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Adaptive switching control based on limited multiple models
Author(s) -
Yin Qitian,
Wang Mao,
Zhao Wei
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2998
Subject(s) - identification (biology) , adaptation (eye) , computer science , scheme (mathematics) , identification scheme , system identification , adaptive control , dynamical systems theory , control theory (sociology) , control (management) , control engineering , engineering , artificial intelligence , data mining , mathematics , mathematical analysis , physics , botany , quantum mechanics , optics , biology , measure (data warehouse)
Summary In this paper, the limited multiple models system identification scheme with third‐level adaptation is proposed to build the virtual identification model for the control of the closed‐loop system. First, the dynamical switching identification models subbank is constructed based on the performance index. Next, the virtual identification model is build through the combination of the identification models in the dynamical switching subbank. Then, the combination proportion of the models is adaptively tuning with the third‐level adaptation. The main contribution of this paper is that the proposed dynamical system identification method can overcome system resources waste of the multiple models switching with second‐level adaptation, and it does not require that all of the identification models combine directly. Finally, the simulation is provided to illustrate its effectiveness.