To provide a forum for discussion of the status and the future of this technology, the NASA Flight Research Center hosted a Symposium on Parameter Estimation Techniques and Application in Aircraft Flight Testing. Technical papers were presented by selected representatives from industry, universities, and various Air Force, Navy, and NASA installations who are actively working in the field.
This book presents neural partial differentiation as an estimation algorithm for extracting aerodynamic derivatives from flight data. It discusses neural modeling of the aircraft system. The neural partial differentiation approach discussed in the book helps estimate parameters with their statistical information from the noisy data. Moreover, this method avoids the need for prior information about the aircraft model parameters. The objective of the book is to extend the use of the neural partial differentiation method to the multi-input multi-output aircraft system for the online estimation of aircraft parameters from an established neural model. This approach will be relevant for the design of an adaptive flight control system. The book also discusses the estimation of aerodynamic derivatives of rigid and flexible aircraft which are treated separately. The longitudinal and lateral-directional derivatives of aircraft are estimated from flight data. Besides the aerodynamic derivatives, mode shape parameters of flexible aircraft are also identified in the book as part of identification for the state space aircraft model. Since the detailed description of the approach is illustrated through the block diagram and their results are presented in tabular form with figures of parameters converge to their estimates, the contents of this book are intended for readers who want to pursue a postgraduate and doctoral degree in science and engineering. This book is useful for practicing scientists, engineers, and teachers in the field of aerospace engineering.
The development of a procedure for estimating aircraft dynamic states and instrument systematic errors from flight test measurements is described. The method has particular application in non-steady performance estimation for reconstructing aircraft flight path and in the estimation of aerodynamic characteristics using the 'equation error' parameter estimation method. The state estimator can be extended to determine systematic measurement errors in the recorded data, giving a set of data which is compatible according to the kinematic equations which relate the measurements. The effectiveness of the procedures cannot be specified in a general way, since the results depend upon the representation of the input and output noise characteristics and on the choice of initial conditions for a given problem. This note has been written to allow users to apply the state estimation procedure to practical problems. A description of the Carlson Square Root Filter and its application to the kinematic equations of aircraft motion is given. The documentation of the computer program for state estimation is also presented. (Author).
