M. Gevers - Academia.edu (original) (raw)
Papers by M. Gevers
Lecture Notes in Control and Information Sciences
2008 47th IEEE Conference on Decision and Control, 2008
This paper examines an ongoing research and development project concerned with identification, co... more This paper examines an ongoing research and development project concerned with identification, control and diagnosis in aircraft engines. Within these topics, this paper will focus on physical diagnosis, where one of the main motivations of the project is to introduce innovation and update techniques currently used in industry. Several fault diagnosis methods are considered and com ared in terms of their possible application to the project. Further %etails are provided for the parametric statistical approach which, according to this initial study, is one approach flexible enough and with good foundations in order to achieve the desired objectives.
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998
The most classical way of obtaining a low order model- based controller for a high order system i... more The most classical way of obtaining a low order model- based controller for a high order system is to apply closed loop reduction techniques to an accurate high order model or controller of the plant. The recent lit- erature on identification for control has promoted the idea that an alternative way is to directly identify a low order model using
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998
The most classical way of obtaining a low order model- based controller for a high order system i... more The most classical way of obtaining a low order model- based controller for a high order system is to apply closed loop reduction techniques to an accurate high order model or controller of the plant. The recent lit- erature on identification for control has promoted the idea that an alternative way is to directly identify a low order model using
Proceedings of 1995 American Control Conference - ACC'95, 1995
We demonstrate that some recently proposed iterative identi cation and control design schemes do ... more We demonstrate that some recently proposed iterative identi cation and control design schemes do not necessarily converge to a local minimum of the design objective in the case of a restricted complexity model. There is, however, a link between these approaches and a recently proposed iterative optimization based control design procedure based on experimental data. We show that if the achieved and the desired output responses are perfectly matched, the schemes are (essentially) equivalent under noise free conditions.
Proceedings of 35th IEEE Conference on Decision and Control, 1996
Using the dual Youla parametrizations of controller-based coprime factor plant perturbations and ... more Using the dual Youla parametrizations of controller-based coprime factor plant perturbations and plant-based coprime factor controller perturbations, we characterize the set of all plants that have the same optimal LQG or MV controller
Proceedings of the 28th IEEE Conference on Decision and Control, 1989
ABSTRACT
European Journal of Control, 2005
This paper presents the author's views on the development of identification for control. The pape... more This paper presents the author's views on the development of identification for control. The paper reviews the emergence of this subject as a specific topic over the last 15 years, at the boundary between system identification and robust control. It shows how the early focus on identification of control-oriented nominal models has progressively shifted towards the design of control-oriented uncertainty sets. This recent trend has given rise to an important revival of interest in experiment design issues in system identification. Some recent results on experiment design are presented.
16th IFAC Symposium on System Identification, 2012
This paper analyzes two recent methods for the nonparametric estimation of the Frequency Response... more This paper analyzes two recent methods for the nonparametric estimation of the Frequency Response Function (FRF) from input-output data using Prediction Error identification. Such FRF estimate can be the main goal of the identification exercise, or it can be a tool for the computation of a nonparametric estimate of the noise spectrum. We show that the choice of the method depends on the signal to noise ratio and on the objective. The method that delivers the best FRF estimate may not deliver the best estimate of the noise spectrum. Our theoretical analysis is illustrated by simulations.
Lecture Notes in Control and Information Sciences, 1999
Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter un... more Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter uncertainty set, from which a corresponding uncertainty set can be constructed in the space of transfer functions. We display the role of the experimental conditions used for validation on the shape of this validated set, and we connect a measure of the size of this set to the stability margin of a controller designed from the nominal model. This allows one to check stability robustness for the validated model set and to propose guidelines for validation design.
European Journal of Control, 1995
IEEE Conference on Decision and Control and European Control Conference, 2011
The Local Polynomial Method (LPM) is a recently developed procedure for nonparametric estimation ... more The Local Polynomial Method (LPM) is a recently developed procedure for nonparametric estimation of the Frequency Response Function (FRF) of a linear system. Compared with other nonparametric FRF estimates based on windowing techniques, it has proved to be remarkably efficient in reducing the leakage errors caused by the application of Fourier transform techniques to non periodic data. In this paper we propose a modification of the LPM that takes account explicitly of constraints between the coefficients of the polynomials at neighbouring frequencies. This new variant contributes a new and significant reduction in the Mean Square Error of the FRF estimates. We also discuss the effects of the various design parameters on the accuracy of the estimates.
The Riccati Equation, 1991
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992
The optimal finite word length (FWL) design problem of state-space filters is investigated. Inste... more The optimal finite word length (FWL) design problem of state-space filters is investigated. Instead of the usual L1/L2-mixed sensitivity measure, it is argued that a sensitivity measure based on the L2 norm only is natural and reasonable. The minimization problem of this newly defined sensitivity measure is studied. The set of optimal realizations minimizing this measure is characterized. It is
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999
This paper focuses on the validation of a controller that has been designed from an unbiased mode... more This paper focuses on the validation of a controller that has been designed from an unbiased model of the true system, identified either in open-loop or in closed-loop using a prediction error framework. A controller is said to be validated if it stabilizes all models in a parametric uncertainty set containing the parameters of the true system with some prescribed probability. This uncertainty set is deduced from the covariance matrix of the parameters of the identified model. Our contribution is to embed this set in the smallest possible overbounding coprime factor uncertainty set. This then allows us to use the results of mainstream robust control theory such as the Vinnicombe gap between plants and its related stability theorems.
Robust Adaptive Control, 1989
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998
This paper highlights the role of feedback in the identi cation and validation of a model, when t... more This paper highlights the role of feedback in the identi cation and validation of a model, when that model is to be used for control design. Feedback reduces the uncertainty of the estimated model in frequency bands that are critical for control design. Thus, in the presence of noise, closed loop identi cation for control leads to less conservative robust control designs than open loop identi cation of validated full order models, followed by controller design.
Lecture Notes in Control and Information Sciences
2008 47th IEEE Conference on Decision and Control, 2008
This paper examines an ongoing research and development project concerned with identification, co... more This paper examines an ongoing research and development project concerned with identification, control and diagnosis in aircraft engines. Within these topics, this paper will focus on physical diagnosis, where one of the main motivations of the project is to introduce innovation and update techniques currently used in industry. Several fault diagnosis methods are considered and com ared in terms of their possible application to the project. Further %etails are provided for the parametric statistical approach which, according to this initial study, is one approach flexible enough and with good foundations in order to achieve the desired objectives.
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998
The most classical way of obtaining a low order model- based controller for a high order system i... more The most classical way of obtaining a low order model- based controller for a high order system is to apply closed loop reduction techniques to an accurate high order model or controller of the plant. The recent lit- erature on identification for control has promoted the idea that an alternative way is to directly identify a low order model using
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998
The most classical way of obtaining a low order model- based controller for a high order system i... more The most classical way of obtaining a low order model- based controller for a high order system is to apply closed loop reduction techniques to an accurate high order model or controller of the plant. The recent lit- erature on identification for control has promoted the idea that an alternative way is to directly identify a low order model using
Proceedings of 1995 American Control Conference - ACC'95, 1995
We demonstrate that some recently proposed iterative identi cation and control design schemes do ... more We demonstrate that some recently proposed iterative identi cation and control design schemes do not necessarily converge to a local minimum of the design objective in the case of a restricted complexity model. There is, however, a link between these approaches and a recently proposed iterative optimization based control design procedure based on experimental data. We show that if the achieved and the desired output responses are perfectly matched, the schemes are (essentially) equivalent under noise free conditions.
Proceedings of 35th IEEE Conference on Decision and Control, 1996
Using the dual Youla parametrizations of controller-based coprime factor plant perturbations and ... more Using the dual Youla parametrizations of controller-based coprime factor plant perturbations and plant-based coprime factor controller perturbations, we characterize the set of all plants that have the same optimal LQG or MV controller
Proceedings of the 28th IEEE Conference on Decision and Control, 1989
ABSTRACT
European Journal of Control, 2005
This paper presents the author's views on the development of identification for control. The pape... more This paper presents the author's views on the development of identification for control. The paper reviews the emergence of this subject as a specific topic over the last 15 years, at the boundary between system identification and robust control. It shows how the early focus on identification of control-oriented nominal models has progressively shifted towards the design of control-oriented uncertainty sets. This recent trend has given rise to an important revival of interest in experiment design issues in system identification. Some recent results on experiment design are presented.
16th IFAC Symposium on System Identification, 2012
This paper analyzes two recent methods for the nonparametric estimation of the Frequency Response... more This paper analyzes two recent methods for the nonparametric estimation of the Frequency Response Function (FRF) from input-output data using Prediction Error identification. Such FRF estimate can be the main goal of the identification exercise, or it can be a tool for the computation of a nonparametric estimate of the noise spectrum. We show that the choice of the method depends on the signal to noise ratio and on the objective. The method that delivers the best FRF estimate may not deliver the best estimate of the noise spectrum. Our theoretical analysis is illustrated by simulations.
Lecture Notes in Control and Information Sciences, 1999
Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter un... more Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter uncertainty set, from which a corresponding uncertainty set can be constructed in the space of transfer functions. We display the role of the experimental conditions used for validation on the shape of this validated set, and we connect a measure of the size of this set to the stability margin of a controller designed from the nominal model. This allows one to check stability robustness for the validated model set and to propose guidelines for validation design.
European Journal of Control, 1995
IEEE Conference on Decision and Control and European Control Conference, 2011
The Local Polynomial Method (LPM) is a recently developed procedure for nonparametric estimation ... more The Local Polynomial Method (LPM) is a recently developed procedure for nonparametric estimation of the Frequency Response Function (FRF) of a linear system. Compared with other nonparametric FRF estimates based on windowing techniques, it has proved to be remarkably efficient in reducing the leakage errors caused by the application of Fourier transform techniques to non periodic data. In this paper we propose a modification of the LPM that takes account explicitly of constraints between the coefficients of the polynomials at neighbouring frequencies. This new variant contributes a new and significant reduction in the Mean Square Error of the FRF estimates. We also discuss the effects of the various design parameters on the accuracy of the estimates.
The Riccati Equation, 1991
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992
The optimal finite word length (FWL) design problem of state-space filters is investigated. Inste... more The optimal finite word length (FWL) design problem of state-space filters is investigated. Instead of the usual L1/L2-mixed sensitivity measure, it is argued that a sensitivity measure based on the L2 norm only is natural and reasonable. The minimization problem of this newly defined sensitivity measure is studied. The set of optimal realizations minimizing this measure is characterized. It is
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999
This paper focuses on the validation of a controller that has been designed from an unbiased mode... more This paper focuses on the validation of a controller that has been designed from an unbiased model of the true system, identified either in open-loop or in closed-loop using a prediction error framework. A controller is said to be validated if it stabilizes all models in a parametric uncertainty set containing the parameters of the true system with some prescribed probability. This uncertainty set is deduced from the covariance matrix of the parameters of the identified model. Our contribution is to embed this set in the smallest possible overbounding coprime factor uncertainty set. This then allows us to use the results of mainstream robust control theory such as the Vinnicombe gap between plants and its related stability theorems.
Robust Adaptive Control, 1989
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998
This paper highlights the role of feedback in the identi cation and validation of a model, when t... more This paper highlights the role of feedback in the identi cation and validation of a model, when that model is to be used for control design. Feedback reduces the uncertainty of the estimated model in frequency bands that are critical for control design. Thus, in the presence of noise, closed loop identi cation for control leads to less conservative robust control designs than open loop identi cation of validated full order models, followed by controller design.