A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters

• Published in: Actuators Vol. 13; no. 11; p. 464
• Main Authors: Ren, Luoyao, Wang, Dazhi, Yan, Xin, Zhang, Yupeng, Wang, Jiaxing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: adaptive control; Algorithms; Artificial neural networks; Back propagation; Back propagation networks; Buck converters; Chaos theory; Control systems; Controllers; CPSC; Dynamic response; Electric current converters; Electric vehicles; Energy; four-switch buck–boost; Interactive learning; Learning strategies; Mathematical optimization; neural network; Neural networks; Optimization algorithms; Optimization techniques; Particle swarm optimization; Performance enhancement; Proportional integral derivative; Renewable resources
• ISSN: 2076-0825; 2076-0825
• DOI: 10.3390/act13110464

The emergence of intelligent control strategies has made optimization techniques essential for the precise control of DC converters. This study aims to enhance the performance of the Four-Switch Buck–Boost (FSBB) converter through control system optimization. Backpropagation neural networks (BPNNs) have been widely used for optimizing proportional–integral–derivative (PID) controllers. To further improve the FSBB control system, particle swarm optimization (PSO) is employed to optimize the BPNN, reducing dynamic response time and enhancing robustness. Despite these advantages, the PSO method still suffers from limitations, such as slow convergence and poor stability. To address these challenges, chaotic optimization algorithms are integrated with BPNN. The chaotic particle swarm optimization (CPSO) algorithm enhances the global search capability, enabling a faster system response and minimizing overvoltage. This hybrid CPSO-BPNN approach refines the optimization process, leading to more precise control of the FSBB converter. The simulation results show that the CPSO-BPNN-PID controller reaches a steady state more quickly and exhibits superior performance compared to traditional PID controllers.