A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

Heng Jie Li; Xiao Hong Hao; Xi Ping Pei

doi:10.4028/www.scientific.net/AMR.753-755.1225

Paper Titles

Delamination Buckling of Piezoelectric Laminated Cylindrical Shell under Axial Compression
p.1207

The Stiffness Analysis of a Mechanical Gantry under Torsion
p.1212

Multi-Objective Optimization Based on TTR Interference Analysis
p.1217

Integration of Process Routes Planning and Scheduling Based on Network Diagram and Ant Colony Algorithm
p.1221

A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm
p.1225

Research on Welding Torch Swing Control System Based on DSP
p.1230

Optimization of Controller Parameters of Rudder Roll Stabilization Using Simplex Optimization Algorithm
p.1234

The CAD/CAE System for Pot Rubber Bearing Based on VB
p.1241

Study on 3D Model of Square Dome Shaped Pipe Based on AutoCAD
p.1246

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755A Nonlinear Optimal Iterative Learning Control...

A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

Abstract:

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

You might also be interested in these eBooks

Materials Processing and Manufacturing III

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

1225-1229

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.1225

Citation:

Cite this paper

Online since:

August 2013

Authors:

Heng Jie Li, Xiao Hong Hao, Xi Ping Pei

Keywords:

Clonal Selection Algorithm, Iterative Learning Control, RBF Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Arimoto S, Kawamura S and Miyazaki F. Bettering operation of robots by learning. Journal of Robotic Systems. 2( 1984) 123-84.

DOI: 10.1002/rob.4620010203

Google Scholar

[2] Amann. N, Owens, D. and Rogers, E. Iterative Learning Control for Discrete-time Systems with Exponential Rate of Convergence. IEE Proceeding of Control Theory and Applications, 143(1996)217-244.

DOI: 10.1049/ip-cta:19960244

Google Scholar

[3] Owens, D. & Fang, K., Parameter optimization in Iterative Learning Control. International Journal of Control, 11 (2003)1059-1069.

Google Scholar

[4] Vasilis E Hatzikos, David H Owens and Jari Hätönen. An Evolutionary Based Optimization Method for Nonlinear Iterative Learning Control Systems. Proceedings of the American Control Conference, Denver, Colorado, (2003).

DOI: 10.1109/acc.2003.1244126

Google Scholar

[5] V. Hatzikos, J. Hätönen and D.H. Owens, Genetic algorithms in norm-optimal linear and non-linear iterative learning control. International Journal of Control, 2(2004) 188-197.

DOI: 10.1080/00207170310001649351

Google Scholar

[6] Hengjie Li, Xiaohong Hao and David Owens, An Improved Clonal Selection Algorithm Based Optimization Method for Iterative Learning Control Systems,. 17th International Federation of Automatic Control World Congress, Seoul, Korea. (2008).

DOI: 10.1109/wcica.2008.4594444

Google Scholar