Volume 17 Number 6
December 2020
Article Contents
Hong-Tao Ye, Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution. International Journal of Automation and Computing, 2020, 17(6): 867-872. doi: 10.1007/s11633-015-0917-7
Cite as: Hong-Tao Ye, Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution. International Journal of Automation and Computing, 2020, 17(6): 867-872.

# PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution

Author Biography:
• Zhen-Qiang Li  received the Ph.D.degree in information science and electrical engineering from Kyushu University, Japan in 2010.He is currently an associate professor in Guangxi University of Science and Technology, China.
His research interests include computational intelligence and optimization theory.
E-mail:lizhenqiang67@163.com

• Corresponding author: Hong-Tao Ye  received the Ph.D.degree in control theory and control engineering from South China University of Technology, China in 2011.He is currently a professor in Guangxi University of Science and Technology, China.He has authored more than 20 referred journal and conference papers.He also serves as a reviewer for several international journals.He is a council member of Guangxi Association of Automation.
His research interests include computational intelligence and intelligent control.
E-mail:yehongtao@126.com (Corresponding author)
• Accepted: 2014-11-25
• Published Online: 2015-11-06
• For complex systems with high nonlinearity and strong coupling, the decoupling control technology based on proportion integration differentiation (PID) neural network (PIDNN) is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem, a hybrid particle swarm optimization (PSO) and differential evolution (DE) algorithm (PSO-DE) is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show that the proposed method has better decoupling capabilities and control quality than the previous approaches.
• Recommended by Associate Editor Chandrasekhar Kambhampati
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###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (4)

## PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution

• ###### Corresponding author:Hong-Tao Ye  received the Ph.D.degree in control theory and control engineering from South China University of Technology, China in 2011.He is currently a professor in Guangxi University of Science and Technology, China.He has authored more than 20 referred journal and conference papers.He also serves as a reviewer for several international journals.He is a council member of Guangxi Association of Automation. His research interests include computational intelligence and intelligent control.E-mail:yehongtao@126.com (Corresponding author)

Abstract: For complex systems with high nonlinearity and strong coupling, the decoupling control technology based on proportion integration differentiation (PID) neural network (PIDNN) is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem, a hybrid particle swarm optimization (PSO) and differential evolution (DE) algorithm (PSO-DE) is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show that the proposed method has better decoupling capabilities and control quality than the previous approaches.

Recommended by Associate Editor Chandrasekhar Kambhampati
Hong-Tao Ye, Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution. International Journal of Automation and Computing, 2020, 17(6): 867-872. doi: 10.1007/s11633-015-0917-7
 Citation: Hong-Tao Ye, Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution. International Journal of Automation and Computing, 2020, 17(6): 867-872.
Reference (23)

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