引用本文:甄圣超,赵韩,黄康,陈义华.不确定性机械系统的最优鲁棒控制设计: 模糊法[J].控制理论与应用,2014,31(5):654~664.[点击复制]
ZHEN Sheng-chao,ZHAO Han,HUANG Kang,CHEN Yi-hua.Optimal robust control design of uncertain mechanical systems: a fuzzy approach[J].Control Theory and Technology,2014,31(5):654~664.[点击复制]
不确定性机械系统的最优鲁棒控制设计: 模糊法
Optimal robust control design of uncertain mechanical systems: a fuzzy approach
摘要点击 2784  全文点击 2148  投稿时间:2013-04-23  修订日期:2013-12-25
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DOI编号  10.7641/CTA.2014.30373
  2014,31(5):654-664
中文关键词  不确定性  模糊理论  鲁棒控制  最优控制
英文关键词  uncertainty  fuzzy set theory  robust control  optimal control
基金项目  
作者单位E-mail
甄圣超* 合肥工业大学 机汽学院
佐治亚理工学院 机械工程系 
zhenshengchao@gmail.com 
赵韩 合肥工业大学 机汽学院  
黄康 合肥工业大学 机汽学院  
陈义华 佐治亚理工学院 机械工程系  
中文摘要
      针对不确定性机械系统, 提出了一种新的最优鲁棒控制方法. 本文用模糊法去描述机械系统中的不确定 性. 机械系统的性能要求是确定的(保证最低要求), 同时也是模糊的(成本控制里用到). 所提出的控制方法是确定 的, 而不是基于假设的规则. 经过严格的理论证明, 控制系统最终可达到理想的性能指标. 基于模糊信息, 本文设计 了一个性能指标(综合成本, 包括系统的平均模糊性能和控制成本). 通过最小化此性能指标, 可解决控制的最优设 计问题. 这种最优设计方法可得到唯一的解析形式的最优解. 总的来说, 这种最优鲁棒控制方法较为系统, 能够保 证确定的系统性能得以实现, 同时控制成本最小. 最后, 本文选了一个机械系统作为例子.
英文摘要
      A new optimal robust control is proposed for mechanical systems with fuzzy uncertainty. Fuzzy set theory is used to describe the bound of uncertainty. The desirable system performance is deterministic (assuring the bottom line) and also fuzzy (enhancing the cost consideration). The proposed control is deterministic but is not the usual if-then rules-based. The resulting controlled system is proved to be uniformly bounded and uniformly ultimately bounded via the Lyapunov minimax approach. A performance index (the composite cost which includes the average fuzzy system performance and the control effort) is proposed based on the fuzzy information. The optimal design problem associated with the control can then be solved by minimizing the performance index. The unique closed-form optimal gain and the cost are explicitly shown. The resulting control design is systematic and is able to guarantee the deterministic performance as well as the minimal cost. Finally, a mechanical system is chosen for demonstration.