Multi-Rate Data Fusion Based Kalman Filtering with Unknown Input for Online Estimation of Dynamic Displacements

Lei, Ying; Luo, Sujuan; Su, Hailin

doi:10.1061/9780784481899.091

Cited by 1 publication

(1 citation statement)

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“…When communication is sparse or nonexistent between nodes, the centralized control scheme performs poorly. To compensate for slow data transmission rates and missed data, decentralized control for attenuation of undesired structural response has been shown to be comparable to fully centralized control . Efforts have also been made to address limited power during wireless structural control and health monitoring by adding energy harvesting and power management hardware to wireless units in the network …”

Section: Introductionmentioning

confidence: 99%

Wireless structural control using multi‐step TDMA communication patterning bandwidth allocation

Winter

Swartz

2017

Struct Control Health Monit

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As the number of sensors in a control network grows, it becomes increasingly difficult to transmit all sensor data during a single control step over the fixed wireless bandwidth. Because control force calculations rely on accurate state measurements or estimates, the use of staggered data communication may become necessary. It is not uncommon that a single sensor may be the only measurement source for an important section of a structure. This paper presents a means of selecting and evaluating different communication group sizes and wireless unit combinations for staggered communication that still provide information for highly accurate state estimates. Within each wireless unit, multiple estimator gains are stored on-board to perform state-estimation calculations using staggered data that may be received from different combinations of units. It is found that, in staggered communication schemes, state estimation and control performance are affected by the network topology used at each time step with some sensor combinations providing more useful information than others. Sensor placement theory is used to form sensor groups that provide consistently high-quality output information to the network over multiple time steps, so as to strategically report data from all sensors.

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Section: Introductionmentioning

confidence: 99%