Presidential Young Investigator Award: Feedback Structures for Supervisory Control

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The objective of this research is to develop methods for design and evaluation of sensory feedback loops for supervisory decision and control in flexible manufacturing cells. In these applications, processes evolving in continuous space and time are modeled and controlled as asynchronous discrete events. The task to be automated is decomposed into basic discrete operations with alternative sequencing and precedence relations among the subtasks. To analyze alternative feedback structures, information-theoretic measures are being explored as a means for representing the requirements of the operations and the information and decision spaces of the system components. Information-based models will characterize sensor resolution, actuator accuracy, and the effects of alternative internal models for information processing and decision algorithms. Techniques from optimal stochastic control are being investigated for potential application to decision problems in flexible cells with real-time resource allocation. As a tool for evaluating alternative feedback structures and decision algorithms, a discrete-event simulation model of the automated processes will be implemented with explicit representation of the sensory feedback loops.