Open Access. Powered by Scholars. Published by Universities.®

Controls and Control Theory Commons

Open Access. Powered by Scholars. Published by Universities.®

Aerospace Engineering

PDF

Theses and Dissertations

Kalman filtering

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Controls and Control Theory

Model-Based Control Using Model And Mechanization Fusion Techniques For Image-Aided Navigation, Constance D. Hendrix Mar 2009

Model-Based Control Using Model And Mechanization Fusion Techniques For Image-Aided Navigation, Constance D. Hendrix

Theses and Dissertations

Unmanned aerial vehicles are no longer used for just reconnaissance. Current requirements call for smaller autonomous vehicles that replace the human in high-risk activities. Many times these activities are performed in GPS-degraded environments. Without GPS providing today's most accurate navigation solution, autonomous navigation in tight areas is more difficult. Today, image-aided navigation is used and other methods are explored to more accurately navigate in such areas (e.g., indoors). This thesis explores the use of inertial measurements and navigation solution updates using cameras with a model-based Linear Quadratic Gaussian controller. To demonstrate the methods behind this research, the controller will provide …


Adaptive And Reconfigurable Flight Control, Yih Shiun Huang Mar 2001

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 …