TY - JOUR
T1 - A new PI-based optimal linear quadratic state-estimate tracker for discrete-time non-square non-minimum phase systems
AU - Tsai, Jason Sheng Hong
AU - Liao, Ying Ting
AU - Lin, Zih Wei
AU - Ebrahimzadeh, Faezeh
AU - Guo, Shu Mei
AU - Shieh, Leang San
AU - Canelon, Jose I.
N1 - Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2018/7/4
Y1 - 2018/7/4
N2 - A new proportional–integral (PI)-based optimal linear quadratic state-estimate tracker, derived using a proportional–integral–derivative (PID) filter-based frequency-domain shaping approach, is proposed in this paper for discrete-time non-square non-minimum phase multi-input-multi-output systems. Subsequently, a new integrated PID filter-shaped optimal PI state estimator is presented for the aforementioned systems, so that both the proposed state estimator and the state-estimate tracker are able to achieve satisfactory minimum phase-like tracking performance, for the case of arbitrary command inputs with significant variations at some isolated time instants.
AB - A new proportional–integral (PI)-based optimal linear quadratic state-estimate tracker, derived using a proportional–integral–derivative (PID) filter-based frequency-domain shaping approach, is proposed in this paper for discrete-time non-square non-minimum phase multi-input-multi-output systems. Subsequently, a new integrated PID filter-shaped optimal PI state estimator is presented for the aforementioned systems, so that both the proposed state estimator and the state-estimate tracker are able to achieve satisfactory minimum phase-like tracking performance, for the case of arbitrary command inputs with significant variations at some isolated time instants.
UR - http://www.scopus.com/inward/record.url?scp=85048017826&partnerID=8YFLogxK
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U2 - 10.1080/00207721.2018.1479461
DO - 10.1080/00207721.2018.1479461
M3 - Article
AN - SCOPUS:85048017826
SN - 0020-7721
VL - 49
SP - 1856
EP - 1877
JO - International Journal of Systems Science
JF - International Journal of Systems Science
IS - 9
ER -