Application of Neural Network for Nonlinear Predictive Control

Zhi Cheng Xu; Bin Zhu; Qing Bin Jiang

doi:10.4028/www.scientific.net/AMR.562-564.1964

Paper Titles

An Adaptive Pattern Recognition Algorithm in Coal Dust On-Line Measure
p.1947

Research on the Variation of Average Temperature of Heat Conduction Process under the Second Boundary Condition
p.1951

Back Analysis of Material Parameters of Concrete Dam Using Genetic Algorithm
p.1955

The Peak Resolution of Diffuse X-Ray Diffraction Pattern of Tibetan Medicine
p.1959

Application of Neural Network for Nonlinear Predictive Control
p.1964

Non-Fragile Robust H∞ Control for Linear Uncertain Switched Systems with Delayed Perturbations
p.1968

Real-Time Detection Method on Draft of Inland Departure Ships
p.1972

Neural Network Inverse Decoupling Control of Flatness and Edge Drop for Tandem Cold Rolling Mill
p.1977

Research on Intrusion Detection for the Internet of Things Based on Clone Selection Principle
p.1982

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 562-564Application of Neural Network for Nonlinear...

Application of Neural Network for Nonlinear Predictive Control

Abstract:

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

You might also be interested in these eBooks

Materials Engineering and Automatic Control

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 562-564)

Pages:

1964-1967

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.562-564.1964

Citation:

Cite this paper

Online since:

August 2012

Authors:

Zhi Cheng Xu, Bin Zhu, Qing Bin Jiang

Keywords:

Model Predictive Control (MPC), Neural Network (NN), Robustness

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] J. Richalet, A. Pault, J. L. Testud, J. Papon, Model predictive heuristic control: applications to industrial processes, Automatica, vol. 14, no. 5, pp.413-428, (1978).

DOI: 10.1016/0005-1098(78)90001-8

Google Scholar

[2] D. W. Clarke, C. Mohtadi, P. S. Tuffs, Generalized predictive control. Part I. The Basic Algorithm, Automatica, vol. 23, no. 2, pp.137-148, (1987).

DOI: 10.1016/0005-1098(87)90087-2

Google Scholar

[3] Y. Xi, Predictive control, Beijing, National Defence Industry Publishing House, (1993).

Google Scholar

[4] K. Chao-Chee, Y. L. Kang, Diagonal recurrent neural networks for dynamic systems control, IEEE transactions on Neural Networks, vol. 6, no. 1, pp.144-156, (1995).

DOI: 10.1109/72.363441

Google Scholar

[5] A. Zhang, Diagonal recurrent neural network controller based on generalized predictive control, Journal of Xiamen University (Nature Science), vol. 36, no. 1, pp.22-26, (1997).

Google Scholar

[6] R. N. Jose, H. Wang, A direct adaptive neural-network control for the unknown nonlinear systems and its application, IEEE Transactions on. Neural Networks, vol. 9, no. 1, pp.27-34, (1998).

DOI: 10.1109/72.655026

Google Scholar

[7] X. Sun, C. Ren, Predictive control based on neural network for nonlinear system with time-delay, Proceedings of the 5th international conference on machine learning and cybernetics, Beijing, 2002, pp.319-322.

DOI: 10.1109/icmlc.2002.1176766

Google Scholar