Equation error versus output error methods

Tsutsumi, Yutaka; Damen, A.A.H.; Hof, Paul M.J. Van den

doi:10.1080/00140139208967836

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…More details can be found e.g. in [28] or [31]. The numerical example analyzed here also gives insight on the procedures proposed in Section III-C.…”

Section: A Simulation Resultsmentioning

confidence: 98%

“…Note that the condition (33) of Lemma 1 is in a sense adverse to the convergence condition (31) of Theorem 2. Sincê ρ i < 1 by definition, there exists always a value ofp large enough to satisfy (31). On the other hand, the right-hand side of (33) decreases asp increases, while τ imax is only weakly dependent onp.…”

Section: E Infinite-horizon Simulation Error Boundmentioning

confidence: 99%

“…Theorem 3. Let Assumptions 2-5 hold, and further assume that the chosen value ofp satisfies (31). Consider a generic parameter vector θ i1 ∈ R o(m+1) .…”

Section: E Infinite-horizon Simulation Error Boundmentioning

confidence: 99%

See 2 more Smart Citations

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Lauricella

Fagiano

2020

IEEE Trans. Automat. Contr.

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The problem of model identification for linear systems is considered, using a finite set of sampled data affected by a bounded measurement noise, with unknown bound. The objective is to identify one-step-ahead models and their accuracy in terms of worst-case simulation error bounds. To do so, the Set Membership identification framework is exploited. Theoretical results are derived, allowing one to estimate the noise bound and system decay rate. Then, these quantities and the data are employed to define the Feasible Parameter Set (FPS), which contains all possible models compatible with the available information.Here, the estimated decay rate is used to refine the standard FPS formulation, by adding constraints that enforce the desired converging behavior of the models' impulse response. Moreover, guaranteed simulation error bounds for an infinite future horizon are derived, improving over recent results pertaining to finite simulation horizon only. These bounds are the basis for a result and method to guarantee asymptotic stability of the identified model. Finally, the desired one-step-ahead model is identified by means of numerical optimization, and the related simulation error bounds are evaluated. Both input-output and state-space model structures are addressed. The approach is showcased on a numerical example and on real-world experimental data of the roll rate dynamics of an autonomous glider.

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“…More details can be found e.g. in [28] or [31]. The numerical example analyzed here also gives insight on the procedures proposed in Section III-C.…”

Section: A Simulation Resultsmentioning

confidence: 98%

Section: E Infinite-horizon Simulation Error Boundmentioning

confidence: 99%

See 1 more Smart Citation

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Lauricella

Fagiano

2020

IEEE Trans. Automat. Contr.

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show abstract

“…An overview of different error models is given in Ljung (1999). In a least-squares sense the EE approach results in the best one-step ahead predictions, while the OE approach yields the best model predictions over a long time horizon (Tomita, Damen, & van den Hof (1992)). In terms of implementation for the problem at hand the two approaches only differ in the question how the error e½k in Eq.…”

Section: Derivation Of the Extended Algorithmmentioning

confidence: 98%

Recursive parameter estimation of exhaust gas oxygen sensors with input-dependent time delay and linear parameters

Moser

Onder

Guzzella

2015

Control Engineering Practice

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“…However, least-squares estimation of G(q) and H(q) emphasizes a fit to high frequency components in the data [13]. This is undesirable, since the relevant frequencies in manual control tasks range only up to about 5 rad/s as stated in [14].…”

Section: Identification Of Driver Model Parametersmentioning

confidence: 99%

Identification and validation of lateral driver models on experimentally induced driving behavior

Hermannstadter

Yang

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Equation error versus output error methods

Cited by 17 publications

References 14 publications

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Recursive parameter estimation of exhaust gas oxygen sensors with input-dependent time delay and linear parameters

Identification and validation of lateral driver models on experimentally induced driving behavior

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