Data driven output joint probability density function control for multivariate non-linear non-Gaussian systems

被引：11

作者：

Yin, Liping ^{[1
]}

Zhang, Hongyan ^{[1
]}

Guo, Lei ^{[2
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, CICAEET, Nanjing 210044, Jiangsu, Peoples R China

[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China

来源：

IET CONTROL THEORY AND APPLICATIONS | 2015年 / 9卷 / 18期

基金：

中国博士后科学基金;

关键词：

probability; nonlinear control systems; multivariable control systems; predictive control; control system synthesis; optimisation; stochastic systems; data driven output joint probability density function control; multivariate nonlinear nonGaussian systems; data-based joint probability density function control strategy; JPDF control strategy; predictive controller design algorithm; intelligent optimisation algorithm; multistep-ahead cumulative performance index; MINIMUM ENTROPY CONTROL; STOCHASTIC-SYSTEMS; FAULT-DETECTION;

D O I：

10.1049/iet-cta.2015.0451

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study presents a novel data-based joint probability density function (JPDF) control strategy for multivariate non-linear non-Gaussian stochastic systems so that the output JPDF of the system can be made to follow a desired JPDF. The output JPDF, which is usually immeasurable, is estimated according to the output sequence of the system. The multi-step-ahead cumulative performance index is constructed with respect to the control objective and is minimised based on an intelligent optimisation algorithm. By minimising this performance function, a new predictive controller design algorithm is established with more simple formulation and less computation load than existed results. Furthermore, a new approach is developed to guarantee convergence in distribution'. Finally, simulations are given to demonstrate the effectiveness of the proposed control algorithm and some desired results have been obtained.

引用

页码：2697 / 2703

页数：7

共 27 条

[1]

[Anonymous], 2000, Bounded Dynamic Stochastic Systems: Modeling and Control

[2]

Astrom K. J., 1970, INTRO STOCHASTIC CON

[3] On predicting roller milling performance Part I: the breakage equation [J].

Campbell, GM ;

Webb, C .

POWDER TECHNOLOGY, 2001, 115 (03) :234-242

[4]

Clark D. W, 1994, ADV MODEL BASED PRED

[5] Control of particle size distribution described by a population balance model of semibatch emulsion polymerization [J].

Crowley, TJ ;

Meadows, ES ;

Kostoulas, E ;

Doyle, FJ .

JOURNAL OF PROCESS CONTROL, 2000, 10 (05) :419-432

[6] Approximate inference of the bandwidth in multivariate kernel density estimation [J].

Filippone, Maurizio ;

Sanguinetti, Guido .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2011, 55 (12) :3104-3122

[7] Optimal probability density function control for NARMAX stochastic systems [J].

Guo, L. ;

Wang, H. ;

Wang, A. P. .

AUTOMATICA, 2008, 44 (07) :1904-1911

[8] Fault detection for non-linear non-Gaussian stochastic systems using entropy optimization principle [J].

Guo, L. ;

Wang, H. ;

Chai, T. .

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2006, 28 (02) :145-161

[9] Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises [J].

Guo, L ;

Wang, H .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2006, 51 (04) :695-700

[10]

Guo L, 2010, ADV IND CONTROL, P1, DOI 10.1007/978-1-84996-030-4

← 1 2 3 →