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| 基于粒子群优化的汽温系统神经网络自整定PID控制 | |
| 引用本文： | 刘国宏,倪桂杰,孙明,翟永杰. 基于粒子群优化的汽温系统神经网络自整定PID控制[J]. 华北电力大学学报(自然科学版), 2009, 36(1) |
| 作者姓名： | 刘国宏 倪桂杰 孙明 翟永杰 |
| 作者单位： | 1. 河南省电力公司培训中心,河南,郑州,450052 2. 郑州电力高等专科学校,动力系,河南,郑州,450052 3. 华北电力大学科技学院,河北,保定,071003 4. 华北电力大学控制科学与工程学院,河北,保定,071003 |
| 摘 要： | 为了使神经网络PID取得更好的控制性能,采用改进的粒子群算法对神经网络的权值进行优化,通过对具有严重参数不确定性、多扰动以及大迟延的电厂主蒸汽温度被控对象进行的仿真研究结果表明,所提出的嵌入混沌序列的小生境粒子群算法可以避免局部极小,具有全局优化的能力,对神经网络PID的权值优化是成功和有效的,使得具有多模型特性的汽温控制系统在不同的负荷下均获得很好的调节品质。 |
| 关 键 词： | 比例积分微分控制 神经网络 粒子群算法 自整定 主汽温控制系统 |
Self-tuning of neural networks PID control for main steam temperature system based on particle swarm optimization |
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| LIU Guo-hong,NI Gui-jie,SUN Ming,ZHAI Yong-jie. Self-tuning of neural networks PID control for main steam temperature system based on particle swarm optimization[J]. Journal of North China Electric Power University, 2009, 36(1) | |
| Authors: | LIU Guo-hong NI Gui-jie SUN Ming ZHAI Yong-jie |
| Affiliation: | 1.Training Center of Electric Power of Henan;Zhengzhou 450052;China;2.Power Engineering Department;Zhengzhou Electric Power College;Zhengzhou 450004;3.a.Science and Technology College;b.School of Control Science and Engineering;North China Electric Power University;Baoding 071003;China |
| Abstract: | In order to get a better control performance for neural network PID controller,the improved PSO is put forward to optimize the initial weights of neural network PID controller.Simulation is proceeded for the steam temperature system in a power plant under such a control which has a severe uncertainty of parameters and multi-disturbance,as well as a large time-delay.The results show that the presented algorithm of embedded chaotic sequence niche PSO can avoid premature effectively and has powerful global opt... |
| Keywords: | proportional-integral-derivative control neural network particle swarm optimization algorithm self-tuning main steam temperature control system |
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