1996
DOI: 10.1002/(sici)1099-1239(199608)6:7<585::aid-rnc167>3.0.co;2-2
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Time-scale separation and robust controller design for uncertain nonlinear singularly perturbed systems under perfect state measurements

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Cited by 28 publications

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“…Hence, we now derive the slow and fast subsystems based on time-scale decomposition principle. Referring to [8], to derive the slow subsystem, let 𝜀 = 0 in Equation (1a) and solve for 𝑥 2 (to be denoted as 𝑥 2𝑠 ) under the nonsingular property of 𝑓 22…”
Section: Assumption 2 𝑓
mentioning
confidence: 99%
“…Hence, we now derive the slow and fast subsystems based on time‐scale decomposition principle. Referring to [8], to derive the slow subsystem, let ε=0$$ \varepsilon =0 $$ in Equation () and solve for x2$$ {x}_2 $$ (to be denoted as x2s$$ {x}_{2s} $$) under the nonsingular property of f22$$ {f}_{22} $$ alignleftrightalign-oddx2s=Fs0(x1s)+Gs0(x1s)us+Ks0(x1s)ω$$ {x}_{2s}={F}_{s0}\left({x}_{1s}\right)+{G}_{s0}\left({x}_{1s}\right){u}_s+{K}_{s0}\left({x}_{1s}\right)\omega $$ where the subscript “s” denotes the slow component of the variable. Substituting the Equation () into () yields the slow subsystem alignleftrightalign-oddx˙1s=Fs0(x1s)+Gs0(x1s)us+Ks0(x1s)ω$$ {\dot{x}}_{1s}={F}_{s0}\left({x}_{1s}\right)+{G}_{s0}\left({x}_{1s}\right){u}_s+{K}_{s0}\left({x}_{1s}\right)\omega $$ where Fs0false(<...…”
Section: Time‐scale Decomposition and Problem Standardization
mentioning
confidence: 99%
“…Therefore, the robustness of the two sub‐controllers that satisfy the attenuation indices γs$$ {\gamma}_s $$ and γf$$ {\gamma}_f $$, respectively, needs to be rigorously analyzed with respect to the original full‐order TTS system. Currently, some scholars focus on exploring the optimal perturbation attenuation level γ$$ {\gamma}^{\ast } $$ (i.e., the infimum of γ$$ \gamma $$) and designing the H$$ {H}_{\infty } $$ suboptimal controller with the help of the zero‐sum game idea [8]. The system decomposition means given therein are of certain reference significance, but there is a noticeable difference between the control objective and that of this article.…”
Section: Time‐scale Decomposition and Problem Standardization
mentioning
confidence: 99%
See 2 more Smart Citations
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…Hence, we now derive the slow and fast subsystems based on time-scale decomposition principle. Referring to [8], to derive the slow subsystem, let 𝜀 = 0 in Equation (1a) and solve for 𝑥 2 (to be denoted as 𝑥 2𝑠 ) under the nonsingular property of 𝑓 22…”
Section: Assumption 2 𝑓
mentioning
confidence: 99%
“…Hence, we now derive the slow and fast subsystems based on time‐scale decomposition principle. Referring to [8], to derive the slow subsystem, let ε=0$$ \varepsilon =0 $$ in Equation () and solve for x2$$ {x}_2 $$ (to be denoted as x2s$$ {x}_{2s} $$) under the nonsingular property of f22$$ {f}_{22} $$ alignleftrightalign-oddx2s=Fs0(x1s)+Gs0(x1s)us+Ks0(x1s)ω$$ {x}_{2s}={F}_{s0}\left({x}_{1s}\right)+{G}_{s0}\left({x}_{1s}\right){u}_s+{K}_{s0}\left({x}_{1s}\right)\omega $$ where the subscript “s” denotes the slow component of the variable. Substituting the Equation () into () yields the slow subsystem alignleftrightalign-oddx˙1s=Fs0(x1s)+Gs0(x1s)us+Ks0(x1s)ω$$ {\dot{x}}_{1s}={F}_{s0}\left({x}_{1s}\right)+{G}_{s0}\left({x}_{1s}\right){u}_s+{K}_{s0}\left({x}_{1s}\right)\omega $$ where Fs0false(<...…”
Section: Time‐scale Decomposition and Problem Standardization
mentioning
confidence: 99%
“…Therefore, the robustness of the two sub‐controllers that satisfy the attenuation indices γs$$ {\gamma}_s $$ and γf$$ {\gamma}_f $$, respectively, needs to be rigorously analyzed with respect to the original full‐order TTS system. Currently, some scholars focus on exploring the optimal perturbation attenuation level γ$$ {\gamma}^{\ast } $$ (i.e., the infimum of γ$$ \gamma $$) and designing the H$$ {H}_{\infty } $$ suboptimal controller with the help of the zero‐sum game idea [8]. The system decomposition means given therein are of certain reference significance, but there is a noticeable difference between the control objective and that of this article.…”
Section: Time‐scale Decomposition and Problem Standardization
mentioning
confidence: 99%
See 1 more Smart Citation
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…H ∞ control problem for SPSs were studied by making use of a differential game theoretic approach in [193,40,161,162]. A class of SPSs being nonlinear only on the slow variables was examined in [163,190] and H ∞ controller design methods were proposed. In [225,155,192], H ∞ control of nonstandard SPSs was considered and some efficient design methods were proposed.…”
Section: Singularly Perturbed Systems
mentioning
confidence: 99%
“…Besides the approaches based on T-S fuzzy model [137]- [233], there have been various design methods for nonlinear SPSs in particular forms. In [163]- [54], H ∞ control for nonlinear SPSs was investigated. However, these methods can be only applied to standard nonlinear SPSs since they are based on decomposing the original system into two reduced-order subsystems in different time scales.…”
Section: Theorem 73
mentioning
confidence: 99%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.