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基于LMI的参数随机变化系统的概率密度函数控制 被引量:5

PDF Control of Stochastic Parameter System Using Linear Matrix Inequalities
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摘要 针对模型参数在有界区域内随机变化的系统,基于平方根B样条模型,提出了输出概率密度函数(Probability density function,PDF)跟踪控制策略.目标是控制系统输出的概率密度函数跟踪给定的概率密度函数.通过B样条逼近建立了输出PDF和权值之间的对应关系,把PDF的跟踪转化为权值的跟踪,同时系统转化为MIMO系统,从而权值向量的跟踪就转化为MIMO系统的跟踪问题,接着给出了系统输出概率密度函数跟踪给定概率密度函数的控制器存在的充分条件,通过求解线性矩阵不等式完成状态反馈和输出反馈跟踪控制器的设计,得到了系统具有Hinfinity范数界Gamma鲁棒镇定的结果.仿真结果表明本文提出的控制算法是有效的. This paper presents a probability density function (PDF) tracking control strategy for stochastic parameter system based on a square root B-spline model for the output probability density functions. The objective is to control the PDF of system output to follow a desired PDF. Using the B-spline approximation the tracking problem of PDFs is transferred to the tracking of given weights values which correspond to the given PDF. At the same time, the system is transferred to a MIMO system, whose output is the weight value vector. As a result, the tracking of given weights values is transformed to a tracking problem of a MIMO system. Furthermore, a sufficient condition of the PDF of system output to follow a desired PDF is given and the control strategy is obtained by solving several linear matrix inequalities. A simulated example is used to demonstrate the efficiency of the proposed approach and encouraging results have been gained.
作者 陈海永 王宏
出处 《自动化学报》 EI CSCD 北大核心 2007年第11期1216-1220,共5页 Acta Automatica Sinica
基金 国家自然科学基金(60472065 60534010)资助~~
关键词 概率密度函数控制 平方根B样条 随机控制 跟踪控制 Probability density functions control, square root B-spline, stochastic control, tracking control
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  • 1邢修三.Physical entropy, information entropy and their evolution equations[J].Science China Mathematics,2001,44(10):1331-1339. 被引量:11
  • 2郑大钟.线性系统理论[M].北京:清华大学出版社,1992..
  • 3Wang H. Bounded dynamic stochastic distributions modelling and control [ M]. London : Springer - Verlag Ltd, 2000.
  • 4Wang H. Robust control of the output probability density functions for dynamic stochastic systems[A]. Proc. of the 1998 Conference on Decision and Control[ C]. Tampa, Madison : Omini, 1998.
  • 5Wang H. Control of the output probability density functions for a class of nonlinear stochastic systems[A]. Proe. of the IFAC Workshop on Algorithms and Architectures for Real-time Control[ C]. Caneun,Mexico .. Elesvier, 1998.
  • 6Wang H. Robust control of the output probability density functions for multivariable stochastic systems with guaranteed stability [J]. IEEE Trans. on Automatic Control, 1999,41(7) :2103-2107.
  • 7Wang H. Control for bounded pseudo ARMAX stochastic systems via linear B-spline approximations[A]. Proc. of the 39th IEEE Conf. on Decision and Control[ C]. Sydney: Omini Press, 2000.
  • 8Wang H, Lin W. Applying observer based FDI techniques to detect faults in dynamic and bounded stochastic distributions [J]. International Journal of Control, 2000, 73 ( 15 ) : 1424-1438.
  • 9Dodson C T J, Wang H. Iterative approximation of statistical distributions and relation to information geometry [ J ]. Journal of Statistical Inference for Stochastic Processes, 2001,4 (2) : 307-318.
  • 10Wang H, Yue H. Minimum entropy control of non-Gaussian dynamic stochastic systems [ A]. Proc ofthe 40^th IEEE Conf on Decision and Control[C]. FL Orlando:Omini Press, 2001.

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  • 1张炤,张素,章琛曦,陈亚珠.基于支持向量机的概率密度估计方法[J].系统仿真学报,2005,17(10):2355-2357. 被引量:24
  • 2陆宁云,王福利,高福荣,王姝.间歇过程的统计建模与在线监测[J].自动化学报,2006,32(3):400-410. 被引量:61
  • 3姚利娜,王宏.基于有理平方根逼近的非高斯随机分布系统的故障诊断和容错控制[J].控制理论与应用,2006,23(4):561-568. 被引量:7
  • 4ZHANG R D, XUE A K , WANG J Z. Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity [ J ]. Jour- nal of Process Control, 2009,19 ( 1 ) :68-74.
  • 5ZHANG J. Batch-to-batch optimal control of a batch po- lymerisation process based on stacked neural network models [ J ]. Chemical Engineering Science, 2008, 63 (5) :1273-1281.
  • 6XIONG ZH H, ZHANG J. Neural network model-based on-line re-optimisation control of fed-batch processes using a modified iterative dynamic programming algorithm [ J ]. Chemical Engineering and Processing, 2005,44 (4) : 477-484.
  • 7ZHANG J. Modeling and optimal control of batch proces- ses using recurrent neuro-fuzzy networks [ J 1. IEEE Transactions on Fuzzy Systems, 2005,13 ( 4 ) :417-427.
  • 8MACGREGOR J F, JAECKLE C, KIPARISSIDES C, et al. Process monitoring and diagnosis by muhiblock PKS methods[J]. AIChE Journal,1994,40(5):826-838.
  • 9ZHANG J, MORRIS A J, MARTIN E B, et al. Predic- tion of polymer quality in batch polymerisation reactors u- sing robust neural networks [ J ]. Chemical Engineering Journal, 1998,69 ( 2 ) : 135-143.
  • 10NOMIKOS P, MACGREGOR J F. Monitoring batch processes using multiway principal component analysis [J]. American Institute of Chemical Engineers Journal, 1994,40 ( 8 ) : 1361-1375.

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