Neural Network PID Decoupling Control Based on Chaos Particle Swarm Optimization

摘要：

　　As a new kind of neural network model,Neural network PID(PIDNN)combines the advantages of PID and neural network.However,the error back propagation algorithm(BP)limits the performance of PIDNN.In order to realize effective control of nonlinear,large delay and strong coupling system,this paper proposes a neural network PID control method based on chaos particle swarm optimization.Using chaos particle swarm algorithm to replace the reverse pass algorithm of original PID neural network,adjusting the weights of PIDNN between each neuron,the algorithm achieved rapid decoupling control effect.The simulation results show that the proposed method in this paper,compared with the original BP algorithm,has more excellent dynamic and steady-state performance.

关键词： Neural network PID Chaos particle swarm optimization Decoupling control.

作者: TENG Wei-feng PAN Hai-peng REN Jia

作者单位: College of Mechanical Engineering and Automation,Zhejiang Sci-Tech University,Hangzhou 310018,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5017-5020

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）