Fractional order state space canonical model identification using fractional order information filter

Abstract

In the present paper the identification and estimation problem of a fractional order state space system will be addressed. This paper presents a fractional order information filter and also a hierarchical identification algorithm to identify and estimate parameters and states of a fractional order system. Then, merging this algorithm with fractional order information filter, a novel identification method based on hierarchical identification theory is introduced to reduce the computational complexity. Finally, the applicability and performance of this platform on an exemplary system is examined.

Implementation of Carlson based Fractional Differentiators in Control of Fractional Order Plants

“…Some of the model order reduction methods available in literature are based on transfer function models and others on state space models [24,25]. Model order reduction based on transfer function is equivalent to approximating the original model of order N to approximated model of order r, where r < N and for state space model, it is reducing the matrix A but B and C unchanged.…”