On optimal input design in system identification for control

In this paper, for the purpose of evaluating the degradation level of the performance in application with the parametric model in which the estimated parameters have the disturbance, we consider the relation between the robustness of parameter estimation and the model order. After the application performance cost function is derived, the gradient at the estimated parameter is focused to evaluate the disturbance level of the performance in the application. As a result, in this paper, it is shown that the robustness of parameter estimation is given as 2nd order function of the model order.

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“…From such a viewpoint, the method which designs the optimal input in system identification for control design is proposed in [6,7].…”

Section: The Similar Representationsmentioning

confidence: 99%

A Study of Relation Between Robustness of Parameter Estimation and Model Order in System Identification

Matsubara

Sugimoto

2013

Stochastic Systems Theory and its Applications (SSS)

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“…In the control literature, a survey on the sensor location problem is presented in [12], while the input design problem is treated in [16]. A more recent discussion on input design can be found in [25]. A common procedure for optimal experiment design is to minimize some scalar function of the covariance matrix of the estimate.…”

Section: Introductionmentioning

confidence: 99%

Optimal Excitation for Nonparametric Identification of Viscoelastic Materials

Rensfelt

Söderström

2011

IEEE Trans. Contr. Syst. Technol.

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The problem of optimal excitation in nonparametric identification of viscoelastic materials is considered. The goal is to design the input spectrum in an optimal way, so that the average variance of the estimates is minimized. It is shown how the covariance matrix of the estimates can be expressed in terms of the input spectrum. This theory can also be used in order to identify the (unknown) excitation, used in a particular experiment, from measured strain data. Two scalar criteria connected to A-and D-optimal experiment design, are considered. The results indicate that the accuracy of the estimates can be greatly improved by applying an optimal input signal. Issues concerning the implementation of the achieved optimal input spectrum in live experiments are discussed briefly.

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“…In this contribution we build on the optimal input design for models used in MPC developed in [3] and [4]. We present a general method of performing optimal input design on a process controlled by MPC.…”

Section: Introductionmentioning

confidence: 99%

On optimal input design in system identification for model predictive control

Larsson

Annergren

Hjalmarsson

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-This paper considers a method for optimal input design in system identification for control. The approach addresses model predictive control (MPC). The objective of the framework is to provide the user with a model which guarantees that a specified control performance is achieved, with a given probability. We see that, even though the system is nonlinear, using linear theory in the input design can reduce the experimental effort. The method is illustrated in a minimum power input signal design in system identification of a water tank system.

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On optimal input design in system identification for control

Cited by 25 publications

References 25 publications

A Study of Relation Between Robustness of Parameter Estimation and Model Order in System Identification

A Study of Relation Between Robustness of Parameter Estimation and Model Order in System Identification

Optimal Excitation for Nonparametric Identification of Viscoelastic Materials

On optimal input design in system identification for model predictive control

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