TABLE OF CONTENTS The filter error method, discussed in Chapter 5, accounts for both process and measurement noise and is, therefore, considered the most general approach to parameter estimation problems. Though primarily used for analysing data in turbulence (process noise), it has also been found to give good results for data without turbulence.
The filter error method has also been used to estimate parameters of unstable systems. In the majority of the parameter estimation applications pertaining to unstable systems, particularly in the field of aircraft flight data analysis, the requirement is to estimate the parameters of the basic unstable plant (open-loop model) rather than obtaining closed loop characteristics of the system. Parameter estimation of open loop unstable models can pose various problems ranging from round off errors to diverging solutions from numerical integration of the unstable system equations. The filter error method is a numerically stable scheme and, as such, easily amenable to unstable systems.
As can be seen from eq. (9.42), the use of the term [ K( k)( z( k) - ?( k))], which represents a kind of feedback of the fit error ( z( k) - ?( k)) weighted with gain K, renders the filter error algorithm numerically stable. Here, it is interesting to draw a parallel between the stabilised output error method and the filter error method.
In analogy to the filter error method, the stabilised output error method also uses...
