Control of an active magnetic bearing system using swarm intelligence-based optimization techniques

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

“…Research in the international community does not stop using methods and algorithms inspired by nature to solve real problems of industry or others, such as the use of artificial intelligence: Fuzzy logic (Badoud et al, 2022) for the design of a bond graph model-based controller, artificial neural networks (Qin et al, 2014) for the control of magnetic levitation, and the use of meta-heuristic optimization algorithms, such as the grey wolf optimizer (Faris et al, 2018) (Hatta et al, 2019), the PSO (Laldingliana et al, 2022) for the optimization of a fractional order PID controller of an electromagnetic suspension, which is also subject to the application of other meta-heuristic methods, Manta Ray Foraging optimization (Ekinci et al, 2022), and in comparison between several optimization methods (Firefly, Grasshopper and ABC) for the concept of a PID regulator in (Gupta et al, 2023).…”

“…The Firefly optimization method is a meta-heuristic method developed by Xin-She Yang (Yang, 2008), inspired by of fireflies' behaviour, who use light to communicate and attract their friends. This method has received a great deal of attention through its application in different systems, in particular in the identification of the parameters of an induction machine , optimization of the parameters of the PID controller of a magnetic suspension (Gupta et al, 2023), the rotational speed of a spinning machine (Vilas et al, 2021) and others, so it has undergone developments and modifications in their operating principle (Kumar et al, 2021).…”

“…To this end, we adopt the function of evaluation of the integral of the quadratic error, the most widespread in the evaluation of such problems (Gupta et al, 2023): The general structure of our system is given by the following structure: The simulation of our system is done using 20-sim software for the bond graph model and Matlab for the Firefly optimization of the controller parameters, as well as adopting the square signal as the reference signal. After optimizing the parameters, we found the following result: In this respect, the controller parameters found by the Firefly algorithm have the following values: k1=2724.3, k2=277273.6.…”

Automatic Control and System Theory and Advanced Applications—Volume 2

“…An approach based on multi-objective control is presented in [27] for preserving the performance even in the presence of faults. Innovative control strategies for active magnetic bearing devices can also be found in [28][29][30], where adaptive H-infinity control is adopted for improving speed tracking in the presence of uncertainties [28]. Swarm-based optimization is used to self-tune a PID controller, improving the transient performance [29], and hybrid repetitive control is considered to mitigate harmonic vibrations [30].…”

“…Innovative control strategies for active magnetic bearing devices can also be found in [28][29][30], where adaptive H-infinity control is adopted for improving speed tracking in the presence of uncertainties [28]. Swarm-based optimization is used to self-tune a PID controller, improving the transient performance [29], and hybrid repetitive control is considered to mitigate harmonic vibrations [30]. This class of systems represents a solid test bench for a wide plethora of automatic control strategies.…”