2023 |
Saafi, Omar; Dabbaghi, Boudour; Hamidi, Faical; Aoun, Mohamed 2023. Résumé | Liens | BibTeX | Étiquettes: Autonomous switching sequence, Hybrid switched systems, Hybrid switching systems, Hybrid systems, Identification problem, Numerical methods, Optimization techniques, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Swarm optimization, Switching instants, Switching sequence @conference{Saafi2023b,In this paper we propose a new optimization technique based on an evolutionary method known as particle swarm optimization (PSO) for solving and specifying the switching instants of hybrid systems. The main objective is to minimize a performance measure that depends on these switching instants within a finite time interval. Our approach assumes that there is a predefined sequence of system modes and, at each switching instant, it is possible for a state-space variable to jump from one mode to another, resulting in an additional associated cost. Our approach is justified by numerical examples and compared with the results obtained by gradient-based methods. The results obtained by PSO are very promising, without requiring any a priori assumptions about the regularity of the objective function to be minimized. © 2023 IEEE. |
Saafi, Omar; Dabbaghi, Boudour; Hamidi, Faical; Aoun, Mohamed 2023. Résumé | Liens | BibTeX | Étiquettes: Autonomous switching sequence, Hybrid switched systems, Hybrid switching systems, Hybrid systems, Identification problem, Numerical methods, Optimization techniques, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Swarm optimization, Switching instants, Switching sequence @conference{Saafi2023,In this paper we propose a new optimization technique based on an evolutionary method known as particle swarm optimization (PSO) for solving and specifying the switching instants of hybrid systems. The main objective is to minimize a performance measure that depends on these switching instants within a finite time interval. Our approach assumes that there is a predefined sequence of system modes and, at each switching instant, it is possible for a state-space variable to jump from one mode to another, resulting in an additional associated cost. Our approach is justified by numerical examples and compared with the results obtained by gradient-based methods. The results obtained by PSO are very promising, without requiring any a priori assumptions about the regularity of the objective function to be minimized. © 2023 IEEE. |