Adaptive fuzzy sliding mode control in chemical process application

doi:10.3969/j.issn.0438-1157.2012.09.027

CIESC Journal ›› 2012, Vol. 63 ›› Issue (9): 2843-2850.DOI: 10.3969/j.issn.0438-1157.2012.09.027

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Adaptive fuzzy sliding mode control in chemical process application

PENG Yawei, CHEN Juan, LIU Zhanfu, GUO Min

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2012-06-15 Revised:2012-06-22 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the National Natural Science Foundation of China(21146009).

自适应模糊滑模控制在化工过程中的应用

彭亚为, 陈娟, 刘占富, 郭敏

北京化工大学信息科学与技术学院, 北京 100029

通讯作者: 陈娟
作者简介:彭亚为(1987-),男,硕士研究生。
基金资助:
国家自然科学基金项目(21146009)。

Abstract

Abstract: In this paper,a new adaptive fuzzy sliding mode control is used to deal with complex chemical process with multivariable nonlinear and non-minimum phase systems.This method for sliding mode control has good robustness,but existing the chattering problems of controller output.The fuzzy control is used to soften the control signal and simple the fuzzy control,with the combination of sliding mode control can make full use of the system information.By adopting a new adaptive fuzzy factor to adjust proportional variable domain,the control signal is softened and reduces the output chattering of sliding mode controller.Meanwhile,the fuzzy sliding mode control algorithms and stability analysis are given; simplified general fuzzy rule is got.To make the control system has strong robustness,better adaptive ability and high control precision by using the scaling factor to adjust input amount of domain and rules of membership functions on line.Finally,the results of simulation experiment on nonlinear SISO and MIMO chemical models show that even big changes or heavy interference is happened in its operation points; the control system still has good disturbance and strong robustness.

Key words: adaptive fuzzy control, sliding mode control, non-minimum phase system, nonlinear, van-de-vusse reactor

摘要： 为有效处理多变量、非线性及非最小相位系统的复杂化工过程,提出了一种新型的自适应模糊滑模控制,该方法针对滑模控制鲁棒性好但存在抖振的问题,采用模糊控制柔化控制信号,而与滑模控制的结合可以充分利用系统信息,简化模糊控制;在此基础上提出一种新的自适应调整比例因子来进行模糊变论域,柔化了控制信号并减小了滑模控制器输出的抖振。并给出模糊滑模控制的算法和稳定性分析,得到简化后的通用模糊规则库,可通过比例因子在线调节输入量的论域,使构成的控制系统具有很强的鲁棒性、较好的自适应能力和较高的控制精度。最后对于非线性单输入单输出(SISO)和多输入多输出(MIMO)化工模型进行仿真研究,结果表明即使工况点发生大的变化或受到较大干扰时,仍具有良好的抗扰动能力和很强的鲁棒性。

关键词: 自适应模糊控制, 滑模控制, 非最小相位系统, 非线性, van-de-vusse反应

CLC Number:

TP29

PENG Yawei, CHEN Juan, LIU Zhanfu, GUO Min. Adaptive fuzzy sliding mode control in chemical process application[J]. CIESC Journal, 2012, 63(9): 2843-2850.

彭亚为, 陈娟, 刘占富, 郭敏. 自适应模糊滑模控制在化工过程中的应用[J]. 化工学报, 2012, 63(9): 2843-2850.

References 15

[1]	Ho D W C,Niu Y G.Robust fuzzy design for nonlinear uncertain stochastic systems via sliding-mode control[J].IEEE Transactions on Fuzzy Systems,2007,15(3):350-358
[2]	Yoo B,Ham W.Adaptive fuzzy sliding mode control of nonlinear system[J].IEEE Transactions on Fuzzy Systems,1998,6(2):315-321
[3]	Hou Guolian(侯国莲),Li Quan(李泉),Zhang Jianhua(张建华).Research of MIMO adaptive fuzzy sliding-mode control with variable universe its application to the coordinated control system in unit[J].Proceeding of the Chinese Society for Electrical Engineering(中国电机工程学报),2005,25(9):114-121
[4]	Shahnazi R,Shanechi H M.Position control of induction and DC servomotors:a novel adaptive fuzzy PI sliding mode control[J].IEEE Transactions on Energy Conversion,2008,23(1):138-147
[5]	Wai R J,Lin C M,Hsu C F.Adaptive fuzzy sliding-mode control for electrical servo drive[J].Fuzzy Sets and Systems,2004,143(2):295-310
[6]	Ho H F,Wong Y K,Rad A B.Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems[J].Simulation Modelling Practice and Theory,2009,17(7):1199-1210
[7]	Roopaei M,Zolghadri J M.Chattering-free fuzzy sliding mode control in MIMO uncertain systems[J].Nonlinear Analysis,2009,71(10):4430-4437
[8]	Shahraz A,Bozorgmehry Boozarjomehry R.A fuzzy sliding mode control approach for nonlinear chemical processes[J].Control Engineering Practice,2009,17(5):541-550
[9]	Li Hongxing(李洪兴),Miao Zhihong(苗志红),Wang Jiayin(王加银).Variable universe stable adaptive fuzzy control of nonlinear system[J].Science in China:Ser. E(中国科学:E辑),2002,32(2):21l-223
[10]	Dai Wenzhan(戴文战),Wang Xiao(王晓).Self-tuning fuzzy predictive functional control strategy for cascade time-delay system[J].CIESC Journal(化工学报),2010,61(8):2132-2137
[11]	Chen Juan(陈娟),He Zefang(贺泽芳),Qi Xin(祁欣),Gu Liheng(谷立恒).Filter time constant self-adjusting internal model control based on variable domain fuzzy control for time-delay uncertain system//2010 Seventh International Conference on Fuzzy System and Knowledge Discovery.FSKD 2010.Yantai,Shandong,China,2010:672-676
[12]	Zhang J H,Shi P,Xia Y Q.Robust adaptive sliding-mode control for fuzzy systems with mismatched uncertainties[J].IEEE Transactions on Fuzzy Systems,2010,18(4):700-713
[13]	Tang Y Z,Zhang N Y,Li Y D.Stable fuzzy adaptive control for a class of nonlinear systems[J].Fuzzy Sets and Systems,1999,104(2):279-288
[14]	Chan P T,Rad A B,Wang J.Indirect adaptive fuzzy sliding mode control(Ⅱ):Parameter projection and supervisory control[J].Fuzzy Sets and Systems,2001,122(1):31-43
[15]	Chen C T,Peng S T.A sliding mode control scheme for non-minimum phase non-linear uncertain input-delay chemical processes[J].Journal of Process Control,2006,16(1):37-51

Adaptive fuzzy sliding mode control in chemical process application

自适应模糊滑模控制在化工过程中的应用

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