| This Paper:Browse 1953 Download 162 |
码上扫一扫！ |
|
| LianghongWU,YaonanWANG,ShaowuZHOU,XiaofangYUAN |
|
|
| (Engineering Research Center of Advanced Mining Equipment, Ministry of Education, Hunan University of Science and Technology;College of Electric and Information Engineering, Hunan University) |
|
| 摘要: |
|
| 关键词: |
| DOI： |
| Received:July 27, 2011Revised:October 08, 2012 |
| 基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 61203309, 61104088, 60835004), the Scientific Research Fund of Hunan Provincial Education Department (No. 12B043), the Natural Science Foundation of Hunan Province (No. 10JJ9007), the Industry-University-Research Combination Innovation Platform of Hunan Province (No. 2010XK6066), and the Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province. |
|
| Design of mixed H-two/H-infinity optimal control systems using multiobjective differential evolution algorithm |
| Lianghong WU,Yaonan WANG,Shaowu ZHOU,Xiaofang YUAN |
| (Engineering Research Center of Advanced Mining Equipment, Ministry of Education, Hunan University of Science and Technology;College of Electric and Information Engineering, Hunan University) |
| Abstract: |
| In this paper, the mixed H-two/H-infinity control synthesis problem is stated as a multiobjective optimization problem, with objectives of minimizing the H-two and H-infinity norms simultaneously. Instead of building a LMIs-based synthesis algorithm, a self-adaptive control parameter multiobjective differential evolution algorithm is developed directly in the controller parameters space. In the case of systems with polytopic uncertainties, the worst case norm computation is formulated as an implicit optimization problem, and the proposed self-adaptive differential evolution is employed to calculate the worst case H-two and H-infinity norms. The numerical examples illustrate the power and validity of the proposed approach for the mixed H-two/H-infinity control multiobjective optimal design. |
| Key words: Mixed H-two/H-infinity control Polytopic uncertainties Parameter self-adaptive Differential evolution Multiobjective optimization |
