This paper proposes a new Linear Fractional Transformation (LFT) modeling approach for uncertain Linear Parameter Varying (LPV) multibody systems with parameter-dependent equilibrium. Traditional multibody approaches, which consist in building the nonlinear model of the whole structure and linearizing it around equilibrium after a numerical trimming, do not allow to isolate parametric variations with the LFT form. Although additional techniques, such as polynomial fitting or symbolic linearization, can provide an LFT model, they may be time-consuming or miss worst-case configurations. The proposed approach relies on the trimming and linearization of the equations at the substructure level, before assembly of the multibody structure, which a...
The notion of balanced realizations and balanced truncation model reduction, including guaranteed er...
Abstract: In this paper, a nonlinear modelling framework is presented that combines symbolic modelli...
We present model reduction methods with guaranteed error bounds for systems represented by a Linear ...
This brief proposes a new linear fractional transformation (LFT) modeling approach for uncertain lin...
Many physical dynamical systems can be described by differential equations depending on parameters,...
Linear Fractional Transformations (LFTs) are objects of study for robust and Linear Parameter Varyin...
The paper presents a general approach to approximate a nonlinear system by a linear fractional repre...
Robust control system analysis and design is based on an uncertainty description, called a linear fr...
Robust control system analysis and design is based on an uncertainty description, called a linear fr...
We present a general approach to generate a linear parametric state-space model, which approximates ...
ABSTRACT: This paper presents a procedure for parametric uncertainty modeling of a highly nonlinear ...
In this thesis, methods for rapidly developing LPV/LFT models for aerospace applications are propose...
This paper introduces a novel method to control linear parameter varying (LPV) systems by employing ...
We present a general approach to generate a linear parametric state-space model, which approximates ...
A general approach to generate a linear parametric state-space model is presented in the following w...
The notion of balanced realizations and balanced truncation model reduction, including guaranteed er...
Abstract: In this paper, a nonlinear modelling framework is presented that combines symbolic modelli...
We present model reduction methods with guaranteed error bounds for systems represented by a Linear ...
This brief proposes a new linear fractional transformation (LFT) modeling approach for uncertain lin...
Many physical dynamical systems can be described by differential equations depending on parameters,...
Linear Fractional Transformations (LFTs) are objects of study for robust and Linear Parameter Varyin...
The paper presents a general approach to approximate a nonlinear system by a linear fractional repre...
Robust control system analysis and design is based on an uncertainty description, called a linear fr...
Robust control system analysis and design is based on an uncertainty description, called a linear fr...
We present a general approach to generate a linear parametric state-space model, which approximates ...
ABSTRACT: This paper presents a procedure for parametric uncertainty modeling of a highly nonlinear ...
In this thesis, methods for rapidly developing LPV/LFT models for aerospace applications are propose...
This paper introduces a novel method to control linear parameter varying (LPV) systems by employing ...
We present a general approach to generate a linear parametric state-space model, which approximates ...
A general approach to generate a linear parametric state-space model is presented in the following w...
The notion of balanced realizations and balanced truncation model reduction, including guaranteed er...
Abstract: In this paper, a nonlinear modelling framework is presented that combines symbolic modelli...
We present model reduction methods with guaranteed error bounds for systems represented by a Linear ...