[1]周西峰,林莹莹,郭前岗.基于粒子群算法的PID神经网络解耦控制[J].计算机技术与发展,2013,(09):158-161.
　ZHOU Xi-feng,LIN Ying-ying,GUO Qian-gang.PID Neural Network Decoupling Control Based on Particle Swarm Optimization[J].,2013,(09):158-161.
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基于粒子群算法的PID神经网络解耦控制()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年09期
页码:
158-161
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
PID Neural Network Decoupling Control Based on Particle Swarm Optimization
文章编号:
1673-629X（2013）09-0158-04
作者:
周西峰; 林莹莹; 郭前岗
南京邮电大学 自动化学院
Author(s):
ZHOU Xi-feng; LIN Ying-ying; GUO Qian-gang
关键词:
粒子群算法; PID控制; 解耦控制; 多变量系统
Keywords:
PSO algorithm; PID control; decoupling control; multivariable system
文献标志码:
A
摘要:
基于粒子群的优化算法具有对整个参数空间进行高效并行搜索的特点以及PID神经网络的自调节和自适应特性，设计了具有PID结构的多变量自适应神经网络控制器。该算法采用粒子群算法优化PID神经网络初始权值，并将优化后的最优初始权值控制非线性耦合系统。系统仿真结果表明，粒子群优化后的PID神经网络控制器具有逼近控制目标更快、响应时间较短的显著优点。该控制策略可在大范围内克服系统的非线性和强耦合问题，具有一定的理论研究价值和工程实用价值
Abstract:
The automatic control of such a system is a research focus in the process control area. A multivariable adaptive PID Artificial Neural Network ( ANN) controller was introduced,which was based on the characteristics of Particle Swarm Optimization ( PSO) algo-rithm searching the parameter space concurrently and efficiently,and the self-regulation and adaptability of PID artificial neuron net-works. Utilize the PSO to optimize the initial weight value of PID neural network,successfully achieve the control strategy of a nonlinear coupling system using the improved PID neural network with those obtained from the original PID neural network. The new control strate-gy could overcome nonlinear and strong coupling features of the system in a wide range and is expected to have certain theoretical and en-gineering application value

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更新日期/Last Update: 1900-01-01