Observers design for uncertain Takagi–Sugeno systems with unmeasurable premise variables and unknown inputs. Application to a wastewater treatment plant

Kiss, Anca Maria Nagy; Marx, Benoît; Mourot, Gilles; Schutz, Georges; Ragot, José

doi:10.1016/j.jprocont.2011.05.001

Cited by 70 publications

(19 citation statements)

References 30 publications

(57 reference statements)

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“…However, few engineering approaches were employed to solve the problem of sludge bulking. These approaches primarily relied on the past observations of sludge bulking without sufficient microbiological insight (Nagy Kiss et al, 2011). Xavier et al…”

Section: Page 6 Of 47mentioning

confidence: 99%

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Bagheri

Mirbagheri

Bagheri

et al. 2015

Process Safety and Environmental Protection

120

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Section: Page 6 Of 47mentioning

confidence: 99%

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Bagheri

Mirbagheri

Bagheri

et al. 2015

Process Safety and Environmental Protection

120

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“…Takagi‐Sugeno (T‐S) fuzzy technique has been recognized as an effective tool for handling the difficult problem in the nonlinear systems. Thus, it has been widely used to study problems of modeling, identification, observation, and control (adaptive, predictive, robust, optimal, parallel distributed compensation (PDC), fault tolerant control, and so on) of various biological processes …”

Section: Introductionmentioning

confidence: 99%

Takagi‐Sugeno tracking control of a fermentation process with respect to asymmetric constraints

Mohamed

Karama

Khallouq

2019

Adaptive Control & Signal

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Summary This article deals with the tracking problem for nonlinear biological process with asymmetric constraints on the controller. Using the fuzzy logic Takagi‐Sugeno (TS), the nonlinear process is modeled by a fuzzy model and a parallel distributed compensation (PDC) controller with respect asymmetric constraints is designed. The main contribution of this work is to extend the available results, in the context of a TS formulation, for symmetrical constraints to asymmetrical ones. Hence, sufficient conditions are obtained for the first time, in terms of linear matrix inequalities (LMIs), for the existence of an asymmetrical constrained PDC controller. Moreover, fuzzy observer based controller with unmeasured premise variables is designed. The performance and effectiveness of the method are illustrated in simulation by considering a fermentation process.

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“…Then, an FDI system should be able to deal with these modes. Applications of descriptor systems can be found in aircraft modelling (Masubuchi et al, 2004), complex systems (Nagy- Kiss et al, 2011a), and electrical, mechanical, or hydraulic systems (Duan, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Some of them are related to fault detection for D-LPVsystems with measurable scheduling functions (Hamdi et al, 2012b;Astorga-Zaragoza et al, 2011;Aguilera-González et al, 2013) and only several are related to D-LPV systems. However, we can mention Nagy- Kiss et al (2011a), who propose a state observer by transforming the D-LPV system with unmeasurable scheduling functions into an equivalent uncertain system. In a work by Nagy Kiss et al (2011b), an unknown input observer was developed by considering the original system another a perturbed system, where the perturbation vector represents a bounded uncertainty given by measurable and unmeasurable scheduling functions.…”

Section: Introductionmentioning

confidence: 99%

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Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

López‐Estrada

Ponsart

Astorga‐Zaragoza

et al. 2015

International Journal of Applied Mathematics and Computer Science

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This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.

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Observers design for uncertain Takagi–Sugeno systems with unmeasurable premise variables and unknown inputs. Application to a wastewater treatment plant

Cited by 70 publications

References 30 publications

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Takagi‐Sugeno tracking control of a fermentation process with respect to asymmetric constraints

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

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