On the simultaneous state and unknown input estimation of complex systems via a multiple model strategy

Orjuela, Rodolfo; Marx, Benoît; Ragot, José; Maquin, Didier

doi:10.1049/iet-cta.2008.0148

Cited by 44 publications

(25 citation statements)

References 40 publications

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“…With P=diag(P 1 , P 2 )>0, (2)-(15), the Schur complement and variable changes defined by (24), we get conditions (26) where ∆ 1i and ∆ 2i are defined by (20). This completes the proof.…”

Section: H -Fault Sensitivity Conditionsmentioning

confidence: 53%

“…with ∆ 1i and ∆ 2i are defined by (20). Then the estimation error (16) is asymptotically stable with the performance (9).…”

Section: H -Fault Sensitivity Conditionsmentioning

confidence: 99%

“…LMI synthesis conditions to design observer for T-S models have been given (See for example [7,10,18,22]). Thus, methods for detecting and also estimating faults using different techniques such as unknown input observers [19,20], sliding mode technique [21], and descriptor system technique [10,16] have been proposed. However, to the best of our knowledge, very few effort has been made to compute residual generator as sensitive as possible to faults and as robust as possible to unknown disturbances for T-S models.…”

Section: Introductionmentioning

confidence: 99%

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Design of robust fault detection observer for Takagi-Sugeno models using the descriptor approach

Bouattour

Chadli

Chaabane³

et al. 2011

Int. J. Control Autom. Syst.

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This paper deals with the design of a robust fault detection observer for a Takagi-Sugeno (T-S) fuzzy model affected by sensor and actuator faults and unknown bounded disturbances simultaneously. An observer based on the technique of descriptor systems is studied. Indeed, by considering faults as auxiliary state variables, both states and faults are estimated simultaneously. In order to guarantee the best robustness to disturbances and sensitivity to faults, the developed observer combine the H -/H ∞ performances. Then, based on Lyapunov method, asymptotic stability conditions are given to design the observer parameters. In order to get convenient and reliable faults estimator in computations, an iterative linear matrix inequality (LMI) algorithm is developed. This algorithm, solved easily using existing numerical tools, allows to minimize influences of disturbances and maximize the ones of faults. Finally, a numerical example is proposed to illustrate the effectiveness of the result.

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Section: H -Fault Sensitivity Conditionsmentioning

confidence: 53%

“…with ∆ 1i and ∆ 2i are defined by (20). Then the estimation error (16) is asymptotically stable with the performance (9).…”

Section: H -Fault Sensitivity Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of robust fault detection observer for Takagi-Sugeno models using the descriptor approach

Bouattour

Chadli

Chaabane³

et al. 2011

Int. J. Control Autom. Syst.

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“…In the sequel, this multiple model is called the heterogeneous multiple model. The previously quoted works have illustrated successful implementations of this structure for modelling (Venkat et al, 2003;Vinsonneau et al, 2005;Orjuela et al, 2006), control (Gawthrop, 1995;Gatzke and Doyle III, 1999;Gregorčič and Lightbody, 2000) or state estimation and diagnostic (Kanev and Verhaegen, 2006;Uppal et al, 2006;Orjuela et al 2008;2009) and have shown its relevance. Hence, this kind of multiple model can be used as an interesting alternative to the homogeneous multiple model.…”

Section: R Orjuela Et Almentioning

confidence: 99%

“…This kind of multiple model, initially proposed by Filev (1991), is reported in the literature under several designations, such as the local-state local model network (Gawthrop, 1995), the multiple local models (Gatzke and Doyle III, 1999;Venkat et al, 2003;Vinsonneau et al, 2005), local model networks by blending the outputs (Gregorčič and Lightbody, 2000;, multiple model for models with a non-common state (Kanev and Verhaegen, 2006), the neuro-fuzzy decoupling multiple model scheme (Uppal et al, 2006) and the recently decoupled multiple model (Orjuela et al, 2006;2009). Despite their different names, these approaches share a similar multiple model structure.…”

Section: R Orjuela Et Almentioning

confidence: 99%

Nonlinear system identification using heterogeneous multiple models

Orjuela¹,

Marx²,

Ragot³

et al. 2013

International Journal of Applied Mathematics and Computer Science

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Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

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State estimation for one‐dimensional agro‐hydrological processes with model mismatch

Liu,

Zhao

et al. 2023

Can J Chem Eng

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The importance of accurate soil moisture data for the development of modern closed‐loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for the agro‐hydrological system. In this study, soil moisture estimation in one‐dimensional (1D) agro‐hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state‐space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real‐time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E‐step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q‐function, while in the M‐step, unknown inputs are updated by locally maximizing the recursive Q‐function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

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On the simultaneous state and unknown input estimation of complex systems via a multiple model strategy

Cited by 44 publications

References 40 publications

Design of robust fault detection observer for Takagi-Sugeno models using the descriptor approach

Design of robust fault detection observer for Takagi-Sugeno models using the descriptor approach

Nonlinear system identification using heterogeneous multiple models

State estimation for one‐dimensional agro‐hydrological processes with model mismatch

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