced Automation Systems.

Abstract

The hypothesis of this grant is that the non-deterministic class of problems is amenable to the techniques and methodologies of control system engineering a discipline which provides a tractable approach for addressing the issues for intelligent systems with cost effectiveness and performance optimality. The objectives of this grant are: (a) To explore the use of adaptive-learning control systems to complex processes or engineering-based expert systems to an inspecton type task. Both types of tasks which are usually performed by humans involve modeling and control or automatic data interpretation in the presence of noisy data with incomplete and time-varying models. (b) To determine the relevance of these approaches to research on intelligent robotic systems. (c) To determine a focus for testing these theories both analytically and experimentally if possible.