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基于DSP智能调节稳定平台速率环参数 被引量:2

Intelligent Adjustment of Speed Loop Parameters for Stabilized Platform Based on DSP
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摘要 速率环控制器是稳定平台三环控制的重要环节之一,其参数整定部分是实现电机理想调速的关键。为缩短整定周期,提出了基于DSP的智能辨识方法,该方法利用力学原理式、力矩电机电枢电压平衡方程式以及电磁作用方程式推导建立速率环控制器模型,并基于DSP通过多元线性回归方法实现全自动智能参数整定。实验结果表明,用速率环调节方法获取的参数能使指令速率与实际执行速率基本吻合,具有一定的可行性。 Speed loop controller is an important part of three-loop control method used in stabilized platform.Its parameter setting is the key to the precise speed control.To shorten the cycle of setting,an intelligent identification method based on DSP is introduced,which uses formula involving mechanics principle,armature voltage balance equation and electromagnetic equation to establish speed cycle controller model.Through multiple linear regression method embedded into DSP,the automatic intelligent parameter setting is realized.The experimental result shows that output is almost the same with the instruction with the method of speed-loop adjustment,thus the method is feasibility.
出处 《测控技术》 CSCD 2015年第11期87-89,98,共4页 Measurement & Control Technology
基金 国家自然科学基金项目(61171126) 上海重点支撑项目(12250501500) 交通运输部应用基础研究项目(2014329810060)
关键词 多元线性回归 稳定平台 速率环控制 智能辨识 multiple linear regression stabilized platform speed loop controller intelligent identification
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  • 1龙卫江,张文修.基于相近原则的半指导直推学习机及其增量算法[J].应用数学学报,2006,29(4):619-632. 被引量:2
  • 2朱忠尼,等.现代伺服系统[M].成都:四川科学技术出版社/乌鲁木齐:新疆科技卫生出版社,2001.
  • 3Nigam K,McCallum A K,Thrun S,Mitchell T.Text classification from labeled and unlabeled documents using EM[J].Machine Learning,2000,39(2-3):103-134.
  • 4Joachims T.Transductive inference for text classification using support vector machines[G].In:Proc 16th Int'l Conf Machine Learning,Bled,Slovenia,1999,200-209.
  • 5Blum A,Mitchell T.Combining labeled and unlabeled data with co-training[G].In:Proc 16th Annual Conf Computational Learning Theory,Madison,WI,1998,92-100.
  • 6Breiman L.,Friedman,J.,Olshen,R.,and Stone,C.Classification and Regression Trees[M].Wadsworth,1984.
  • 7Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
  • 8Cortes,C.,Vapnik,V.M.,.Support Vector Networks[J].Machine Learning,1995,20:273-297.
  • 9Dietterich,T.G.An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:Bagging,Boosting,and Randomization[J].Machine Learning,2000,40:139-157.
  • 10Breiman,L.,Bagging Predictors[J].Machine Learning,1996,24(2):123-140.

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