Abstract

Since the superheated steam temperature system of boiler in thermal power plant is characterized as time varying and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional-integral-derivative (PID) cascade control scheme. This paper presents a design method of adaptive PID cascade control system for superheated steam temperature based on inverse model: First, the inner loop and the outer process in the cascade control system are equivalent to a generalized plant. A simplified Takagi–Sugeno (STS) fuzzy model is adopted to identify the inverse model of the generalized plant. By choosing the appropriate structure and optimizing with constrains for the parameters of the inverse model, the organic combination of the PID primary controller with the inverse model is realized. To maintain the structure of the existing conventional PID cascade control system in power plant without change, in the control process, the parameters of the primary controller are adjusted on-line according to the identification result of the inverse model of the generalized plant; thus an adaptive PID cascade control system is formed, which matches with the characteristics of the controlled plant. Through the simulation experiments of controlling superheated steam temperature, it is illustrated that the proposed scheme has good adaptability and anti-interference ability.

1.
Li
,
B.
,
Chen
,
T. K.
, and
Yang
,
D.
, 2005, “
DBSSP—A Computer Program for Simulation of Controlled Circulation Boiler and Natural Circulation Boiler Start Up Behavior
,”
Energy Convers. Manage.
0196-8904,
46
(
4
), pp.
533
549
.
2.
Ghaffari
,
A.
,
Chaibakhsh
,
A.
, and
Lucas
,
C.
, 2007, “
Soft Computing Approach for Modeling Power Plant With a Once-Through Boiler
,”
Eng. Applic. Artif. Intell.
0952-1976,
20
(
6
), pp.
809
819
.
3.
Moelbak
,
T.
, 1999, “
Advanced Control of Superheater Steam Temperatures an Evaluation Based on Practical Applications
,”
Control Eng. Pract.
0967-0661,
7
, pp.
1
10
.
4.
Silva
,
R. N.
,
Shirley
,
P. O.
,
Lemos
,
J. M.
, and
Goncalves
,
A. C.
, 2000, “
Adaptive Regulation of Super-Heated Steam Temperature: A Case Study in an Industrial Boiler
,”
Control Eng. Pract.
0967-0661,
8
(
12
), pp.
1405
1415
.
5.
Goto
,
S.
,
Nakamura
,
M.
, and
Matsumura
,
S.
, 2002, “
Automatic Realization of Human Experience for Controlling Variable Pressure Boilers
,”
Control Eng. Pract.
0967-0661,
10
(
1
), pp.
15
22
.
6.
Lopez
,
S.
,
Figueroa
,
G. A.
, and
Ramirez
,
A. V.
, 2004, “
Advanced Control Algorithms for Steam Temperature Regulation of Thermal Power Plants
,”
Int. J. Electr. Power Energy Syst.
0142-0615,
26
(
10
), pp.
779
785
.
7.
Liu
,
X. J.
, and
Chan
,
C. W.
, 2006, “
Neuro-Fuzzy Generalized Predictive Control of Boiler Steam Temperature
,”
IEEE Trans. Energy Convers.
0885-8969,
21
(
4
), pp.
900
908
.
8.
Ghaffari
,
A.
,
Mehrabian
,
A. R.
, and
Zaheri
,
M. M.
, 2007, “
Identification and Control of Power Plant De-Superheater Using Soft Computing Techniques
,”
Eng. Applic. Artif. Intell.
0952-1976,
20
(
2
), pp.
273
287
.
9.
Hou
,
G. l.
,
Zhang
,
J. F.
,
Liu
,
J. J.
, and
Zhang
,
J. H.
, 2010, “
Multiple-Model Predictive Control Based on Fuzzy Adaptive Weights and Its Application to Main-Steam Temperature in Power Plant
,”
IEEE Conference on Industrial Electronics and Applications
, pp.
668
673
.
10.
Chen
,
J. H.
, and
Huang
,
T. C.
, 2004, “
Applying Neural Networks to On-Line Updated PID Controllers for Nonlinear Process Control
,”
J. Process Control
0959-1524,
14
(
2
), pp.
211
230
.
11.
Takagi
,
T.
, and
Sugeno
,
M.
, 1985, “
Fuzzy Identification of System and Its Applications to Modeling and Control
,”
IEEE Trans. Syst. Man Cybern.
0018-9472,
15
(
1
), pp.
116
132
.
12.
Venkat
,
A. N.
,
Vijaysai
,
P.
, and
Gudi
,
R. D.
, 2003, “
Identification of Complex Nonlinear Processes Based on Fuzzy Decomposition of the Steady State Space
,”
J. Process Control
0959-1524,
13
(
6
), pp.
473
488
.
13.
Wu
,
X. J.
,
Zhu
,
X. J.
,
Cao
,
G. Y.
, and
Tu
,
H. Y.
, 2008, “
Dynamic Modeling of SOFC Based on a T–S Fuzzy Model
,”
Simul. Model. Pract. Theory
,
16
(
5
), pp.
494
504
.
14.
Du
,
H. P.
, and
Zhang
,
N.
, 2009, “
Static Output Feedback Control for Electrohydraulic Active Suspensions via T–S Fuzzy Model Approach
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
131
(
5
), p.
051004
.
15.
Chen
,
Q.
,
Xi
,
Y. G.
, and
Zhang
,
Z. J.
, 1998, “
A Clustering Algorithm for Fuzzy Models Identification
,”
Fuzzy Sets Syst.
0165-0114,
98
(
3
), pp.
319
329
.
16.
Pei
,
J. H.
,
Fan
,
J. L.
, and
Xie
,
V. X.
, 1998, “
Potential Function Partial Weighted Fuzzy C-Mean (PWFCM) Clustering Method
,”
International Conference on Signal Processing Proceedings, ICSP
, Vol.
2
, pp.
1209
1212
.
17.
Bezdek
,
J. C.
,
Coray
,
C.
,
Gunderson
,
R.
, and
Watson
,
J.
, 1981, “
Detection and Characterization of Cluster Substructure
,”
SIAM J. Appl. Math.
0036-1399,
40
(
2
), pp.
339
372
.
18.
Rao
,
C. R.
, and
Toutenburg
,
H.
, 2005,
Linear Model: Least Squares and Alternatives
,
Springer
,
Berlin
.
19.
Corripio
,
A. B.
, 2001,
Tuning of Industrial Control Systems
, 2nd ed.,
Instrument Society of America (ISA)
,
Research Triangle Partk, NC
.
20.
Li
,
H. -X.
, 1997, “
A Comparative Design and Tuning for Conventional Fuzzy Control
,”
IEEE Trans. Syst., Man, Cybern., Part B: Cybern.
1083-4419,
27
(
5
), pp.
884
889
.
21.
Sargolzaei
,
J.
,
Khoshnoodi
,
M.
,
Saghatoleslami
,
N.
, and
Mousavi
,
M.
, 2008, “
Fuzzy Inference System to Modeling of Crossflow Milk Ultrafiltration
,”
Appl. Soft Comput.
1568-4946,
8
(
1
), pp.
456
465
.
22.
Timothy
,
J. R.
, 2010,
Fuzzy Logic With Engineering Applications
, 3rd ed.,
Wiley
,
Chichester, West Sussex
.
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