Neural network model based predictive control for multivariable nonlinear systems
Authors
Jixin Qian1, Yang Jianfeng, Zhao Jun, Niu Jian
1State Key Lab. of Industrial Control Technology, Zhejiang University
Corresponding Author
Jixin Qian
Available Online October 2007.
- DOI
- 10.2991/iske.2007.101How to use a DOI?
- Keywords
- Neural networks, Model predictive control, Nonlinear systems, ARX model
- Abstract
A nonlinear model predictive control (NMPC) algorithm based on a BP-ARX combination model is proposed for multivariable nonlinear systems whose static nonlinearity between inputs and outputs could be obtained. The dynamic behavior of the system is described by a parameter varying ARX model, whose parameters are estimated on-line with recursive least-squares algorithm and rescaled properly according to a BP neural network representing the system static nonlinearity. The construction of the BP-ARX model and a constrained NMPC algorithm based on the BP-ARX model are elaborated. The effectiveness of the proposed method is demonstrated by simulation on a multivariable chemical reactor system
- Copyright
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jixin Qian AU - Yang Jianfeng AU - Zhao Jun AU - Niu Jian PY - 2007/10 DA - 2007/10 TI - Neural network model based predictive control for multivariable nonlinear systems BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 591 EP - 597 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.101 DO - 10.2991/iske.2007.101 ID - Qian2007/10 ER -