Reasoning About and Optimizing Distributed Parameter Physical Systems Using Influence Graphs

We develop the influence graph mechanism for reasoning about and optimizing decentralized controls for distributed parameter physical systems. Distributed parameter systems, such as air flow around an airplane wing, temperature over a semiconductor wafer, and noise from a photocopy machine, are common physical phenomena. The influence graph mechanism encodes the structural dependency information in a distributed parameter system and exploits the information to (1) alleviate redundant computation and (2) reduce communication and support cooperation among local control processes. Using the mechanism, we obtained a dramatic computational speed-up in optimizing control design for a distributed temperature field.

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