传统控制器在柴油发电机转速控制中难以兼顾快速响应与小超调，并且在面对多变的工作条件时，需频繁调整参数。为了更好地控制柴油发电机转速，提出了基于RBF神经网络的一阶串级线性自抗扰控制器（RBF-CLADRC）。利用自抗扰控制无需精确数学模型和实时扰动补偿的优势，通过RBF神经网络实现控制器参数的在线整定，解决了参数调整难题。Matlab/Simulink仿真测试结果表明：相比于常规控制算法，改进型自抗扰控制算法在动态实验中的瞬时调速率指标低于3%，稳态时间小于2 s，符合国家一级电站标准。
The traditional controller is difficult to balance the fast response and small overshoot in the speed control of diesel generator, and the parameters need to be adjusted frequently in the face of changing working conditions. In order to better adjust the speed of diesel generator, a first-order cascade linear active disturbance rejection controller (RBF-CLADRC) based on RBF neural network is proposed. Using the advantages of active disturbance rejection control technology without accurate mathematical model and real-time compensation of disturbance, the online tuning of controller parameters is realized by RBF neural network, and the problem of parameter adjustment is solved. The Matlab/Simulink simulation test results show that compared with the conventional control algorithm, the improved self-control disturbance control algorithm has an instantaneous adjustment rate index of less than 3% and a steady-state time of less than 2s in the dynamic experiment, which is in line with the national first-level power station standard.
2025,47(21): 101-107 收稿日期：2025-1-29
DOI：10.3404/j.issn.1672-7649.2025.21.017
分类号：U664.121
基金项目：河南省自然科学基金资助项目（222102220100）；河南省科技研发计划联合基金项目（222103810084）；中原学者工作站项目（224400510030）
作者简介：臧义(1980-),男,博士,副教授,研究方向为电力电子领域的高效电源
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