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Multiple Model Adaptive Control Of A Large Flexible Space Structure With Purposeful Dither For Enhanced Identifiability, James A. Fitch
Multiple Model Adaptive Control Of A Large Flexible Space Structure With Purposeful Dither For Enhanced Identifiability, James A. Fitch
Theses and Dissertations
Dithering techniques for enhancing uncertain parameter identification with moving-bank Multiple Model Adaptive Estimation MMAE and Control MMAC algorithms are analyzed in this thesis. The dithering techniques and multiple- model adaptive algorithms are applied to the SPICE 2 flexible space structure. The dithering techniques studied include purposefully constructed square wave, sine wave fixed and swept frequency and wide-band noise wave forms. Purposeful rigid-body slew commands are also used in order to excite the structures flexible bending modes. Dither inputs into the structure are performed in an effort to enhance the open-loop identifiability of the uncertain parameter, namely a scalar multiplier on …
Control Of A Large Space Structure Using Multiple Model Adaptive Estimation And Control Techniques, Gregory J. Schiller
Control Of A Large Space Structure Using Multiple Model Adaptive Estimation And Control Techniques, Gregory J. Schiller
Theses and Dissertations
The purpose of this thesis is to apply moving-bank multiple model adaptive estimation and control MMAEMMAC algorithms to an actual space structure SPICE being examined at Phillips Laboratory at Kirtland AFB, NM. The structure consists of a large platform and a smaller platform connected by three legs in a tripod fashion. Kalman filtering and LQG control techniques are utilized as the primary design tool. Implementing a bank of filters increases the robustness of the LQG controller when uncertainties exist in the system model, whereas the moving bank is utilized to reduce the computational load. Several reduced-order models are developed from …
Multiple Model Adaptive Estimation Applied To The Lambda Urv For Failure Detection And Identification, David W. Lane
Multiple Model Adaptive Estimation Applied To The Lambda Urv For Failure Detection And Identification, David W. Lane
Theses and Dissertations
Multiple Model Adaptive Estimation (MMAE) is a method of estimating unknown system parameters by modeling all possible parameter configurations in several models. The parameters for this research are failure status conditions associated with flight control actuators and sensors on the LAMBDA Unmanned Research Vehicle, an experimental aircraft operated by Wright Laboratory Flight Controls Division at Wright-Patterson Air Force Base, Ohio. Six actuator failures and eight sensor failures are modeled, along with the fully functional aircraft, in fifteen elemental Kalman filters. These filters propagate and update their own aircraft state estimates in real time. A probability computation representing the likelihood of …
Development Of A Performance Evaluation Tool (Mmsofe) For Detection Of Failures With Multiple Model Adaptive Estimation (Mmae), Robert L. Nielsen
Development Of A Performance Evaluation Tool (Mmsofe) For Detection Of Failures With Multiple Model Adaptive Estimation (Mmae), Robert L. Nielsen
Theses and Dissertations
Multiple model Kalman Filter (KF) techniques are used extensively for Multiple Model Adaptive Estimation (MMAE), Multiple Model Adaptive Control (MMAC), and Distributed Kalman Filter (DKF) applications to determine Bayesian- blended optimal estimates of states, uncertain parameters, and optimal control signals. Multiple model methods are used for sensor management, Failure Detection and Isolation (FDI), and other Guidance and Control (G and C) applications. A simulation tool called the Multiple Model Simulation for Optimal Filter Evaluation (MMSOFE) has been in this research. MMSOFE is based on the well-benchmarked single Kalman filter tool called Multimode Simulation for Optimal Filter Evaluation (MSOFE). MMSOFE is …