Optimization Based Partitioning Selection for Improved Contaminant Detection Performance
Date
2018Author
Kyriacou, AlexisTimotheou, Stelios
Reppa, Vasso
Boem, Francesca
Panayiotou, Christos
Polycarpou, Marios
Parisini, Thomas
Source
2018 IEEE Conference on Decision and Control (CDC)Pages
5568-5573Google Scholar check
Metadata
Show full item recordAbstract
Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance.