Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulation of control laws and allow experiments tobe performed in a simulation environment. For complex systems, parts of themodel may be missing which increases uncertainties and limits practical applications.Using input-output measurements makes it possible to estimate themodel, but requires the measurements to be informative. The idea in so-calledinput design is to find an input sequence for the system such that the measurementsreveal the properties and dynamics of the true system as much aspossible. This is commonly formulated as an optimization problem.This thesis focuses on formulating an optimization algorithm for inputdesign, which is imple...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
In designing a control system the knowledge of the dynamics of the physical plant is quite essential...
Parameter identification experiments deliver an identified model together with an ellipsoidal uncert...
Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulat...
There are many aspects to consider when designing system identification experiments in control appli...
Abstract — This paper considers a method for optimal input design in system identification for contr...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
System identification is about constructing and validating modelsfrom measured data. When designing ...
When system identification methods are used to construct mathematical models of real systems, it is ...
An optimal feedback input design method for active parameter identification of dynamic nonlinear sys...
The main part of this thesis focuses on optimal experiment design for system identification within t...
This thesis is divided into two main parts. The first part considers application-oriented input desi...
Model predictive control (MPC) makes use of a model of the system, therefore performances are highly...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
In designing a control system the knowledge of the dynamics of the physical plant is quite essential...
Parameter identification experiments deliver an identified model together with an ellipsoidal uncert...
Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulat...
There are many aspects to consider when designing system identification experiments in control appli...
Abstract — This paper considers a method for optimal input design in system identification for contr...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
System identification is about constructing and validating modelsfrom measured data. When designing ...
When system identification methods are used to construct mathematical models of real systems, it is ...
An optimal feedback input design method for active parameter identification of dynamic nonlinear sys...
The main part of this thesis focuses on optimal experiment design for system identification within t...
This thesis is divided into two main parts. The first part considers application-oriented input desi...
Model predictive control (MPC) makes use of a model of the system, therefore performances are highly...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
In designing a control system the knowledge of the dynamics of the physical plant is quite essential...
Parameter identification experiments deliver an identified model together with an ellipsoidal uncert...