Publications

@conference{Saafi2023,

title = {Particle swarm optimization-based solutions for identification problems of autonomous hybrid switching systems},

author = {O. Saafi and B. Dabbaghi and F. Hamidi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182924528\&doi=10.1109%2fIW_MSS59200.2023.10369212\&partnerID=40\&md5=100bf547f598cc0f36cc7adc5d8c3fa0},

doi = {10.1109/IW_MSS59200.2023.10369212},

isbn = {9798350327564},

year  = {2023},

date = {2023-01-01},

journal = {2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {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.},

note = {cited By 0},

keywords = {Autonomous switching sequence; Hybrid switched systems; Hybrid switching systems; Identification problem; Optimization techniques; Particle swarm; Particle swarm optimization; Swarm optimization; Switching instants; Switching sequence, Hybrid systems, Numerical methods; Particle swarm optimization (PSO)},

pubstate = {published},

tppubtype = {conference}

}