| 基于 GA-模糊 RBF 的发电机组滑模自抗扰控制 |
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| 引用本文： | 冯旭刚,黄鹏辉,张泽辰,王正兵,宋爱国.基于 GA-模糊 RBF 的发电机组滑模自抗扰控制[J].仪器仪表学报,2023,44(8):319-328. |
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| 作者姓名： | 冯旭刚 黄鹏辉 张泽辰 王正兵 宋爱国 |
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| 作者单位： | 1. 安徽工业大学电气与信息工程学院;2. 东南大学仪器科学与工程学院 |
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| 基金项目： | 安徽省高校自然科学研究重大项目(KJ2021ZD0042)；;安徽省重点研究与开发计划项目(2022f04020004)资助； |
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| 摘 要： | 针对燃煤发电机组风烟系统大惯性、大滞后、参数不稳定等特点，提出一种基于发电机组的滑模自抗扰控制策略。选择模糊径向基函数(RBF)算法辨识模型，以梯度下降法和遗传算法分别对神经网络权值进行粗调和细调，通过扩张状态观测器估计系统内外部扰动，将非线性状态误差反馈控制律与滑模控制策略相结合以克服系统惯性、滞后和扰动的问题，并设计Lyapunov函数验证控制系统稳定性。仿真结果表明，滑模自抗扰控制与串级比例-积分-微分(PID)控制、滑模控制和自抗扰控制相比，在模型适配的情况下，所设计的控制策略在38 s达到设定值，无超调量；当向系统施加20%的反向阶跃干扰时，系统调节时间为39.5 s,超调量为3.4%。在模型失配情况下的调节时间为43.2 s,无超调量；当向系统施加20%的反向阶跃干扰时，系统调节时间为46.4 s,超调量为3.87%。工程应用结果表明，一次风量控制偏差在±10 000 m3/h以内，相比串级PID控制策略波动范围降低21%,系统抗干扰能力和鲁棒性得到有效提升。
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| 关 键 词： | 模糊RBF 遗传算法 滑模自抗扰控制 扩张状态观测器 风烟系统 |
Sliding mode active disturbance rejection control for generator sets based on GA-fuzzy RBF |
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| Feng Xugang,Huang Penghui,Zhang Zechen,Wang Zhengbing,Song Aiguo.Sliding mode active disturbance rejection control for generator sets based on GA-fuzzy RBF[J].Chinese Journal of Scientific Instrument,2023,44(8):319-328. |
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| Authors: | Feng Xugang Huang Penghui Zhang Zechen Wang Zhengbing Song Aiguo |
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| Affiliation: | 1. School of Electrical and Information Engineering, Anhui University of Technology; 2. School of Instrument Science and Engineering, Southeast University |
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| Abstract: | Aiming at the characteristics of large inertia, large hysteresis and unstable parameters of the air and flue gas system of coal-fired generator sets, a sliding mode active disturbance rejection control strategy based on generator sets is proposed. The fuzzy radial
basis function (RBF) algorithm is selected to identify the model, the gradient descent method is used to coarse-tune the neural network
weights, and the genetic algorithm is used to fine-tune the neural network weights. The internal and external disturbances of the system
are estimated by the extended state observer, the nonlinear state error feedback rate is designed and sliding mode control strategies are
designed to overcome the inertia, hysteresis and disturbances of the system, and Lyapunov functions are designed to evaluate the stability
of the control system. The simulation results show that the designed control strategy reaches the set value in 38 s with no overshoot
compared with the cascaded proportion integration differentiation (PID) control, sliding mode control and self-rejecting control in the
case of model mismatch. When a 20% backward step disturbance is applied to the system, the system regulation time is 39. 5 s with
3. 4% overshoot. The regulation time in the case of model mismatch is 43. 2 s with no overshoot. When the system applies 20% reverse
step disturbance, the system regulation time is 46. 4 s with 3. 87% overshoot. The engineering application results show that the primary
air volume control deviation is within ±10 000 m
3
/ h, which is 21% lower than the fluctuation range of the cascaded PID control, and the
anti-disturbance capability and robustness of the system are improved. |
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| Keywords: | fuzzy RBF genetic algorithm sliding mode active disturbance rejection control extended state observer air and flue gas
system |
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