Improved robust adaptive cascade control of buck converters using complex-order PSO algorithm: theory and experiments

This article attempts to design an improved robust controller for the DC-DC buck converters subject to the load variations. The design mechanism is given by adopting the radial basis function neural networks for identifying unknown terms in which the neural weights are updated adaptively. To improve...

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Vydáno v:International journal of dynamics and control Ročník 13; číslo 10; s. 357
Hlavní autoři: Saadat, Seyyed Amirhossein, Keighobadi, Javad, Alfi, Alireza, Abedi Pahnehkolaei, Seyyed Mehdi, Vahedi, Hani
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Springer Nature B.V 01.10.2025
Témata:
Algorithms
Buck converters
Cascade control
Control systems design
Controllers
Disturbance observers
Load fluctuation
Neural networks
Optimization
Parameter identification
Particle swarm optimization
Proportional integral derivative
Radial basis function
Robust control
Systems stability
ISSN:2195-268X, 2195-2698
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Shrnutí:This article attempts to design an improved robust controller for the DC-DC buck converters subject to the load variations. The design mechanism is given by adopting the radial basis function neural networks for identifying unknown terms in which the neural weights are updated adaptively. To improve the robust behavior of the overall system, two disturbance observers are designed on the basis of cascade control. Furthermore, the control parameters are selected optimally using the complex-order particle swarm optimization algorithm. The stability of the overall system is proven by the Lyapunov theorem. The results of the proposed idea are presented and compared with a fuzzy backstepping control approach and a PID controller in the presence of supplied voltage fluctuations as well as the load variations. In addition, the experimental study is also conducted to assess the practicality of the proposed control framework.
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ISSN:2195-268X
2195-2698
DOI:10.1007/s40435-025-01872-7