铣削加工过程的模糊PID控制
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摘要
金属铣削加工过程是一种具有非线性、时变性和影响因素不确定的复杂系统,难以用精确的数学模型来描述。随着加工技术和生产自动化程度的提高,对加工系统自动控制的鲁棒性能要求越来越高。而传统的基于精确数学模型的控制算法对此类复杂系统的控制难于胜任,因此,不依赖数学模型的智能控制将为加工过程提供新的控制方法。基于这种考虑,本文将模糊控制引入到铣削加工过程,并结合PID控制,构成模糊PID复合控制方法,以期实现对铣削加工过程中诸如变工况的一类不确定信息进行有效而稳定的控制。
本文在分析闭环铣削加工控制系统的不确定性的基础上,论述了PID控制的原理,讨论了PID参数对其控制性能的影响。接着提出了铣削加工过程的PID控制,通过假定一些工况的变化模拟加工过程中出现的不确定信息。计算机仿真实验结果显示在背吃刀量突变时超调量很大。为了克服这一缺点,进而提出铣削加工过程的模糊控制,以使得切削力尽可能快速、无差、稳定地跟踪设定的期望值。文中讨论了模糊控制器的设计方法和步骤,分析了模糊控制的特点,构建了铣削加工过程的模糊控制器并进行了仿真实验。
在此基础上,提出了铣削加工过程的模糊PID控制。用Matlab软件分别对这三种控制方法进行了仿真实验,并对仿真结果进行了深入的分析。最后,在XK5140数控铣床上,实现了对变工况铣削过程的闭环控制,针对上述的控制方法进行了实际切削加工的实验。
计算机仿真结果表明,铣削加工过程的模糊控制响应速度很快,鲁棒性能好,但存在一定的稳态误差;PID控制的稳态性能很好,但超调量比较大;模糊PID控制保持了PID控制良好的稳态性能,超调量明显减小,响应速度很快。对比三种控制方法,可以说,利用模糊逻辑整定PID参数,可实现性能良好的模糊PID复合控制,对于变工况为代表的一类不确定信息的控制具有很强的鲁棒性能。这在实际加工实验中得到了验证。
The metal-milling manufacturing system is a time-varying nonlinear dynamic system. As the improvement of machining technology and production automation, the robust performance requirements of automatic control of manufacturing system are higher and higher. The conventional control algorithms based on accurate mathematical models are not competent for controlling the complicated dynamic systems. Therefore, intelligent control, which is not based on accurate mathematical model, will provide a new approach for those complicated dynamic systems. In this paper, fuzzy control is imported to the milling process control system, combined with PID, for controlling the manufacturing process.
In the thesis, the uncertainty of closed loop milling control system is discussed in detail. Principle of PID control is discussed, and the effect of PID parameters on the performance of PID controller is analysised. And then, PID control for machining process is proposed. And then Fuzzy Controller (FC) for machining process is proposed to make cutting force reach the set desired value rapidly, steadily, and without error. The mechanism and peculiarity of FC is analyzed deeply, and fuzzy controller for milling process is designed. By assuming variation of the Depth of cutting, uncertainty of milling process is simulated, and simulation of both of the two way of control for the milling process is excuted with personal computer(PC), and results is analysised deeply.
Basing on the work above, fuzzy PID controller for milling process is proposed. By presupposition of the variety of work situation, using Matlab to simulate the three controllers aboved for uncertain milling process. At the end, an experiment based on these controllers is progressed, with a closed loop milling control system by milling machine XK5140.
It is indicated, from the results of the simulations and the experiments, that the response speed of FC for milling process is rapid, but a little steady-state error exists; to PID, the result is steady but overshoot is much larger; fuzzy PID has strongpoints but not defect, comparing with FC and conventional PID. In a word, it is advisable to import fuzzy mechanism to setting the parameters of PID for uncertain milling process.
本文在分析闭环铣削加工控制系统的不确定性的基础上,论述了PID控制的原理,讨论了PID参数对其控制性能的影响。接着提出了铣削加工过程的PID控制,通过假定一些工况的变化模拟加工过程中出现的不确定信息。计算机仿真实验结果显示在背吃刀量突变时超调量很大。为了克服这一缺点,进而提出铣削加工过程的模糊控制,以使得切削力尽可能快速、无差、稳定地跟踪设定的期望值。文中讨论了模糊控制器的设计方法和步骤,分析了模糊控制的特点,构建了铣削加工过程的模糊控制器并进行了仿真实验。
在此基础上,提出了铣削加工过程的模糊PID控制。用Matlab软件分别对这三种控制方法进行了仿真实验,并对仿真结果进行了深入的分析。最后,在XK5140数控铣床上,实现了对变工况铣削过程的闭环控制,针对上述的控制方法进行了实际切削加工的实验。
计算机仿真结果表明,铣削加工过程的模糊控制响应速度很快,鲁棒性能好,但存在一定的稳态误差;PID控制的稳态性能很好,但超调量比较大;模糊PID控制保持了PID控制良好的稳态性能,超调量明显减小,响应速度很快。对比三种控制方法,可以说,利用模糊逻辑整定PID参数,可实现性能良好的模糊PID复合控制,对于变工况为代表的一类不确定信息的控制具有很强的鲁棒性能。这在实际加工实验中得到了验证。
The metal-milling manufacturing system is a time-varying nonlinear dynamic system. As the improvement of machining technology and production automation, the robust performance requirements of automatic control of manufacturing system are higher and higher. The conventional control algorithms based on accurate mathematical models are not competent for controlling the complicated dynamic systems. Therefore, intelligent control, which is not based on accurate mathematical model, will provide a new approach for those complicated dynamic systems. In this paper, fuzzy control is imported to the milling process control system, combined with PID, for controlling the manufacturing process.
In the thesis, the uncertainty of closed loop milling control system is discussed in detail. Principle of PID control is discussed, and the effect of PID parameters on the performance of PID controller is analysised. And then, PID control for machining process is proposed. And then Fuzzy Controller (FC) for machining process is proposed to make cutting force reach the set desired value rapidly, steadily, and without error. The mechanism and peculiarity of FC is analyzed deeply, and fuzzy controller for milling process is designed. By assuming variation of the Depth of cutting, uncertainty of milling process is simulated, and simulation of both of the two way of control for the milling process is excuted with personal computer(PC), and results is analysised deeply.
Basing on the work above, fuzzy PID controller for milling process is proposed. By presupposition of the variety of work situation, using Matlab to simulate the three controllers aboved for uncertain milling process. At the end, an experiment based on these controllers is progressed, with a closed loop milling control system by milling machine XK5140.
It is indicated, from the results of the simulations and the experiments, that the response speed of FC for milling process is rapid, but a little steady-state error exists; to PID, the result is steady but overshoot is much larger; fuzzy PID has strongpoints but not defect, comparing with FC and conventional PID. In a word, it is advisable to import fuzzy mechanism to setting the parameters of PID for uncertain milling process.
引文
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[37]杨志永,黄田,张大卫.数控铣削过程模糊-PID复合控制.天津大学学报,2000(3):280-284
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[2]Lauderbaugh L K,Ulsoy A G Dynamic Modeling for Control of the Milling Process.Sensor and Controls for Manufacturing.ASME,1985,18(PED):149-158
[3]Ulsoy A G,Koren Y.Control of Machining Processes.ASME J.Dyn.Syst.,Meas.,Control,1993,115(2):301-308
[4]倪其民,李祥,伍俊等.加工过程约束型自适应控制方法综述.机床与液压,2001(2):3-6
[5]Huang S-J,Yan M-T.Theoretical and Experimental Study of PI Control,Adaptive Control and Variable Structure Control for Converted Traditional Milling Machines.Int.J Mach.Tools & Manufact.,1993,33(5):695-712
[6]Fussell B K,Srinivasan K.Adaptive Control of Force in End Milling Operations-An Evaluation of Available Algorithms.J.Manuf.Systems,10(1):8-20
[7]Masory O,Koren Y.Stability of Analysis of a Constant Force Adaptive Control System for Turning.ASME J.Eng.for Industry,1985,107(4):295-299
[8]Tomizuka M,Zhang S.Modeling and Conventional/Adaptive PI Control of a Lathe Cutting Process.ASME J.Dyn.Syst.,Meas.,Control,1988,110(4):350-354
[9]L.A.Zadeh.Fuzzy Sets.Information and Control.1965,8(3):338-353
[10]温度.基于元学习的加工过程多策略协作控制研究.[博士学位论文].广州:华南理工大学,2003
[11]鲍新福,都志杰,王芳君.自调整比例因子控制器.自动化学报,1987,13(2):129-133
[12]毛宗源,狄净.自调整比例因子控制器控制锅炉燃烧过程.自动化学报,1991,17(5):611-614
[13]刘向杰,柴天佑,刘红波.动力锅炉燃烧系统的模糊控制策略.自动化学报,1998,26(4):534-538
[14]A Linkens,J N ie.Construction Rule-bases for Multivariable Fuzzy Control By Self-learning,Part Ⅰ:System Structure and Learning Algorithms.Int J Systems Sci,1993,24(1):111-127
[15]Khee F V D,D A Linkens J N ie.Constructing Rule-bases for Multivariable Fuzzy Control by Self-learning,Part Ⅱ:Rule-base Formation and Blood Pressure Control Application.IntjSystems.Sci,1993,24(1):129-157
[16]R Layne et al.Fuzzy Model Reference Learning Control for Cargo Ship Steering. IEEE Contr Syst,1993,13(5):23-24
[17]Kosko B.Neural Networks And Fuzzy Systems,Pretice-Hall,Englewood Cliffs,1992
[18]应行仁,曾南.采用B-P神经网络记忆模糊规则的控制.自动化学报,1991,17(1):63-67
[19]B Kosko,S G Kong.Adaptive Fuzzy Systems for Backing up a Track-and-trailer.IEEE Trans Neural Networks,1992,3(2):211-233
[20]Chin-Teng Lin,Neural-network-based Fuzzy Logic Control and Decision System.IEEE Trans.Computers,1991,40(12):1320-1336
[21]C L Chen,W C Chen.Self-organizing Neural Control System Design for Dynamic Processes.Int J System Sci,1993,2(8):1487-1507
[22]K J Hunt.Neural Networks for Control Systems-A Survey.Automatica,1992,28(6):1083-1112
[50]Gupta M M,Trojan G M,Kiszka J B.Conrtrollability of Fuzzy Control Systems.IEEE Trans.Syst.Man and Cy-bem,1986,16(4):576-582
[23]Kiszka J B,Gupta M M,Nlkiforeuk P N.Energetistic Stability of Fuzzy Dynamic Systems.IEEE Trans.Syst.Man and Cybern,1985,15(5):583-792
[24]T.Takagi,M Sugeno.Fuzzy Identification of Systems and its Applications to Modeling and Control.IEEE Trans.Syst.,Man and Cyber.,1985,15:116-132
[25]Kazuo Tanaka,Michio Sugeno.Stability Analysis and Design of Fuzzy Control Systems.Fuzzy Sets and Systems,1992,45:135-156
[26]Chieh-Li Chen,Pey-Chung Chenand Cha'o-Kuang Chen.Analysis and Design of Fuzzy Control System.Fuzzy Sets and Systems,1993,57:125-140
[27]Kazuo Tanaka,Manabu Sano.A Robust Stabilization Problem of Fuzzy Control Systems and Its Application to Backing up Control of a Track-Trailer.IEEE Tran Fuzzy Systems,1994,2(2):119-133
[28]Wang L-X,Mendel J M.Fuzzy Control of Nonlinear Systems,IEEE Tran.Fuzzy Systems,1993,1(2):146-155
[29]Wang L-X.A Supervisory Controller for Fuzzy Control Systems that Guarantees Stability,IEEE Tran.Automatic Control,1994,39(9):1845-1847
[30]Hu Y,Ramamoorthy P A.Design of Stable RecrrrentFuzzy-Logic-Controllers Proc.Of IEEE Intl.Conf.on Fuzzy Systems.1994,918-923
[31]陈建勤,吕剑虹,陈来九.模糊控制系统的闭环模型及稳定性分析.自动化学报,1994,20(1):1-10
[32]陈建勤,吕剑虹,陈来九.利用关系矩阵分析模糊控制系统的稳定性.控制理论与 应用,1995,12(5):635-639
[33]姚锡凡.智能加工系统的模糊与神经自适应控制.[博士学位论文].广州:华南理工大学,1999
[34]赖兴余.加工过程智能控制的研究现状与展望.自动化博览.1997(4):30-31
[35]马平,王珉.加工过程自适应模糊控制.组合机床与自动化加工技术,1996(4):6-10
[36]彭永红,陈统坚,周泽华.加工过程的智能模糊控制研究.华南理工大学学报,1996,24(8):21-25
[37]杨志永,黄田,张大卫.数控铣削过程模糊-PID复合控制.天津大学学报,2000(3):280-284
[38]龙成和,姚锡凡.加工过程的切削力模拟.组合机床与自动化加工技术,2000(5)
[39]窦振中.模糊逻辑控制技术及其应用.北京:北京航空航天大学出版社,1995
[40]Carrillo F J,Rotella F,Zadshakoyan M.Delta Approach Robust Controller for Constant Turning Force Regulation.Control Eng.Pract.,1999,7(11):1321-1331.
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