I. Introduction

In General, the motion of a flight vehicle can be represented though well-defined equations of motion, whereby adequate nonlinear models with time-invariant parameters can be postulated and updated to characterize aerodynamic effects. Accordingly, the major applications of aircraft parameter estimation have been in an offline mode, applying methods discussed in Chapters 4 6, which assume the availability of data set over a fixed interval of time. Such methods are also called batch mode or post-flight analysis techniques, which implicitly assume that system parameters are constant over the period of observation. All data points are processed together at a time, yielding parameters representing average system behavior. As opposed to batch-processing, recursive estimation methods utilize the data point-by-point as they become available. Such methods are alternatively called sequential, adaptive, or real-time algorithms.1 ^,2 We use the terminology of recursive methods which is commonly followed in the control applications.

Historically, it appears that C.-F. Gauss, the inventor of the least squares method (see Chapter 6, Sec. I), had also suggested the recursive least squares (RLS) approach.3 The RLS method was, however, not widely pursued until 1950, when R. L. Plackett rediscovered the algorithm.4 This was followed by widespread use of RLS and development of other recursive algorithms. The basic ideas of practical engineering applications of recursive parameter estimation (RPE) originate from control applications in chemical and thermal power-generating industrial processes, where online adaptation of model parameters is desired to increase the overall plant efficiency. In such applications, we encounter not only...