A new PI optimal linear quadratic state-estimate tracker for continuous-time non-square non-minimum phase systems

Jason Sheng Hong Tsai; Ying Ting Liao; Faezeh Ebrahimzadeh; Sheng Ying Lai; Te Jen Su; Shu Mei Guo; Leang San Shieh; Tzong Jiy Tsai

doi:10.1080/00207721.2016.1261201

A new PI optimal linear quadratic state-estimate tracker for continuous-time non-square non-minimum phase systems

Jason Sheng Hong Tsai, Ying Ting Liao, Faezeh Ebrahimzadeh, Sheng Ying Lai, Te Jen Su, Shu Mei Guo, Leang San Shieh, Tzong Jiy Tsai

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Based on the proportional-integral-derivative (PID) filter-shaping approach, this paper presents a new proportional-plus-integral (PI) optimal linear quadratic state estimator (LQSE) for the continuous-time non-square and non-minimum phase (NMP) multivariable systems. Together with the recently developed optimal linear quadratic tracker (LQT), the proposed LQSE-based tracker is able to optimally achieve good minimum phase-like tracking performances for a non-square NMP multivariable system with unmeasurable states and arbitrary command inputs.

Original language	English
Pages (from-to)	1438-1459
Number of pages	22
Journal	International Journal of Systems Science
Volume	48
Issue number	7
DOIs	https://doi.org/10.1080/00207721.2016.1261201
Publication status	Published - 2017 May 19

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications

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10.1080/00207721.2016.1261201

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title = "A new PI optimal linear quadratic state-estimate tracker for continuous-time non-square non-minimum phase systems",

abstract = "Based on the proportional-integral-derivative (PID) filter-shaping approach, this paper presents a new proportional-plus-integral (PI) optimal linear quadratic state estimator (LQSE) for the continuous-time non-square and non-minimum phase (NMP) multivariable systems. Together with the recently developed optimal linear quadratic tracker (LQT), the proposed LQSE-based tracker is able to optimally achieve good minimum phase-like tracking performances for a non-square NMP multivariable system with unmeasurable states and arbitrary command inputs.",

author = "Tsai, {Jason Sheng Hong} and Liao, {Ying Ting} and Faezeh Ebrahimzadeh and Lai, {Sheng Ying} and Su, {Te Jen} and Guo, {Shu Mei} and Shieh, {Leang San} and Tsai, {Tzong Jiy}",

note = "Publisher Copyright: {\textcopyright} 2016 Informa UK Limited, trading as Taylor & Francis Group.",

year = "2017",

month = may,

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doi = "10.1080/00207721.2016.1261201",

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AU - Tsai, Jason Sheng Hong

AU - Liao, Ying Ting

AU - Ebrahimzadeh, Faezeh

AU - Lai, Sheng Ying

AU - Su, Te Jen

AU - Guo, Shu Mei

AU - Shieh, Leang San

AU - Tsai, Tzong Jiy

PY - 2017/5/19

Y1 - 2017/5/19

N2 - Based on the proportional-integral-derivative (PID) filter-shaping approach, this paper presents a new proportional-plus-integral (PI) optimal linear quadratic state estimator (LQSE) for the continuous-time non-square and non-minimum phase (NMP) multivariable systems. Together with the recently developed optimal linear quadratic tracker (LQT), the proposed LQSE-based tracker is able to optimally achieve good minimum phase-like tracking performances for a non-square NMP multivariable system with unmeasurable states and arbitrary command inputs.

AB - Based on the proportional-integral-derivative (PID) filter-shaping approach, this paper presents a new proportional-plus-integral (PI) optimal linear quadratic state estimator (LQSE) for the continuous-time non-square and non-minimum phase (NMP) multivariable systems. Together with the recently developed optimal linear quadratic tracker (LQT), the proposed LQSE-based tracker is able to optimally achieve good minimum phase-like tracking performances for a non-square NMP multivariable system with unmeasurable states and arbitrary command inputs.

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A new PI optimal linear quadratic state-estimate tracker for continuous-time non-square non-minimum phase systems

Abstract

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