An investigation study on model reference adaptive techniques as applied to attitude control system for launch vehicles Final report

Self adaptive control techniques for attitude control on Saturn 5 launch vehicle

Research on the design of adaptive control systems, volume 1 Final report

Adaptive control systems - combined optimization and adaptive control, analysis-synthesis and passive adaptive systems, learning systems, and measurement adaptive system

F-8C adaptive flight control extensions

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated

Non-global parameter estimation using local ensemble Kalman filtering

We study parameter estimation for non-global parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, we present a methodology whereby spatially-varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as our numerical testbed, we show that this parameter estimation methodology accurately estimates parameters which vary in both space and time, as well as parameters representing physics absent from the model

Adaptive Kalman Filter Based on Evolutionary Algorithm and Fuzzy Interference System

This is a survey paper.The performance of the Kalman filter (KF), which is Algoritstandard as an outstanding implementation for dynamic system state estimation, greatly depends on its parameter R, called the measurement noise covariance matrix. . However, it’s dif?cult to obtain the accurate value of R before the ?lter starts, and the value of R is possible to change with the measurement environment once the ?lter is working. To solve this difficulty, a new parameter adaptive Kalman ?lter is proposed in this paper. In this new Kalman ?lter, the initial value of R is of?ine determined by Evolutionary hm (EA), and the value of R determined by EA is online updated by Fuzzy Inference System (FIS). The new adaptive Kalman ?lter proposed in this paper (HYdGeFuzKF) has a stronger adaptableness to time-varying measurement noises than regular Kalman ?lter (RegularKF)

Digital adaptive flight controller development

A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Two designs are described for an example aircraft. Each of these designs uses a weighted least squares procedure to identify parameters defining the dynamics of the aircraft. The two designs differ in the way in which control law parameters are determined. One uses the solution of an optimal linear regulator problem to determine these parameters while the other uses a procedure called single stage optimization. Extensive simulation results and analysis leading to the designs are presented