Results 1  10
of
45
Robust optimal experiment design for system identification
 AUTOMATICA
, 2007
"... This paper develops the idea of min–max robust experiment design for dynamic system identification. The idea of min–max experiment design has been explored in the statistics literature. However, the technique is virtually unknown by the engineering community and, accordingly, there has been little p ..."
Abstract

Cited by 30 (12 self)
 Add to MetaCart
This paper develops the idea of min–max robust experiment design for dynamic system identification. The idea of min–max experiment design has been explored in the statistics literature. However, the technique is virtually unknown by the engineering community and, accordingly, there has been little prior work on examining its properties when applied to dynamic system identification. This paper initiates an exploration of these ideas. The paper considers linear systems with energy (or power) bounded inputs. We assume that the parameters lie in a given compact set and optimise the worst case over this set. We also provide a detailed analysis of the solution for an illustrative one parameter example and propose a convex optimisation algorithm that can be applied more generally to a discretised approximation to the design problem. We also examine the role played by different design criteria and present a simulation example illustrating the merits of the proposed approach.
Optimal experimental design and some related control problems
, 2008
"... This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control. The mathematical background of optimal experiment ..."
Abstract

Cited by 26 (0 self)
 Add to MetaCart
This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control. The mathematical background of optimal experimental design is briefly presented, and the role of experimental design in the asymptotic properties of estimators is emphasized. Although most of the paper concerns parametric models, some results are also presented for statistical learning and prediction with nonparametric models.
On the Equivalence of Least Costly and Traditional Experiment Design for Control
 AUTOMATICA
, 2008
"... In this paper we establish the equivalence between least costly and traditional experiment design for control. We consider experiment design problems for both open and closed loop systems. In open loop, equivalence is established for three specific cases, relating to different parametrisations of th ..."
Abstract

Cited by 8 (2 self)
 Add to MetaCart
In this paper we establish the equivalence between least costly and traditional experiment design for control. We consider experiment design problems for both open and closed loop systems. In open loop, equivalence is established for three specific cases, relating to different parametrisations of the covariance expression (i.e. finite and high order approximations) and model structure (i.e. dependent and independently parameterised plant and noise models). In the closed loop setting, we consider only finite order covariance expressions. H ∞ performance specifications for control are used to determine the bounds on the covariance expression for both the open and closed loop cases.
Input design: from openloop to controloriented design
 In: Proc. IFAC Symposium on System Identification
, 2006
"... Abstract: In this paper we briefly review the evolution of the main tools and results for optimal experiment design for system identification. The initial work dates back to the seventies and focused on the accuracy of the parameters of the inputoutput transfer function estimate. In the eighties, n ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
(Show Context)
Abstract: In this paper we briefly review the evolution of the main tools and results for optimal experiment design for system identification. The initial work dates back to the seventies and focused on the accuracy of the parameters of the inputoutput transfer function estimate. In the eighties, new formulas for the variance of transfer function estimates based on highorder model approximations led to the first goaloriented experiment design results. The recent trend is to address controloriented optimal design questions using the more accurate parameter covariance formulas for finite order models.
Robust experiment design
, 2012
"... This paper focuses on the problem of robust experiment design, i.e., how to design an input signal which gives relatively good estimation performance over a large number of systems and model structures. Specifically, we formulate the robust experiment design problem utilizing fundamental limitat ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
This paper focuses on the problem of robust experiment design, i.e., how to design an input signal which gives relatively good estimation performance over a large number of systems and model structures. Specifically, we formulate the robust experiment design problem utilizing fundamental limitations on the variance of estimated parametric models as constraints. Using this formulationwe design an input signal for situations where only diffuse a priori information is known about the system. Furthermore, we present a robust version of the unprejudiced optimal input design problem. To achieve this, we first develop a closed form solution for the input spectrum which minimizes the maximum weighted integral of the variance of the frequency response estimate over all model structures.
Datadriven model reference control with asymptotically guaranteed
"... stability ..."
(Show Context)
Chance constrained input design
 in Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDCECC11), 2011
"... Abstract — In this paper the following problem is studied: design an input signal with the property that the estimated model based on this signal satisfies a given performance level with a prescribed probability. This problem is mathematically translated into a chance constrained optimization proble ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Abstract — In this paper the following problem is studied: design an input signal with the property that the estimated model based on this signal satisfies a given performance level with a prescribed probability. This problem is mathematically translated into a chance constrained optimization problem, which is typically non convex. To solve it, several convex approximations are proposed and compared. I.
Optimal experiment design with diffuse prior information
 in Proceedings of the European Control Conference (ECC
, 2007
"... Abstract — In system identification one always aims to learn as much as possible about a system from a given observation period. This has led to ongoing interest in the problem of optimal experiment design. Not surprisingly, the more one knows about a system the more focused the experiment can be. ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
Abstract — In system identification one always aims to learn as much as possible about a system from a given observation period. This has led to ongoing interest in the problem of optimal experiment design. Not surprisingly, the more one knows about a system the more focused the experiment can be. Indeed, many procedures for ‘optimal ’ experiment design depend, paradoxically, on exact knowledge of the system parameters. This has motivated recent research on, so called, ‘robust ’ experiment design where one assumes only partial prior knowledge of the system. Here we go further and study the question of optimal experiment design when the apriori information about the system is diffuse. We show that bandlimited ‘1/f ’ noise is optimal for a particular choice of cost function. I.
Estimating Disturbances and Model Uncertainty in Model Validation for Robust Control,”
 47th IEEE Conference on Decision and Control (CDC
, 2008
"... AbstractDeterministic approaches to model validation for robust control are investigated. In common deterministic model validation approaches, a tradeoff between disturbances and model uncertainty is present, resulting in an illposed problem. In this paper, an approach to model validation is pre ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
AbstractDeterministic approaches to model validation for robust control are investigated. In common deterministic model validation approaches, a tradeoff between disturbances and model uncertainty is present, resulting in an illposed problem. In this paper, an approach to model validation is presented that attempts to remedy the illposedness. By employing accurate, nonparametric, deterministic disturbance models in conjunction with enforcing averaging properties of deterministic disturbances, a novel framework enabling model validation for robust control is obtained. The approach results in a realistically estimated model uncertainty and a disturbance model, and is illustrated in a simulation example.
SENSOR NETWORK SCHEDULING FOR IDENTIFICATION OF SPATIALLY DISTRIBUTED PROCESSES
"... The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, the Dsoptimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a twodimensional diffusion process.