Skip to main content

S. Gunnarsson

Followers

9

Following

1

Co-author

1

Public Views

Dublin City University

Kansas State University

UCSC

Armando Marques-Guedes

UNL - New University of Lisbon

Macquarie University

The Ohio State University

Eastern Mediterranean University

Bond University

Patrick Blessinger

St. John's University

Professor Karin Murris

Oulun yliopisto

Interests

Uploads

Papers by S. Gunnarsson

Robustness in Adaptive Control from a Frequency Domain System Identification Perspective

IFAC Proceedings Volumes, 1990

In this paper we study robustness in adaptive control from a frequency domain system identificati... more In this paper we study robustness in adaptive control from a frequency domain system identification perspective. Using a frequency domain description of identified models we study how a model identified in closed loop is influenced by the feedback and prefiltering in the identification procedure. We propose that the prefiltering in the identification part of the adaptive algorithm shall be used to secure a good model in a predetermined frequency interval. This shall be combined with monitoring of the nominal control system properties, to assure that the pole placement regulator is robust with respect to the identified model.

Framstående utbildningsmiljö–Hur blir man det?

Model free data driven optimal tuning of controller parameters

On the performance of the recursive least squares method in the presence of bounded disturbances

[1991] Proceedings of the 30th IEEE Conference on Decision and Control

ABSTRACT

Model Quality in Recursive Identification of Time Varying Systems with ARX Structure

1990 American Control Conference, 1990

Recursive identification of time varying systems with ARX structures is considered, with the inte... more Recursive identification of time varying systems with ARX structures is considered, with the interest focused on the quality of the estimated transfer functions. Three recursive identification algorithms are considered, the least mean squares (LMS) algorithm, the recursive least squares (RLS) algorithm and the Kalman filter respectively. The model quality is studied in terms of algorithm design variables, input and disturbance signal properties and variations of the true system. Using asymptotic methods approximate expressions for the model quality are derived.

On Covariance Modification and Re-Gularization in Recursive Least Squa Res Identification

IFAC Proceedings Volumes, 1994

In this paper the relationships between covariance modification and regularization in recursive l... more In this paper the relationships between covariance modification and regularization in recursive least squares identification are investigated. An update equation for the information matrix is derived and it is shown how regularization of the information matrix can be expressed as a particular type of cO\'ariance matrix modification. The paper also presents an analysis of the effects of a covariance modification for obtaining regularization that was proposed in Salgado et al., 1988.

Some Aspects of an Optimization Approach Learning Controll

On Some Updating Strategies in Recursive Least Squares Identification

Computer Supported Learning and Assessment in Engineering Education

Some aspects of computer support in learning and assessment in engineering education are discusse... more Some aspects of computer support in learning and assessment in engineering education are discussed. It is emphasized that the use of computer support, like e.g. simulations, computations, visualizations, has to be closely connected to the formulation of the expected learning outcomes and the assessment methods. Some examples of computer support are related to the CDIO Syllabus. Some experiences from more that two decades of computer aided learning and assessment within the Division of Automatic Control at Linkoping University are presented

Adaptive control with impact detection of a hydraulic actuator connected to a flexible mechanical structure

Adaptive Control with Impact Detection of a Hydraulic Actuator Connected to a Flexible Mechanical... more

LQG control of a flexible mechanical structure using hydraulic actuators

Frequency domain aspects of modeling and control in adaptive systems

In this thesis various aspects of modeling and control in adaptive systems are presented from a f... more In this thesis various aspects of modeling and control in adaptive systems are presented from a frequency domain viewpoint.The thesis consists of three parts, where the first part contains a general introduction and background information concerning the problems that will be treated. In the second part some recursive identification algorithms are studied with respect to their ability to track time-varying systems and their disturbance sensitivity. Simple and illustrative frequency domain expressions that describe these properties are derived using asymptotic methods. The algorithms that are treated are the constant gain gradient (LMS) algorithm, the recursive least squares algorithm with constant forgetting factor and the Kalman filter respectively. The behavior of these methods when applied to FIR and ARX systems are studied. In the third part of the thesis adaptive control based on low order models is studied. The adaptive control algorithm that is investigated is the recursive least squares algorithm combined with pole placement regulator design. Starting from frequency domain expressions, that describe how a low order model obtained by system identification approximates a higher order system, the consequences for adaptive control are investigated. It is shown how a proper use of filtering in the identification part of the adaptive regulator can improve the robustness properties of the adaptive regulator with respect to unmodelled dynamics.

Frequency domain tracking characteristics of adaptive algorithms

The problem of tracking time-varying linear systems is discussed. The focus is on the model quali... more The problem of tracking time-varying linear systems is discussed. The focus is on the model quality in terms of the mean square error (MSE) between the true (momentary) transfer function and the estimated one. This MSE is thus a function of frequency. The exact expression for the MSE is complicated, but simple expressions that are asymptotic in the model order

Some asymptotic results in recursive identification using laguerre models

International Journal of Adaptive Control and Signal Processing, 1991

This paper deals with recursive identification of time‐varying systems using Laguerre models. Lag... more This paper deals with recursive identification of time‐varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman‐filter‐like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.

Some Experiences of the Use of Iterative Learning Control for Performance Improvement in Robot Control Systems

IFAC Proceedings Volumes, 1997

Some aspects of the use of learning control for improved performance in robot control systems are... more Some aspects of the use of learning control for improved performance in robot control systems are studied. The learning control signal is used in combination with conventional feedback and feed-forward control. The e ects of disturbances, unmodeled dynamics and friction are studied theoretically and in simulations of a simpli ed model of a robot arm. Convergence and robustness aspects of the choice of lters in the updating scheme of the learning control signal are studied.

LQG Control of an Hydraulic Actuator with a Flexible Mechanical Load

IFAC Proceedings Volumes, 1993

This paper presents a control system based on linear quadratic state feedback and state estimatio... more This paper presents a control system based on linear quadratic state feedback and state estimation for an hydraulic actuator with a exible mechanical load. The purpose of the control system is to improve the dynamic properties by reducing the oscillatory behavior of the load and to eliminate the steady state error in the load position caused by external disturbances. Using a simpli ed linear model a control system is developed and evaluated using simulations and experiments.

Simulation based Evaluation of Fault Detection Algorithms with Applications to Wear Diagnosis in Manipulators

IFAC Proceedings Volumes, 2014

Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are u... more Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches are proposed in an attempt to determine: i) which disturbances affect the output of an FDA the most; ii) how to compare the performance of different FDAs; and iii) which combinations of fault change size and disturbances variations are allowed to achieve satisfactory performance. The ideas presented are inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection. The approaches are illustrated for the problem of wear diagnosis in manipulators where three FDAs are considered. The application study reveals that disturbances caused by variations in temperature and payload mass error affect the FDAs the most. It is also shown how the size of these disturbances delimit the capacity of an FDA to relate to wear changes. Further comparison of the FDAs reveal which performs "best" in average.

Enhancing Feedback Control Scheduling Performance by On-line Quantification and Suppression of Measurement Disturbance

11th IEEE Real Time and Embedded Technology and Applications Symposium

In the control of continuous and physical systems, the controlled system is sampled sufficiently ... more In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.

Nonlinear Grey-Box Identification of Industrial Robots Containing Flexibilities

IFAC Proceedings Volumes, 2005

Nonlinear grey-box identification of industrial robots is considered. A three-step identification... more Nonlinear grey-box identification of industrial robots is considered. A three-step identification procedure is proposed in which parameters for rigid body dynamics, friction, and flexibilities can be identified only using measurements on the motor. In the first two steps, good initial parameter estimates are derived which are used in the last step, where the parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is exemplified using real data from an experimental industrial robot.

A frequency domain analysis of a second order iterative learning control algorithm

Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)

Robustness in Adaptive Control from a Frequency Domain System Identification Perspective

IFAC Proceedings Volumes, 1990

In this paper we study robustness in adaptive control from a frequency domain system identificati... more In this paper we study robustness in adaptive control from a frequency domain system identification perspective. Using a frequency domain description of identified models we study how a model identified in closed loop is influenced by the feedback and prefiltering in the identification procedure. We propose that the prefiltering in the identification part of the adaptive algorithm shall be used to secure a good model in a predetermined frequency interval. This shall be combined with monitoring of the nominal control system properties, to assure that the pole placement regulator is robust with respect to the identified model.

Framstående utbildningsmiljö–Hur blir man det?

Model free data driven optimal tuning of controller parameters

On the performance of the recursive least squares method in the presence of bounded disturbances

[1991] Proceedings of the 30th IEEE Conference on Decision and Control

ABSTRACT

Model Quality in Recursive Identification of Time Varying Systems with ARX Structure

1990 American Control Conference, 1990

Recursive identification of time varying systems with ARX structures is considered, with the inte... more Recursive identification of time varying systems with ARX structures is considered, with the interest focused on the quality of the estimated transfer functions. Three recursive identification algorithms are considered, the least mean squares (LMS) algorithm, the recursive least squares (RLS) algorithm and the Kalman filter respectively. The model quality is studied in terms of algorithm design variables, input and disturbance signal properties and variations of the true system. Using asymptotic methods approximate expressions for the model quality are derived.

On Covariance Modification and Re-Gularization in Recursive Least Squa Res Identification

IFAC Proceedings Volumes, 1994

In this paper the relationships between covariance modification and regularization in recursive l... more In this paper the relationships between covariance modification and regularization in recursive least squares identification are investigated. An update equation for the information matrix is derived and it is shown how regularization of the information matrix can be expressed as a particular type of cO\'ariance matrix modification. The paper also presents an analysis of the effects of a covariance modification for obtaining regularization that was proposed in Salgado et al., 1988.

Some Aspects of an Optimization Approach Learning Controll

On Some Updating Strategies in Recursive Least Squares Identification

Computer Supported Learning and Assessment in Engineering Education

Some aspects of computer support in learning and assessment in engineering education are discusse... more Some aspects of computer support in learning and assessment in engineering education are discussed. It is emphasized that the use of computer support, like e.g. simulations, computations, visualizations, has to be closely connected to the formulation of the expected learning outcomes and the assessment methods. Some examples of computer support are related to the CDIO Syllabus. Some experiences from more that two decades of computer aided learning and assessment within the Division of Automatic Control at Linkoping University are presented

Adaptive control with impact detection of a hydraulic actuator connected to a flexible mechanical structure

Adaptive Control with Impact Detection of a Hydraulic Actuator Connected to a Flexible Mechanical... more

LQG control of a flexible mechanical structure using hydraulic actuators

Frequency domain aspects of modeling and control in adaptive systems

In this thesis various aspects of modeling and control in adaptive systems are presented from a f... more In this thesis various aspects of modeling and control in adaptive systems are presented from a frequency domain viewpoint.The thesis consists of three parts, where the first part contains a general introduction and background information concerning the problems that will be treated. In the second part some recursive identification algorithms are studied with respect to their ability to track time-varying systems and their disturbance sensitivity. Simple and illustrative frequency domain expressions that describe these properties are derived using asymptotic methods. The algorithms that are treated are the constant gain gradient (LMS) algorithm, the recursive least squares algorithm with constant forgetting factor and the Kalman filter respectively. The behavior of these methods when applied to FIR and ARX systems are studied. In the third part of the thesis adaptive control based on low order models is studied. The adaptive control algorithm that is investigated is the recursive least squares algorithm combined with pole placement regulator design. Starting from frequency domain expressions, that describe how a low order model obtained by system identification approximates a higher order system, the consequences for adaptive control are investigated. It is shown how a proper use of filtering in the identification part of the adaptive regulator can improve the robustness properties of the adaptive regulator with respect to unmodelled dynamics.

Frequency domain tracking characteristics of adaptive algorithms

The problem of tracking time-varying linear systems is discussed. The focus is on the model quali... more The problem of tracking time-varying linear systems is discussed. The focus is on the model quality in terms of the mean square error (MSE) between the true (momentary) transfer function and the estimated one. This MSE is thus a function of frequency. The exact expression for the MSE is complicated, but simple expressions that are asymptotic in the model order

Some asymptotic results in recursive identification using laguerre models

International Journal of Adaptive Control and Signal Processing, 1991

This paper deals with recursive identification of time‐varying systems using Laguerre models. Lag... more This paper deals with recursive identification of time‐varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman‐filter‐like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.

Some Experiences of the Use of Iterative Learning Control for Performance Improvement in Robot Control Systems

IFAC Proceedings Volumes, 1997

Some aspects of the use of learning control for improved performance in robot control systems are... more Some aspects of the use of learning control for improved performance in robot control systems are studied. The learning control signal is used in combination with conventional feedback and feed-forward control. The e ects of disturbances, unmodeled dynamics and friction are studied theoretically and in simulations of a simpli ed model of a robot arm. Convergence and robustness aspects of the choice of lters in the updating scheme of the learning control signal are studied.

LQG Control of an Hydraulic Actuator with a Flexible Mechanical Load

IFAC Proceedings Volumes, 1993

This paper presents a control system based on linear quadratic state feedback and state estimatio... more This paper presents a control system based on linear quadratic state feedback and state estimation for an hydraulic actuator with a exible mechanical load. The purpose of the control system is to improve the dynamic properties by reducing the oscillatory behavior of the load and to eliminate the steady state error in the load position caused by external disturbances. Using a simpli ed linear model a control system is developed and evaluated using simulations and experiments.

Simulation based Evaluation of Fault Detection Algorithms with Applications to Wear Diagnosis in Manipulators

IFAC Proceedings Volumes, 2014

Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are u... more Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches are proposed in an attempt to determine: i) which disturbances affect the output of an FDA the most; ii) how to compare the performance of different FDAs; and iii) which combinations of fault change size and disturbances variations are allowed to achieve satisfactory performance. The ideas presented are inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection. The approaches are illustrated for the problem of wear diagnosis in manipulators where three FDAs are considered. The application study reveals that disturbances caused by variations in temperature and payload mass error affect the FDAs the most. It is also shown how the size of these disturbances delimit the capacity of an FDA to relate to wear changes. Further comparison of the FDAs reveal which performs "best" in average.

Enhancing Feedback Control Scheduling Performance by On-line Quantification and Suppression of Measurement Disturbance

11th IEEE Real Time and Embedded Technology and Applications Symposium

In the control of continuous and physical systems, the controlled system is sampled sufficiently ... more In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.

Nonlinear Grey-Box Identification of Industrial Robots Containing Flexibilities

IFAC Proceedings Volumes, 2005

Nonlinear grey-box identification of industrial robots is considered. A three-step identification... more Nonlinear grey-box identification of industrial robots is considered. A three-step identification procedure is proposed in which parameters for rigid body dynamics, friction, and flexibilities can be identified only using measurements on the motor. In the first two steps, good initial parameter estimates are derived which are used in the last step, where the parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is exemplified using real data from an experimental industrial robot.

A frequency domain analysis of a second order iterative learning control algorithm

Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)