Add to ORKGInternational audienceThis work deals with a new method for computing Lyapunov functions represented by neural networks for autonomous nonlinear systems. Based on the Lyapunov theory and the notion of domain of attraction, we propose an optimization method for determining a Lyapunov function modelled by a neural network while maximizing the domain of attraction. The potential of the proposed method is demonstrated by simulation examples
This thesis implements a recently proposed algebraic methodology for optimal domain of attraction es...
AbstractThe stability is studied of a class of nonlinear dynamical systems which possess many nonlin...
The stability is studied of a class of nonlinear dynamical systems which possess many nonlinearities...
International audienceThis work deals with a new method for computing Lyapunov functions represented...
International audienceThis work deals with a new method for computing Lyapunov functions represented...
In this paper we present various theoretical and computational methods for estimating the domain of ...
We propose an automatic and formally sound method for synthesising Lyapunov functions for the asympt...
This paper investigates Lyapunov approaches to expand the domain of attraction (DA) of nonlinear aut...
Machine learning-based methodologies have recently been adapted to solve control problems. The Neura...
In this paper a methodology for the estimation of domains of attraction of stable equilibriums based...
In this paper, two methods for constructing systems of ordinary differential equations realizing any...
This paper proposes a strategy for estimating the domain of attraction (DA) for non-polynomial syste...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
Nowadays, solving constrained engineering problems related to optimization approaches is an attracti...
The stability is studied of a class of nonlinear dynamical systems which possess many nonlinearities...
This thesis implements a recently proposed algebraic methodology for optimal domain of attraction es...
AbstractThe stability is studied of a class of nonlinear dynamical systems which possess many nonlin...
The stability is studied of a class of nonlinear dynamical systems which possess many nonlinearities...
International audienceThis work deals with a new method for computing Lyapunov functions represented...
International audienceThis work deals with a new method for computing Lyapunov functions represented...
In this paper we present various theoretical and computational methods for estimating the domain of ...
We propose an automatic and formally sound method for synthesising Lyapunov functions for the asympt...
This paper investigates Lyapunov approaches to expand the domain of attraction (DA) of nonlinear aut...
Machine learning-based methodologies have recently been adapted to solve control problems. The Neura...
In this paper a methodology for the estimation of domains of attraction of stable equilibriums based...
In this paper, two methods for constructing systems of ordinary differential equations realizing any...
This paper proposes a strategy for estimating the domain of attraction (DA) for non-polynomial syste...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
Nowadays, solving constrained engineering problems related to optimization approaches is an attracti...
The stability is studied of a class of nonlinear dynamical systems which possess many nonlinearities...
This thesis implements a recently proposed algebraic methodology for optimal domain of attraction es...
AbstractThe stability is studied of a class of nonlinear dynamical systems which possess many nonlin...
The stability is studied of a class of nonlinear dynamical systems which possess many nonlinearities...