Iterative Identification of Hammerstein Parameter Varying Systems With Parameter Uncertainties Based on the Variational Bayesian Approach

被引:43
作者
Ma, Junxia [1 ,2 ]
Huang, Biao [2 ]
Ding, Feng [3 ]
机构
[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China
[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada
[3] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2020年 / 50卷 / 03期
基金
中国国家自然科学基金;
关键词
Mathematical model; Nonlinear systems; Bayes methods; Numerical models; Signal processing algorithms; Job shop scheduling; Parameter estimation; Input nonlinear output-error model; multiple model; parameter estimation; variational Bayesian (VB) approach; NONLINEAR PROCESS IDENTIFICATION; MODEL LPV APPROACH; ALGORITHM; DESIGN;
D O I
10.1109/TSMC.2017.2756913
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The identification of the multiple model-based Hammerstein parameter varying systems is studied in this paper. The parameters of the considered systems vary as the systems perform on different operating conditions. For each local model, the input nonlinear output-error structure is introduced to describe the dynamical property. Allocating an exponential weighting function to each local model, the nonlinear dynamics of the global system is approximated by combining all local models. The variational Bayesian (VB) approach is adopted to find the solution to the problem of parameter estimation. For the parameter uncertainties, instead of the point estimation, the posterior distribution of each model parameters is obtained under the framework of the VB approach. Two numerical simulation examples and an experiment carried on a multitank system have been employed to demonstrate that the proposed approach can work effectively.
引用
收藏
页码:1035 / 1045
页数:11
相关论文
共 41 条
[1]   Volterra-system identification using adaptive real-coded genetic algorithm [J].
Abbas, Hazem M. ;
Bayoumi, Mohamed M. .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2006, 36 (04) :671-684
[2]   Modeling and Identification of Nonlinear Systems: A Review of the Multimodel Approach-Part 1 [J].
Adeniran, Ahmed Adebowale ;
El Ferik, Sami .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (07) :1149-1159
[3]   Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems [J].
Ala-Luhtala, Juha ;
Sarkka, Simo ;
Piche, Robert .
SIGNAL PROCESSING, 2015, 111 :124-136
[4]   A blind approach to the Hammerstein-Wiener model identification [J].
Bai, EW .
AUTOMATICA, 2002, 38 (06) :967-979
[5]   An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems [J].
Bai, EW .
AUTOMATICA, 1998, 34 (03) :333-338
[6]   Identification of linear parameter varying models [J].
Bamieh, B ;
Giarré, L .
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2002, 12 (09) :841-853
[7]  
Beal MJ, 2003, BAYESIAN STATISTICS 7, P453
[8]   Optimal Piecewise Linear Function Approximation for GPU-Based Applications [J].
Berjon, Daniel ;
Gallego, Guillermo ;
Cuevas, Carlos ;
Moran, Francisco ;
Garcia, Narciso .
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (11) :2584-2595
[9]   Observer-Based Adaptive Fuzzy Control for a Class of Nonlinear Delayed Systems [J].
Chen, Bing ;
Lin, Chong ;
Liu, Xiaoping ;
Liu, Kefu .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (01) :27-36
[10]   Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System [J].
Chen, Mou ;
Shi, Peng ;
Lim, Cheng-Chew .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (02) :260-270