Rebounded PSO Method for Sigmoid PID Controller for a Maglev System with Input Saturation
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Soft Computing and Its Engineering Applications: 6th International Conference, icSoftComp 2024, Bangkok, Thailand, December 10-12, 2024, Revised Selected Papers, Part II. ed. / Kanubhai K. Patel; KC Santosh; Gabriel Gomes de Oliveira; Atul Patel; Ashish Ghosh. Vol. 2 Cham: Springer Nature, 2025. p. 230-244 (Communications in Computer and Information Science; Vol. 2431 CCIS).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Rebounded PSO Method for Sigmoid PID Controller for a Maglev System with Input Saturation
AU - Diab, Mohammad
AU - Mercorelli, Paolo
AU - Haus, Benedikt
AU - Dethmann, Jannis
N1 - Conference code: 6
PY - 2025
Y1 - 2025
N2 - In this paper a control of a magnet levitation system using a PID controller will be presented. In particular, the article provides a PSO optimization method to optimize a cost function consisting of an ITAE (integral of the time weighted absolute error) with a weighted ITAI (integral of the time weighted absolute input) for tuning the parameters of a PID controller and an Input-Sigmoid anti-windup control function to minimize the effect of the limitation of the voltage input in a Maglev system. The proposed technique takes the system in a saturation for a suitable time to obtain the optimization of the defined cost function. The proposed rebounded PSO is inspired by the idea that the integral part of the controller should not exceed the saturation limit. To realise a rebounded PSO, the idea is to randomize the reference best particles (gbestjk) inside their boundary when they are on the lower or upper bound. Once a large enough search region is defined, the idea is to obtain a suboptimum inside the boundary. A PSO is developed and is devoted to optimize five parameters: PID parameters, a parameter which characterizes the input-sigmoid function and a parameter to realize an optimal derivative action in the presence of noise. Simulation results show the effectiveness of the proposed method.
AB - In this paper a control of a magnet levitation system using a PID controller will be presented. In particular, the article provides a PSO optimization method to optimize a cost function consisting of an ITAE (integral of the time weighted absolute error) with a weighted ITAI (integral of the time weighted absolute input) for tuning the parameters of a PID controller and an Input-Sigmoid anti-windup control function to minimize the effect of the limitation of the voltage input in a Maglev system. The proposed technique takes the system in a saturation for a suitable time to obtain the optimization of the defined cost function. The proposed rebounded PSO is inspired by the idea that the integral part of the controller should not exceed the saturation limit. To realise a rebounded PSO, the idea is to randomize the reference best particles (gbestjk) inside their boundary when they are on the lower or upper bound. Once a large enough search region is defined, the idea is to obtain a suboptimum inside the boundary. A PSO is developed and is devoted to optimize five parameters: PID parameters, a parameter which characterizes the input-sigmoid function and a parameter to realize an optimal derivative action in the presence of noise. Simulation results show the effectiveness of the proposed method.
KW - Anti-saturation
KW - Anti-windup
KW - Maglev system
KW - PID
KW - PSO method
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=105006829690&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-88042-1_18
DO - 10.1007/978-3-031-88042-1_18
M3 - Article in conference proceedings
AN - SCOPUS:105006829690
SN - 978-3-031-88041-4
VL - 2
T3 - Communications in Computer and Information Science
SP - 230
EP - 244
BT - Soft Computing and Its Engineering Applications
A2 - Patel, Kanubhai K.
A2 - Santosh, KC
A2 - Gomes de Oliveira, Gabriel
A2 - Patel, Atul
A2 - Ghosh, Ashish
PB - Springer Nature
CY - Cham
T2 - 6th International Conference on Soft Computing and its Engineering Applications, -icSoftComp 2024
Y2 - 10 December 2024 through 12 December 2024
ER -