A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters
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)...
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MDPI AG
2024-11-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/13/11/464 |
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| author | Luoyao Ren Dazhi Wang Xin Yan Yupeng Zhang Jiaxing Wang |
| author_facet | Luoyao Ren Dazhi Wang Xin Yan Yupeng Zhang Jiaxing Wang |
| author_sort | Luoyao Ren |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cb34f80b62514a60923a0180541c99b8 |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-cb34f80b62514a60923a0180541c99b82025-08-20T01:53:42ZengMDPI AGActuators2076-08252024-11-01131146410.3390/act13110464A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost ConvertersLuoyao Ren0Dazhi Wang1Xin Yan2Yupeng Zhang3Jiaxing Wang4College of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaThe 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.https://www.mdpi.com/2076-0825/13/11/464neural networkadaptive controlfour-switch buck–boostCPSC |
| spellingShingle | Luoyao Ren Dazhi Wang Xin Yan Yupeng Zhang Jiaxing Wang A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters Actuators neural network adaptive control four-switch buck–boost CPSC |
| title | A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters |
| title_full | A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters |
| title_fullStr | A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters |
| title_full_unstemmed | A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters |
| title_short | A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters |
| title_sort | novel chaotic particle swarm optimized backpropagation neural network pid controller for four switch buck boost converters |
| topic | neural network adaptive control four-switch buck–boost CPSC |
| url | https://www.mdpi.com/2076-0825/13/11/464 |
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