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Adaptive And Reconfigurable Flight Control, Yih Shiun Huang
Adaptive And Reconfigurable Flight Control, Yih Shiun Huang
Theses and Dissertations
An indirect adaptive and reconfigurable flight control system is developed. The three-module controller consists of: (1) a system identification module, (2) a parameter estimate smoother, and (3) a proportional and integral compensator for tracking control. Specifically: (1) The identification of a linear discrete-time control system's open-loop gain is addressed. The classical Kalman filter theory for linear control systems is extended and the control system's state and loop gain are jointly estimated on-line. Explicit formulae for the loop gain's estimate and estimation error covariance are derived. The estimate is unbiased and the predicted covariance is reliable. (2) An adaptive smoother is …
Using Gps As A Reference System To Hit A Moving Target, Daryl J. Burnette
Using Gps As A Reference System To Hit A Moving Target, Daryl J. Burnette
Theses and Dissertations
The Affordable Moving Surface Target Engagement (AMSTE) project attempts to develop affordable solutions to the precise moving target surface target engagement problem. Up to this point, most of the error analysis performed for the AMSTE project has been at the error variance level, generating root-sum-square (RSS) total errors from error budgets consisting of constant error variances. In reality, the level of error for both Global Positioning System (GPS) positioning and radar targeting systems is highly dependent upon the given situation (such as the distance between sensor and target, the altitude differences, etc.) This research generates a more comprehensive model of …
Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Theses and Dissertations
Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions. Our Kalman filters have a dynamics model based on a Box-Jenkins Auto-Regressive Moving Average (ARMA) model and a measure model with additive noise. The time-series prediction is based …