The design of a non-minimal state space fractional-order predictive functional controller for fractional systems of arbitrary order

被引:29
作者
Bigdeli, Nooshin [1 ]
机构
[1] Imam Khomeini Int Univ, EE Dept, Daneshgah Blv, Qazvin, Iran
关键词
Fractional-order systems; Fractional predictive functional control; Non-minimal state space model; Genetic algorithm; Fractional cost function; CHAMBER PRESSURE; FORMULATION; STABILITY; MODELS;
D O I
10.1016/j.jprocont.2015.03.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the design of the fractional-order predictive functional controller ((PFC)-P-alpha) for the linear fractional systems of arbitrary order has been presented. For this purpose, at first, the fractional order transfer function has been digitized via Grunwald-Letnikov definition to obtain the linear regression model of the system. Next, the non-minimal input-output fractional-order state space ((NMSS)-N-alpha) model of the system has been derived. The fractional-order predictive functional controller ((PFC)-P-alpha) has been then designed for the (NMSS)-N-alpha model structure via defining a fractional order cost function over the fractional-order non-minimal state vector. Finally, genetic algorithm (GA) has been employed to obtain the optimal (PFC)-P-alpha control coefficients. The fractional-order model of two rods thermal bench has been considered as the uncertain case study in this paper. Simulation results for temperature control of this fractional-order system are representative of better performance of the designed controller with respect to the NMSSPFC as well as the fractional GPC. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:45 / 56
页数:12
相关论文
共 59 条
[1]  
[Anonymous], 2011, ACTA MECH AUTOM
[2]  
[Anonymous], PRATIQUE COMMANDE PR
[3]  
[Anonymous], P 17 WORLD C IFAC SE
[4]   Fault detection based on fractional order models: Application to diagnosis of thermal systems [J].
Aribi, Asma ;
Farges, Christophe ;
Aoun, Mohamed ;
Melchior, Pierre ;
Najar, Slaheddine ;
Abdelkrim, Mohamed Naceur .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (10) :3679-3693
[5]  
ARORA AK, 1987, INDIAN J PURE AP MAT, V18, P931
[6]   Predictive functional control for active queue management in congested TCP/IP networks [J].
Bigdeli, N. ;
Haeri, M. .
ISA TRANSACTIONS, 2009, 48 (01) :107-121
[7]  
Bitmead R. R., 1990, Adaptive Optimal Control: The Thinking Man's GPC
[8]   Design and stability analysis of fuzzy model-based predictive control - A case study [J].
Blazic, Saso ;
Skrjanc, Igor .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2007, 49 (03) :279-292
[9]   Dynamic modeling of electrical characteristics of solid oxide fuel cells using fractional derivatives [J].
Cao, Hongliang ;
Deng, Zhonghua ;
Li, Xi ;
Yang, Jie ;
Qin, Yi .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2010, 35 (04) :1749-1758
[10]   Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor [J].
Causa, Javier ;
Karer, Gorazd ;
Nunez, Alfredo ;
Saez, Doris ;
Skrjanc, Igor ;
Zupancic, Borut .
COMPUTERS & CHEMICAL ENGINEERING, 2008, 32 (12) :3254-3263