PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution
文献类型：期刊论文
| 作者 | Hong-Tao Ye1,2; Zhen-Qiang Li1,2 |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:6页码:867-873 |
| 关键词 | Particle swarm optimization differential evolution proportion integration differentiation (PID) neural network hybrid approach decoupling control. |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-015-0917-7 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42260]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou 545006, China 2.School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China |
| 推荐引用方式 GB/T 7714 | Hong-Tao Ye,Zhen-Qiang Li. PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution[J]. International Journal of Automation and Computing,2020,17(6):867-873. |
| APA | Hong-Tao Ye,&Zhen-Qiang Li.(2020).PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution.International Journal of Automation and Computing,17(6),867-873. |
| MLA | Hong-Tao Ye,et al."PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution".International Journal of Automation and Computing 17.6(2020):867-873. |
入库方式： OAI收割
来源：自动化研究所
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