| 一种改进的RBF整定PID及其仿真实现 |
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| 引用本文： | 刘斌,周德俭,刘电霆.一种改进的RBF整定PID及其仿真实现[J].通信技术,2009,42(11):219-221. |
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| 作者姓名： | 刘斌 周德俭 刘电霆 |
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| 作者单位： | 1. 桂林工学院电子与计算机系,广西,桂林,541004
2. 桂林工学院电子与计算机系,广西,桂林,541004;广西工学院机械工程系,广西,柳州,545006 |
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| 摘 要： | 文中在分析RBF神经网络整定PID算法优缺点的基础上,给出了一种采用遗传模拟退火算法来优化网络结构和权值参数的RBF神经网络,将改进的RBF神经网络用于整定PID控制,并给出了相应的仿真测试例子。仿真实验结果表明,与采用梯度法优化网络权值等参数的RBF神经网络相比,给出的优化算法能更好地辨识控制系统,具有通用性好、调节精度高、在抑制超调量能力强等优点。
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| 关 键 词： | RBF神经网络 遗传算法 模拟退火算法 自整定PID |
Study and Simulation of Self-Tuning PID Controller Based on Novel RBF Neural Network |
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| LIU Bin,ZHOU De-jian,LIU Dian-ting.Study and Simulation of Self-Tuning PID Controller Based on Novel RBF Neural Network[J].Communications Technology,2009,42(11):219-221. |
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| Authors: | LIU Bin ZHOU De-jian LIU Dian-ting |
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| Affiliation: | LIU Bin, ZHOU De-jian, LIU Dian-ting (Department of Electronics and Computer, Guilin University of Technology , Guilin Guangxi 541004, China Department of Mechanical Engineering , Guangxi University of Technology, Liuzhou Guangxi 545006, China) |
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| Abstract: | Based on the analysis of the advantage and disadvantage of RBF neural networks tuning PID algorithm, a RBF neural network with optimized structure and weights by using genetic and simulated annealing algorithm is described. The novel RBF neural network is used in tuning PID control, and the relevant simulated example is given. The simulation results shows that, as compared with the RBF neural network with parameters such as network weights optimized by gradient descent algorithm, the optimized algorithm described in this paper could provide better control system distinction, and has the advantages of excellent general purpose capability, high adjustment precision and strong ability in suppression overshoot. |
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| Keywords: | REF neural network simulated annealing algorithm genetic algorithm self-tuning PID |
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