基于T-S模糊模型的船舶柴油机动态辨识

肖健梅; 王锡淮; 李顺林

基于T-S模糊模型的船舶柴油机动态辨识

1.
上海海事大学电气自动化系, 上海 200135
2.
兰州交通大学信息中心, 甘肃兰州 730070

基金项目:

上海市教育委员会科研重点项目 04FA02

上海市重点学科建设项目 T0602

详细信息

作者简介:
肖健梅(1962-), 女, 辽宁大连人, 上海海事大学教授, 从事轮机自动化研究

中图分类号: U664.121
计量
- 文章访问数: 317
- HTML全文浏览量: 134
- PDF下载量: 207
- 被引次数: 0
出版历程
- 收稿日期: 2005-06-15
- 刊出日期: 2006-03-25

Dynamic Identification of Ship Diesel Engine Based on T-S Fuzzy Model

1.
Department of Electrical Engineering and Automation, Shanghai Maritime University, Shanghai 200135, China
2.
Information Center, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

More Information

Author Bio:
XIAO Jian-mei(1962-), female, professor, 86-21-58603674, jmxiao@cen.shmtu.edu.cn

摘要

摘要: 考虑到船舶柴油机模型的非线性和负载的不确定性, 用T-S (Takagi-Sugeno) 模糊辨识方法建立了船舶柴油机的动态模型。采用模糊聚类简化了T-S模糊规则数的确定和前提中隶属度函数参数的生成, 用加权最小二乘算法得出辨识结论中的线性参数。对船舶柴油机在稳态运行工况下作小偏差工况扰动实验, 得到在油门尺度和负载变化下柴油机转速、涡轮增压器转速等输出数据, 利用该数据建立了描述柴油机动态性能的T-S模糊模型。仿真结果表明, 利用该算法能有效地辨识出柴油机转速、涡轮增压器转速、增压压力、空冷器压力等输出在小工况扰动下的变化模型。
- 轮机工程 /
- 柴油机 /
- 系统辨识 /
- T-S模糊模型 /
- 模糊聚类
Abstract: Considering the nonlinear of ship diesel engine model and the uncertainty of its loads, the paper investigated a T-S fuzzy identification algorithm to build ship diesel engine dynamic model.The number of fuzzy rules and the parameters of membership functions in the antecedent of T-S model were generated by fuzzy clustering method, the linear parameters of identification consequent in each rule were separately identified by weighted least square method.The output data of diesel speeds and turbocharger speeds under different throttle scales and loads were gained from some experimentations, when diesel engine was disturbed by a small deviation signal in its steady running state, the data were used to build a T-S fuzzy model of ship diesel engine, which could express the dynamic characteristic of diesel engine.Simulation result shows that the method is effective to identify the models of diesel engine speed, turbocharger speed, turbocharger pressure and air condenser pressure under small deviations.
- marine engineering /
- diesel engine /
- system identification /
- T-S fuzzy model /
- fuzzy clustering

HTML全文

图 1 涡轮增压柴油机结构

Figure 1. Structure of Turbocharged Diesel Engine

下载: 全尺寸图片幻灯片

图 2 输入信号

Figure 2. Input Signals

下载: 全尺寸图片幻灯片

图 3 柴油机转速曲线

Figure 3. Curves of Diesel Engine Speed

下载: 全尺寸图片幻灯片

图 4 隶属度函数

Figure 4. Membership Functions

下载: 全尺寸图片幻灯片

图 5 涡轮增压器转速曲线

Figure 5. Curves of Turbo-Charger Speed

下载: 全尺寸图片幻灯片

图 6 隶属度函数

Figure 6. Membership Functions

下载: 全尺寸图片幻灯片

表 1 结论参数

Table 1. Consequent Parameters

规则序号	a_i			b_i
1	0.983 9	0.030 8	-0.044 5	0.000 5
2	0.941 5	0.023 2	-0.045 5	-0.000 9
3	0.748 6	0.016 0	-0.043 7	0.000 2

下载: 导出CSV

表 2 结论参数

Table 2. Consequent Parameters

规则序号	a_i					b_i
1	1.299 4	0.069 1	-0.408 8	0.003 3	-0.001 2	0
2	1.194 9	0.194 4	-0.412 3	0.002 7	-0.000 6	0
3	1.261 5	0.099 1	-0.395 7	0.002 8	-0.000 7	0

下载: 导出CSV

参考文献(10)

[1]	王赟松, 褚福磊, 刘亚东. 基于神经网络的柴油机故障诊断[J]. 交通运输工程学报, 2003, 3 (4): 44-47. http://transport.chd.edu.cn/article/id/200304016 WANG Yun-song, CHU Fu-lei, LIU Ya-dong. Fault Diagnosis of Diesel Engine Based on Neural Network[J]. Journal of Traffic and Transportation Engineering, 2003, 3 (4): 44-47. (in Chinese) http://transport.chd.edu.cn/article/id/200304016
[2]	吴桂涛, 尹自斌, 孙培廷. 主机缸套冷却水出口温度控制方法[J]. 交通运输工程学报, 2003, 3 (4): 35-38. http://transport.chd.edu.cn/article/id/200304026 WU Gui-tao, YIN Zi-bin, SUN Pei-ting. Temperature Control Method for Jacket Cooling Water System of Main Marine Diesel Engine[J]. Journal of Traffic and Transportation Engineering, 2003, 3 (4): 35-38. (in Chinese) http://transport.chd.edu.cn/article/id/200304026
[3]	肖健梅, 王锡淮, 鲍敏中. 神经网络在船舶主柴油机建模中的应用[J]. 船舶工程, 2001, 23 (6): 46-48. https://www.cnki.com.cn/Article/CJFDTOTAL-CANB200106011.htm XIAO Jian-mei, WANG Xi-huai, BAO Min-zhong. Application of Neural Network to the Ship Engine Model[J]. Ship Engineering, 2001, 23 (6): 46-48. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CANB200106011.htm
[4]	ZHANG Jian, KANG Jing-li, WANG Min-nan. A RBF Network Based Nonlinear Modeling of Diesel Engine[J]. Transactions of CSICE, 2003, 21 (3): 221-226.
[5]	李顺林, 肖健梅. 基于T-S模糊模型的非线性系统辨识[J]. 兰州交通大学学报: 自然科学版, 2004, 23 (4): 58-60. https://www.cnki.com.cn/Article/CJFDTOTAL-LZTX200404017.htm LI Shun-lin, XIAO Jian-mei. Identification of Nonlinear Systems by Takagi-Sugeno Fuzzy Model[J]. Journal of Lanzhou Jiaotong University: Natural Sciences, 2004, 23 (4): 58-60. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LZTX200404017.htm
[6]	TAKAGI T, SUGENO M. Fuzzy Identification of Systemsand Its Application to Modeling and Control[J]. IEEE Transactions on Systems, Man and Cybernetics, 1985, 15 (1): 116-132.
[7]	YOSHI NARI Y, PEDRYCZ W, HIROTA K. Construction of Fuzzy Models Through Clustering Techniques[J]. Fuzzy Setsand Systems, 1993, 54 (2): 157-165.
[8]	王锡淮, 李少远, 席裕庚. 基于自适应模糊聚类的神经网络软测量建模方法[J]. 控制与决策, 2004, 19 (8): 951-953. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200408026.htm WANG Xi-huai, LI Shao-yuan, XI Yu-geng. Method of Neural Network Soft Sensor Modeling Based on Adaptive Fuzzy Clustering[J]. Control and Decision, 2004, 19 (8): 951-953. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200408026.htm
[9]	翁史烈. 船舶动力装置仿真技术[M]. 上海: 上海交通大学出版社, 1991.
[10]	吴晓莉. Matlab辅助模糊系统设计[M]. 西安: 西安电子科技大学出版社, 2002.