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Entropy optimization based filtering for non-gaussian stochastic systems

Science China(Information Sciences) 2017年第12期60卷 33-43页

作者： Bo TIAN Yan WANG Lei GUO School of Automation Science and Electrical Engineering Beihang University National Key Laboratory on Aircraft Control Technology Beihang University

This paper is concerned with the entropy optimization based filter design for a class of multivariate dynamic stochastic systems with simultaneous presence of non-gaussian process noise and measurement *** filter consists of time update and measurement update two steps, where the selection of the filter gain in the measurement update equation is a key issue to be addressed. Different from the classic Kalman filter theory, entropy rather than variance is employed as the filtering performance criterion due to the non-gaussian characteristic of the estimation error. Following the establishment of the relationship between the probability density functions of random noises and estimation error, two kinds of entropy based performance indices are provided. On this basis, the corresponding optimal filter gains are obtained respectively by using the gradient optimization technique. Finally, some numerical simulations are provided to demonstrate the effectiveness of the proposed filtering algorithms.

关键词： non-gaussian systems joint probability density function(JPDF) quadratic information potential relative entropy optimal filtering

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Statistic PID Tracking Control for non-gaussian Stochastic systems Based on T-S Fuzzy Model

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International Journal of Automation and computing 2009年第1期6卷 81-87页

作者： Yang Yi Hong Shen Lei Gu School of Automation Southeast University Nanjing 210096 PRC School of Economics and Management Southeast University Nanjing 210096 PRC School of Instrumentation and Opto-Eiectronics Engineering Beihang University Beijing 100083 PRC

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.

关键词： non-gaussian systems probability density function statistic tracking control T-S fuzzy model proportional-integralderivative control.

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