TABLE OF CONTENTS In previous chapters, we have discussed several parameter estimation techniques for linear and nonlinear dynamic systems. It was stated often that the Kalman filter, being a recursive algorithm, is more suitable for real-time applications. Many other approaches like estimation before modelling and model error estimation algorithms can be used in a recursive manner for parameter estimation. However, they put a heavy burden on computation.
Modern day systems are complex and they generate extensive data, which puts a heavy burden on post-processing data analysis requirements. Many times, simple results of system identification and parameter estimation are required quickly. Often, it is viable to send data to a ground station by telemetry for 'real-time' analysis.
There are situations where on-line estimation could be very useful: a) model-based approach to sensor failure detection and identification; b) reconfigurable control system; c) adaptive control; and d) determination of lift and drag characteristics of an aircraft from its dynamic manoeuvres.
For the on-line/real-time parameter estimation problem, several aspects are important: i) the estimation algorithm should be robust; ii) it should converge to an estimate close to the true value; iii) its computational requirements should be moderately low or very low; and iv) the algorithm should be numerically reliable and stable so that condition (i) is assured.
It is possible to apply on-line techniques to an industrial process as long as transient responses prevail, since when these responses die out or subside, there is no activity and all input-output signals of the process (for identification)...
