基于自适应变异概率二进制PSO的球磨制粉系统控制(英文)

被引：12

作者：

Muhammad Ilyas MENHAS ^{[1
]}

费敏锐 ^{[1
]}

王灵 ^{[1
]}

钱麟 ^{[2
]}

机构：

[1] 上海大学机电工程与自动化学院上海市电站自动化技术重点实验室

[2] 上海电力建设启动调整试验所

来源：

系统仿真学报 | 2011年 / 08期

关键词：

粒子群优化算法; PID; 球磨制粉系统; 群智能;

D O I：

10.16182/j.cnki.joss.2011.08.044

中图分类号：

TP273 [自动控制、自动控制系统];

学科分类号：

080201 ; 0835 ;

摘要：

研究了一种新型自适应变异概率二进制粒子群算法。提出的自适应变异策略通过以一定的概率进行动态比特转换帮助算法更好地保持种群多样性和搜索新解,从而有效防止算法早熟。最终将提出的自适应变异概率二进制粒子群算法(adaptive mutation based pobability binary PSO,APBPSO)用于球磨制粉系统这一复杂多变量对象的PID控制器优化设计中以验证算法性能。多变量控制器分别采用了三种多目标优化目标函数,仿真结果表明提出APBPSO能有效避免陷入局部最优,其对控制器优化性能优于粒子群优化算法、离散二进制粒子群优化算法及基本的概率二进制粒子群优化算法。

引用

页码：1568 / 1574

页数：7

共 10 条

[1] Coordinated Controller Tuning of a Boiler Turbine Unit with New Binary Particle Swarm Optimization Algorithm [J].

Muhammad Ilyas Menhas .

International Journal of Automation & Computing, 2011, (02) :185-192

[2]

Design of PID controller with incomplete derivation based on differential evolution algorithm[J]. Wu Lianghong1,2, Wang Yaonan2, Zhou Shaowu1 ＆ Tan Wen1 1. School of Information and Electric Engineering, Hunan Univ. of Science and Technology, Xiangtan 411201, P. R. China;2. Coll. of Electric and Information Engineering, Hunan Univ., Changsha 410082, P. R. China (Received November 5, 2006).Journal of Systems Engineering and Electronics. 2008(03)

[3] BP神经网络PID控制器在球磨机控制中的应用 [J].

梁兆阳 ;

孙建平 ;

孙静 .

仪器仪表用户, 2008, (02) :42-43

[4] 球磨机多模型PID型神经元网络控制系统 [J].

程启明 ;

郑勇 .

中国电机工程学报, 2008, (02) :103-109

[5]

A Neural Intellectual Decoupling Control Strategy for a Power Plant Ball Miller[J]. Hong Zhou Department of Automation, Wuhan University, Wuhan 430072, PRC Ming-Hui Zhong GuangQian Electric Power CO. LTD, Shenzhen 518051, PRC.International Journal of Automation and Computing. 2005(01)

[6] Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization [J].

Zhao, S. -Z. ;

Iruthayarajan, M. Willjuice ;

Baskar, S. ;

Suganthan, P. N. .

INFORMATION SCIENCES, 2011, 181 (16) :3323-3335

[7] Covariance matrix adaptation evolution strategy based design of centralized PID controller [J].

Iruthayarajan, M. Willjuice ;

Baskar, S. .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (08) :5775-5781

[8] Computational intelligence approach to PID controller design using the universal model [J].

Sumar, Rodrigo Rodrigues ;

Rodrigues Coelho, Antonio Augusto ;

Coelho, Leandro dos Santos .

INFORMATION SCIENCES, 2010, 180 (20) :3980-3991

[9] Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell [J].

Tang, Jian ;

Zhao, Li-jie ;

Zhou, Jun-wu ;

Yue, Heng ;

Chai, Tian-you .

MINERALS ENGINEERING, 2010, 23 (09) :720-730

[10]

Particle swarm optimization. Kennedy J, Eberhart RC. Proceedings of the IEEE International Joint Conference on Neural Networks . 1995

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