| 基于神经网络PID参数自学习的过热汽温Smith预估补偿控制 |
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| 引用本文： | 彭道刚,杨平,王志萍. 基于神经网络PID参数自学习的过热汽温Smith预估补偿控制[J]. 发电设备, 2004, 0(Z1) |
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| 作者姓名： | 彭道刚 杨平 王志萍 |
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| 作者单位： | [1]上海电力学院信息与控制技术系 [2]上海电力学院信息与控制技术系 上海200090 [3]上海200090 |
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| 摘 要： | 针对火电厂过热汽温控制系统的大惯性、大迟延和时变等特性,提出基于神经网络PID参数自学习的Smith预估补偿控制策略。仿真研究表明,该策略的控制效果优于常规的PID控制,能适应对象参数的变化并表现出良好的控制品质,具有较强的鲁棒性和自适应能力。
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| 关 键 词： | 神经网络 自学习 Smith预估器 过热汽温系统 |
PID Parameters Self-studying with Smith Predictor Based on Neural Networks for Superheated Steam Temperature |
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| PENG Dao-gang,YANG Ping,WANG Zhi-ping. PID Parameters Self-studying with Smith Predictor Based on Neural Networks for Superheated Steam Temperature[J]. Power Equipment, 2004, 0(Z1) |
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| Authors: | PENG Dao-gang YANG Ping WANG Zhi-ping |
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| Abstract: | As to the superheated steam temperature control system with large time constant, long time-delay and time-varying in thermal power plant. An PID parameters self-studying with smith predictor based on neural networks strategy is presented in this paper. Simulation results show that this method can adapt the changing of the parameters of the object and behave well control performance compared with PID control. It has strong robustness and self-adaptive ability. |
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| Keywords: | neural networks self-studying smith predictor superheated steam temperature system |
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