Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Actuators

DOI: 10.3390/act13110464

Authors: Luoyao Ren, Dazhi Wang, Xin Yan, Yupeng Zhang, Jiaxing Wang

Institution: Northeastern University

Language: English

Topic: Advanced DC-DC Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728