Partitioning identification algorithms

In this paper, batch processing partitioning parameter identification agorithms are obtained using the "partitioning" approach to estimation. The algorithms, herein denoted the GPIA\´s, are applicable to linear as well as nonlinear systems and are derived by a natural applicalton of the generalized partitioned algorithms (GPA\´s) of Lainiotis; namely, by selecting a natural partitioning of the augmented state vector (the system state and unknown parameters); by linearization of the model equations; and then by using, in an iterative fashion, the GPA algorithms for the augmented state. The relationships between the GPIA\´s and maximum-likelihood identification methods, which employ gradient based numerical techniques to obtain a solution, are also established. An example of the application of the GPIA to aircraft parameter identification from actual flight test data is presented, as well as a direct comparison with the results obtaining using an iterated extended Kalman filter algorithm.