Design of mixed H-two/H-infinity optimal control systems using multiobjective differential evolution algorithm

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• 作者：Lianghong Wu ; Yaonan Wang ; Shaowu Zhou…
• 关键词：Mixed H ; two/H ; infinity control ; Polytopic uncertainties ; Parameter self ; adaptive ; Differential evolution ; Multiobjective optimization
• 刊名：Control Theory and Technology
• 出版年：2013
• 出版时间：August 2013
• 年：2013
• 卷：11
• 期：3
• 页码：521-528
• 全文大小：385 KB
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• 作者单位：Lianghong Wu (11156)
  Yaonan Wang (21156)
  Shaowu Zhou (11156)
  Xiaofang Yuan (21156)
  
  11156. Engineering Research Center of Advanced Mining Equipment, Ministry of Education, Hunan University of Science and Technology, Xiangtan Hunan, 411201, China
  21156. College of Electric and Information Engineering, Hunan University, Changsha Hunan, 410082, China
  
• 刊物类别：Control; Systems Theory, Control; Optimization; Computational Intelligence; Complexity; Control, Rob
• 刊物主题：Control; Systems Theory, Control; Optimization; Computational Intelligence; Complexity; Control, Robotics, Mechatronics;
• 出版者：South China University of Technology and Academy of Mathematics and Systems Science, CAS
• ISSN：2198-0942

文摘

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. Keywords Mixed H-two/H-infinity control Polytopic uncertainties Parameter self-adaptive Differential evolution Multiobjective optimization