State, Parameter, and Input Estimation through MMSE Estimation for Structural Systems
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Author(s)
Lander, Peter S.
Advisor(s)
Dodson, Jacob
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Abstract
The research areas of bridge weigh-in-motion and high-rate structural health monitoring seek to use sensor data to gain real-time insights into monitored structures and their operating conditions. This thesis contributes to both research areas by applying minimum-mean-square error estimation algorithms. Specifically, bridge weigh-in-motion estimates the weights of vehicles as they cross a bridge by measuring the bridge’s dynamic response. This thesis details a full-scale experimental validation of the recently developed finite input covariance estimator for bridge weigh-in-motion in tandem with the development of a low-cost autonomous bridge weigh-in-motion system. Experimental results using acceleration and strain measurements demonstrated that the finite input covariance estimator achieves axle weight estimates with less than 10% error. When combined with the extended Kalman filter, the same finite input covariance estimator can be applied to nonlinear systems. High-rate structural health monitoring seeks to track the state of structures experiencing nonlinearities while being subjected to high-rate events, like impacts. This research presents the development and application of the extended version of the finite input covariance estimator towards performing joint state-input-parameter estimation. The proposed estimator is validated through simulations and experiments based on a nonlinear high-rate testbed. The estimator is found to be capable of performing accurate joint state-input-parameter estimation in the presence of impacts and high-rate parameter changes. Additionally, the proposed estimator maintains the stability of the original finite input covariance estimator in scenarios where only acceleration measurements are available.
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Date
2022-06-22
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Text
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Dissertation