Open AccessFrequency selective learning model reference adaptive control
Author(s) -
Höcht Leonhard,
Maity Arnab,
Holzapfel Florian
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2014.1278
Subject(s) - control theory (sociology) , adaptive control , key (lock) , constraint (computer aided design) , computer science , adaptation (eye) , controller (irrigation) , control (management) , stability (learning theory) , rank (graph theory) , control engineering , artificial intelligence , mathematics , engineering , machine learning , geometry , agronomy , physics , computer security , combinatorics , optics , biology
This study proposes a new frequency selective learning adaptive control approach for model reference adaptive control systems to improve their adaptation performance without depending on high learning rates. It is developed by inspiration from the philosophies of the Q ‐modification and concurrent learning approaches and retains the key advantages of both. The philosophy of this proposed approach is that the known part of the plant dynamics passes through multiple filters. By constraint of the governing dynamic equations, this result equals an expression containing the unknown parameters in a filtered version, which is used for augmentation of the update law. The use of multiple filters aims at increasing the rank of the update law based on online instantaneous information and for a sufficient number of filters at different bandwidths, exponential stability can be achieved in the presence of the structured matched uncertainty. Moreover, it can cope with a sudden change of the system configuration. As a result, it leads to an efficient adaptive control approach. Furthermore, a challenging roll control problem of an aircraft demonstrates the usefulness of this proposed approach.