Determination of the PID Controller Parameters by Modified Binary Particle Swarm Optimization Algorithm

被引:5
作者
Ma, Chengxi [1 ]
Qian, Lin [2 ]
Wang, Ling [1 ]
Menhas, Muhammad Ilyas [1 ]
Fei, Minrui [1 ]
机构
[1] Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Sch Mechatron & Automat, Shanghai 200072, Peoples R China
[2] Shanghai Elect Power Construct Co Ltd, Shanghai 200031, Peoples R China
来源
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5 | 2010年
关键词
Circulating Fluidized Bed Boiler; Discrete Binary PSO; PID Control; PSO; GENETIC ALGORITHM;
D O I
10.1109/CCDC.2010.5498741
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 control of the bed temperature which is the key variable in Circulating Fluidized Bed Boiler (CFBB) system is a challenging problem due to its characteristics of the strong nonlinearity, large time delay and time-varying. It is very difficult to tune its PID controller based on the traditional PID tuning methods such as the classic Z-N method. Therefore, a Modified Binary Particle Swarm Optimization (MBPSO) algorithm is introduced to tune the parameters of bed temperature controller of CFBB in this paper, which is easy to implement and works with excellent optimization ability. To test and verify the effectiveness of the proposed method, the control performance on CFBB's bed temperature control is compared with Z-N method, and the tuning methods based on the standard PSO and discrete binary PSO. The simulation results show that PID tuning based MBPSO is valid and outperforms the ones based on the original discrete binary PSO algorithm and Z-N method, while the standard PSO fails to tackle this problem.
引用
收藏
页码:2689 / +
页数:2
相关论文
共 21 条
[1]  
[Anonymous], P INT C CONTR AUT SY
[2]  
[Anonymous], 2006, CONTROL ENG CHINA
[3]  
[Anonymous], 1995, 1995 IEEE INT C
[4]   A multi-crossover genetic approach to multivariable PID controllers tuning [J].
Chang, Wei-Der .
EXPERT SYSTEMS WITH APPLICATIONS, 2007, 33 (03) :620-626
[5]  
Cohen G.H., 1953, ASME, V75, P827
[6]   Use of neural networks for quick and accurate auto-tuning of PID controller [J].
D'Emilia, Giulio ;
Marra, Antonio ;
Natale, Emanuela .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2007, 23 (02) :170-179
[7]  
Eberhart RC, 2000, IEEE C EVOL COMPUTAT, P84, DOI 10.1109/CEC.2000.870279
[8]  
Fu P, 2005, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, P825
[9]   A particle swarm optimization approach for optimum design of PID controller in AVR system [J].
Gaing, ZL .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2004, 19 (02) :384-391
[10]   The self-tuning PID control in a slider-crank mechanism system by applying particle swarm optimization approach [J].
Kao, Chih-Cheng ;
Chuang, Chin-Wen ;
Fung, Rong-Fong .
MECHATRONICS, 2006, 16 (08) :513-522