Identification of decomposable time-varying parameters by means of gradient algorithms

This paper investigates the identification of a class of rapidly varying parameters, bk (n ?? 1 vector), by means of sequential identification algorithms. These algorithms are heuristic extensions of relatively well known, constant-parameter, identification algorithms to the time-varying parameter situation. It is assumed that bk = P(k)??k where P(k) is an n ?? n invertible information matrix whose elements are either measurable, or specified ahead of time, at tk, and ??k is a collection of unknown parameters which vary less rapidly than bk. Such a decomposition is often possible in aerospace applications. A priori and a posteriori identification algorithms are described and synthesized for both the perfect and noisy measurement situations. These algorithms are then applied to the identification of aerodynamic parameters of a high-performance, aerodynamically controlled aerospace vehicle. The feasibility of tracking these parameters by means of the a posteriori algorithms is demonstrated.