2023 |
Aloui, Messaoud; Hamidi, Faical; Jerbi, Houssem; Aoun, Mohamed Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool Conférence 2023. Résumé | Liens | BibTeX | Étiquettes: Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov’s functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization @conference{Aloui2023b, This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE. |
Aloui, Messaoud; Hamidi, Faical; Jerbi, Houssem; Aoun, Mohamed Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool Conférence 2023. Résumé | Liens | BibTeX | Étiquettes: Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov’s functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization @conference{Aloui2023, This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE. |
2016 |
Frej, G. Bel Haj; Thabet, A.; Boutayeb, M.; Aoun, M. Decentralized observers of a large class of nonlinear interconnected systems Conférence 2016, (Cited by: 1). Résumé | Liens | BibTeX | Étiquettes: Asymptotic stability, Automation, Control matrices, Decentralized state observers, Differential mean value theorems, Linear matrix inequalities, Matrix algebra, Non linear, Nonlinear interconnected systems, Process control @conference{BelHajFrej2016905b, The objective of this paper is the synthesis of decentralized state observers for large class of nonlinear interconnected systems. The procedure uses the Differential Mean Value Theorem (DMVT) to simplify the design of estimation and control matrices gains. A general condition on the non linear time-varying interconnections functions is introduced. To ensure asymptotic stability, sufficient conditions are formulated in Linear Matrix Inequalities (LMIs). High performances are shown through numerical simulation. © 2015 IEEE. |
Frej, G. Bel Haj; Thabet, A.; Boutayeb, M.; Aoun, M. Decentralized observers of a large class of nonlinear interconnected systems Conférence 2016, (Cited by: 1). Résumé | Liens | BibTeX | Étiquettes: Asymptotic stability, Automation, Control matrices, Decentralized state observers, Differential mean value theorems, Linear matrix inequalities, Matrix algebra, Non linear, Nonlinear interconnected systems, Process control @conference{BelHajFrej2016905c, The objective of this paper is the synthesis of decentralized state observers for large class of nonlinear interconnected systems. The procedure uses the Differential Mean Value Theorem (DMVT) to simplify the design of estimation and control matrices gains. A general condition on the non linear time-varying interconnections functions is introduced. To ensure asymptotic stability, sufficient conditions are formulated in Linear Matrix Inequalities (LMIs). High performances are shown through numerical simulation. © 2015 IEEE. |
Publications
2023 |
Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool Conférence 2023. |
Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool Conférence 2023. |
2016 |
Decentralized observers of a large class of nonlinear interconnected systems Conférence 2016, (Cited by: 1). |
Decentralized observers of a large class of nonlinear interconnected systems Conférence 2016, (Cited by: 1). |