Parametric design of robust fault isolation observers for linear non-square systems

Wahrburg, Arne; Adamy, Jürgen

doi:10.1016/j.sysconle.2013.01.012

Cited by 20 publications

(19 citation statements)

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“…P. Liu & Patton, 1998;Y. Liu, Tan, Wang, & Wang, 2013;Moore, 1976;Ouyang, Richiedei, Trevisani, & Zanardo, 2012;Piou & Sobel, 1994, 1995Pomfret & Clarke, 2009;Shi & Patton, 2012;Wahrburg & Adamy, 2013;White, 1995;White, Bruyere, & Tsourdos, 2007). EA facilitates control system design by synthesizing a feedback gain matrix that exactly places the closed loop eigenvalues whilst matching the closed loop eigenvectors as closely as possible to a desired set.…”

mentioning

confidence: 99%

Increasing eigenstructure assignment design degree of freedom using lifting

Chen

Pomfret

Clarke

2016

International Journal of Control

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This paper presents the exposition of an output-lifting eigenstructure assignment (EA) design framework, wherein the available EA design degrees of freedom (DoF) is significantly increased, and the desired eigenstructure of a single-rate full state feedback solution can be achieved within an output feedback system. A structural mapping is introduced to release the output-lifting causality constraint. Additionally, the available design DoF can be further enlarged via involving the input-lifting into the output-lifting EA framework. The newly induced design DoF can be utilised to calculate a structurally-constrained, causal gain matrix which will maintain the same assignment capability. In this paper, the robustification of the output-lifting EA is also proposed, which allows a trade-off between performance and robustness in the presence of structured model uncertainties to be established. A lateral flight control benchmark in the EA literature and a numerical example are used to demonstrate the effectiveness of the design framework.Keywords: lifting; eigenstructure assignment; causality constraint IntroductionEigenstructure assignment (EA) is a mature technique for the design of control systems, especially for flight control system (Alireza & Batool, 2012;B. Chen & Nagarajaiah, 2007;Clake, Ensor, & Griffin, 2003;Farineau, 1989;Kshatriya, Annakkage, Hughes, & Gole, 2007;G. P. Liu & Patton, 1998;Y. Liu, Tan, Wang, & Wang, 2013;Moore, 1976;Ouyang, Richiedei, Trevisani, & Zanardo, 2012;Piou & Sobel, 1994, 1995Pomfret & Clarke, 2009;Shi & Patton, 2012;Wahrburg & Adamy, 2013;White, 1995;White, Bruyere, & Tsourdos, 2007). EA facilitates control system design by synthesizing a feedback gain matrix that exactly places the closed loop eigenvalues whilst matching the closed loop eigenvectors as closely as possible to a desired set. Some useful properties EA imbues on a system are: stability of response, appropriateness of transient response, decoupling of state or output response and disturbance rejection. Compared with many other competitive approaches exist to manipulate the eigenvalues of the closed-loop system and do not takes into account the role of the eigenvector, EA clearly exploits how system inputs affect mode dynamics and how these mode dynamics will be assigned to system states. Through defining a set of ideal closed-loop eigenstructure (e.g. eigenstructure which represents the realistic handling qualities of the flight control system), the realistic control effect will be guaranteed. In addition, the algorithm itself and the expression of the available design DoF can be highly visible. However, due to the lack of design DoF, e.g. the Kimura condition is not satisfied (Kimura, 1975), output feedback EA usually cannot fully assign the desired eigenstructure. This is an open problem which has been widely discussed in the literature (Andry, Shapiro, & Chung, 1983;L. Chen & Clarke, 2009;Clarke & Griffin, 2004;Pomfret, Clarke, & Ensor, 2005;Roppenecker & O'Reilly, 1989;Srinathkumar, 1978;Zhao & Lam, 2016a, 2016b.Mul...

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mentioning

confidence: 99%

Increasing eigenstructure assignment design degree of freedom using lifting

Chen

Pomfret

Clarke

2016

International Journal of Control

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“…The objective is to parametrize all solutions leading to (11a) and (11b) for the nominal system. We only give the basic idea here and refer to [17] for details and proofs.…”

Section: Nominal Fault Isolation Observer Designmentioning

confidence: 99%

“…The core idea of the design proposed in [34] and used for FIO design in [16,17] is to tune the eigenstructure of (15) using E L and E V in order to obtain a diagonal transfer matrix G rf;0 .s/. To this end, double indices are used for the first ı observer eigenvalues B ij .…”

Section: Assumptionmentioning

confidence: 99%

“…In a deterministic setting, robustness with respect to exogenous disturbances has been extensively studied in [14][15][16][17] for FIOs. However, regarding robustness of FIOs with respect to parametric uncertainties, there exist much less results in literature.…”

Section: Introductionmentioning

confidence: 99%

“…The approach is based on parametric eigenstructure assignment and thus extends the results presented in [16], which deal with the rejection of exogenous disturbances. The results were extended to finite frequency disturbances in [17]. The parametric eigenstructure assignment approach allows to guarantee ideal fault isolation for a specified nominal model, that is, in the absence of parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Design of robust fault isolation observers for linear systems with bounded uncertain parameters

Wahrburg¹

2015

Adaptive Control & Signal

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In this article, a design procedure for robust fault isolation observers for linear systems is proposed. It allows to detect and isolate faults by means of a single, specifically parametrized observer, yielding a small implementation effort compared with standard bank-of-observers approaches. A design based on parametric eigenstructure assignment for nominal system dynamics is presented. The main contribution is to show how the remaining degrees of freedom can be utilized to optimize robustness of the fault isolation observer with respect to uncertain system parameters. Therein, the uncertainties are assumed to belong to a polyhedral domain. The application of the design to two examples verifies the results for systems defined by nominal dynamics and known bounds on the uncertain parameters.

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