A Novel PID Control Strategy Based on PSO-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

Liu, Hemiao; Zhao, Yulian; Shurafa, Mahmoud Al; Chen, Jiufei; Wu, Jing; Cheng, Yan-Ming

doi:10.1109/imcec51613.2021.9482183

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…As analysed above, it is feasible and effective to combine intelligent algorithm with neural network. As one of the main optimization algorithms, particle swarm optimization algorithm is used to optimize the initial weight of neural network and improve the performance of PID controller in controlling complex systems [20, 21].…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Wang

Deng

et al. 2021

IET Renewable Power Gen

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Recently, solar power generation systems are more and more popular and widely used in grid connected power generation, intelligent buildings, and power supply in remote areas. For photovoltaic panels, due to the influence of factors such as light intensity and ambient temperature, their output voltage and current become uns, and the output voltage of a single photovoltaic panel is considerably low. As a result, Boost circuits are needed for voltage boosting. PID controller is commonly used for Boost converter because it can effectively control the controlled object according to the characteristics of the controlled object. However, when the controlled object is complex and variable, the appropriate parameters are hardly to be selected by experience, and the fixed controller parameters may lead to unexpected performances under different working conditions. Here, a genetic algorithm combined with BP neural network PID control (GA‐BPPID) is proposed to improve both dynamic and anti‐interference performances of Boost circuit by introducing the global optimization ability of genetic algorithm and the adaptive adjustment characteristics of BP neural network. System modelling and detailed controller design procedures are provided. Finally, the theoretical analysis and controller design are validated by simulation results.

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Section: Introductionmentioning

confidence: 99%