液压伺服驱动位置系统的智能控制
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摘要
本课题是在已完成的国家“九五”科技攻关项目“结晶器非正弦振动的研究”的基础上，针对该项目中的液压伺服驱动连铸结晶器振动模拟装置所做的控制方面的研究。由于液压伺服驱动位置系统中存在参数摄动、外干扰和非线性摩擦力等问题，而模糊控制不需要精确的数学模型，神经网络可以有效地解决非线性问题，因此本文采用模糊控制和神经网络等控制方法对液压伺服位置系统进行控制，具有较大的实际意义和理论价值。
本文首先利用已得到的液压伺服位置系统被控对象的简化数学模型作为控制器设计的依据。在对模糊控制和神经网络理论的特点和应用等进行概述的基础上，分别设计了模糊滑模控制器、基于模糊自学习的滑模控制器、基于改进Elman网络辨识的模糊神经网络控制器、模糊神经网络滑模控制器，以及针对液压伺服系统的非线性机理模型设计了模糊神经网络滑模控制器。其中，针对液压伺服系统非线性机理模型的模糊神经网络滑模控制方法，是以对伺服阀和液压缸的非线性函数的分析得出的状态方程为基础，并结合Lyapunov函数方法设计了控制器，从而有效解决了液压伺服系统的非线性问题。通过对所设计控制器的仿真研究表明，本文所设计的液压伺服位置控制系统具有较好的动静态性能和较高的跟踪精度，证明了所设计控制器的可行性。
Based on the having been finished project of the state "the ninth five years" tackling key scientific and technological problems, this paper makes some detailed research on the continuous casting analogy device in the laboratory of our school. As far as the hydraulic servo position system is concerned, there are parameters variety, outside disturbance and the nonlinear friction in it. In fuzzy control method, it is well known that accurate model of the system is not needed. Moreover, neural network can solve the problems caused by non-linear effectively. As a result, in this paper, the two intelligent control methods are combined to control the hydraulic servo position system, and that has practical meaning and theory value.
In this paper, the simplified models derived from the hydraulic system are used to design the controllers. First the summary of the characteristics and application of fuzzy control and neural network theory are narrated. Then, the fuzzy sliding mode controller, the sliding mode controller based on fuzzy self-learning, fuzzy neural network controller based on the modified Elman neural network identification, the fuzzy neural network sliding mode controller and a fuzzy neural network sliding mode controller based on the hydraulic mechanical model are proposed. Where, with Lyapunov function and the state equation that is analyzed from the nonlinear function of servo valve and hydraulic cylinder, the fuzzy-neural network sliding mode controller based on the mechanical model of hydraulic servo system is designed. Consequently, This controller is availability to the nonlinear problem existing in the hydraulic system. At last, The simulation shows that hydraulic servo position control system has good per
formances in the dynamic and static state control accuracy, thus the feasibility of these control methods are proved effectively.
本文首先利用已得到的液压伺服位置系统被控对象的简化数学模型作为控制器设计的依据。在对模糊控制和神经网络理论的特点和应用等进行概述的基础上，分别设计了模糊滑模控制器、基于模糊自学习的滑模控制器、基于改进Elman网络辨识的模糊神经网络控制器、模糊神经网络滑模控制器，以及针对液压伺服系统的非线性机理模型设计了模糊神经网络滑模控制器。其中，针对液压伺服系统非线性机理模型的模糊神经网络滑模控制方法，是以对伺服阀和液压缸的非线性函数的分析得出的状态方程为基础，并结合Lyapunov函数方法设计了控制器，从而有效解决了液压伺服系统的非线性问题。通过对所设计控制器的仿真研究表明，本文所设计的液压伺服位置控制系统具有较好的动静态性能和较高的跟踪精度，证明了所设计控制器的可行性。
Based on the having been finished project of the state "the ninth five years" tackling key scientific and technological problems, this paper makes some detailed research on the continuous casting analogy device in the laboratory of our school. As far as the hydraulic servo position system is concerned, there are parameters variety, outside disturbance and the nonlinear friction in it. In fuzzy control method, it is well known that accurate model of the system is not needed. Moreover, neural network can solve the problems caused by non-linear effectively. As a result, in this paper, the two intelligent control methods are combined to control the hydraulic servo position system, and that has practical meaning and theory value.
In this paper, the simplified models derived from the hydraulic system are used to design the controllers. First the summary of the characteristics and application of fuzzy control and neural network theory are narrated. Then, the fuzzy sliding mode controller, the sliding mode controller based on fuzzy self-learning, fuzzy neural network controller based on the modified Elman neural network identification, the fuzzy neural network sliding mode controller and a fuzzy neural network sliding mode controller based on the hydraulic mechanical model are proposed. Where, with Lyapunov function and the state equation that is analyzed from the nonlinear function of servo valve and hydraulic cylinder, the fuzzy-neural network sliding mode controller based on the mechanical model of hydraulic servo system is designed. Consequently, This controller is availability to the nonlinear problem existing in the hydraulic system. At last, The simulation shows that hydraulic servo position control system has good per
formances in the dynamic and static state control accuracy, thus the feasibility of these control methods are proved effectively.
引文
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5 胡佑德，马东升，张莉松．伺服系统原理与设计．北京：北京理工大学出版社，1999：6-9
6 L. A. Zadeh. Fuzzy Sets. Informat Control. 1965, (8):338-353
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31 Shoureshi, K. Rahmani. Derivation and Application of an expert Fuzzy Optimal Control System. Fuzzy Sets and Systems, 1992, (49):93-101
32 Palm. Sliding Mode Fuzzy Control. Proc. Internet. IEEE Conf. on Fuzzy Systems, San Diego, 1992:519-526
33 P. Filev. Dimitar, R. Y. Ronald. On the Analysis of Fuzzy Logic Controllers. Fuzzy Sets and Systems, 1994,(68):39-66
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2 王春行．液压伺服控制系统．北京：机械工业出版社，1981：1-12
3 孙文质．液压控制系统．国防工业出版社，1985：184-197
4 冯国南．现代伺服理论的分析与设计．北京：机械工业出版社，1990：13-17
5 胡佑德，马东升，张莉松．伺服系统原理与设计．北京：北京理工大学出版社，1999：6-9
6 L. A. Zadeh. Fuzzy Sets. Informat Control. 1965, (8):338-353
7 L. A. Zadeh. Fuzzy Algorithm. Informat Control.1968, 12:94-102
8 L. A. Zadeh. A Rationale for Fuzzy Control. Trans. ASME, J. Dynam. Syst. Measur Control, 1972,(94):3-4
9 E. h. Mamdani. Advances in the Linguistic Synthesis of Fuzzy Controllers. Fuzzy Reasoning and its Applications. Academic Press, New York, 1981: 325-334
10 T. J. Procyk, E. H. Mamdani. A Linguistic Self-organizing Process Controller. Automatica, 1979,15(1): 15-30
11 E. M. Scharf, N. J. Mandic. The Application of a Fuzzy Controller to the Control of a Multi-degree Freedom Robot Arm. Industrial Application for Fuzzy Control M Sugeno, North-Holland, 1985:102-117
12 S. Shao. Fuzzy Self-organizing Controller and its Application for Dynamic Processes. Fuzzy Sets and Systems, 1988,(26): 151-164
13 S. Dalay, K. F. Gill. A Design Study of a Self-organizing Fuzzy Logic Controller. Proc Inst Mech Engrs, 1986, 200(c1):59-69
14 Z. Q. Wu et al. A Rule Self-regulating Fuzzy Controller. Fuzzy Sets and Systems. 1992,(47): 13-21
15 Chih-Hsun Chou, Hung-Ching Lu. A Heuristic Self-tuning Fuzzy Controller. Fuzzy Sets and Systems, 1994,(61):249-264
16 F. V. D. Rhee, F. Vander Rhee, Van. Na. Alemke, H. R etal. Know- ledge Based Fuzzy Control of Systems. IEEE Trans. on Auto-matic Control, 1990, 35(2): 148-155
17 Chen, T. C. Tsao. A Description of the Dynamical Behavior of Fuzzy Systems. IEEE Trans. on Systems, Man and Cyber- netics, 1989, 19(4):745-759
18 黎浩荣，李立勤等．对一种高精度模糊控制方案的研究与改进．清华大学学报(自然科学版)，2000，40(2)：114-117
19 A. J. Roy, J. Yen. A Robust Self-Turning scheme for PI and PID Type Fuzzy Controller. IEEE Trans on Fuzzy Systems, 1999,17 (1):862-875
20 曹承志，曲红梅．基于参数自调整模糊控制的变频闭环调速系统．电力电子技术，2001，(1)：29-32
21 杨西侠，王瑞娟等．基于f1-f2推理的模糊PID控制器．山东工业大学学报．1999，29(5)，470-474
22 张金明，李人厚．模糊控制系统的设计和稳定性分析．西安交通大学学报，1998，32(7)：24-26
23 A. Ollero, A. J Garcia-Cerezo. Direct Digital Control Auto-tuning and Supervision using Fuzzy Logic, Fuzzy Sets and Systems, 1989,(30):35-153
24 V. Tseng. Hwang, S. L. Lui. Fuzzy Servo Controller: The Hierarchical Approach, Proc. of the 1st IEEE. Int. Conf. On Fuzzy Systems. 1992,(25):623-631
25 Tzafestas, N. P. Papanikolopoulos. Increm ental Fuzzy Expert PID Control, IEEE Transactions on Industrial Electronics, 1990, 37(5):365-371
26 张琦，冯培恩．模糊参数自整定PID控制技术在推土机自动控 制系统中的应用．控制理论与应用，1997，14(2)：287-291
27 李卓，萧德云．基于Fuzzy推理的自调整PID控制器．控制理论与应用，1997，14(2)：238-243
28 D. Filev, P. Angelov. Fuzzy Optimal Control. Fuzzy Sets and Systems, 1992, (47):151-156
29 D. Jang, S. H. Wang, C. K. Liau. On-line off-line Optimization of Complex Processes Using a Linguistic Self-organized Optimizing Control Scheme. Fuzzy Sets and Systems, 1995,(69):22-23
30 R. N. Sastri, Wiwari, K. S. Sastri. Solution of Optimal Control Problems with Lumped Parameters Having Single or Multiple Goal Objectives in Fuzzy Environment. Fuzzy Sets and Systems, 1992,(48): 173-183
31 Shoureshi, K. Rahmani. Derivation and Application of an expert Fuzzy Optimal Control System. Fuzzy Sets and Systems, 1992, (49):93-101
32 Palm. Sliding Mode Fuzzy Control. Proc. Internet. IEEE Conf. on Fuzzy Systems, San Diego, 1992:519-526
33 P. Filev. Dimitar, R. Y. Ronald. On the Analysis of Fuzzy Logic Controllers. Fuzzy Sets and Systems, 1994,(68):39-66
34 Zong-Mu Yeh. A Performance Approach to Fuzzy Control Designed for Nonlinear Systems. Fuzzy Sets and Systems, 1994, (64):339-352
35 金耀初，蒋静坪．一类非线性系统的模糊变结构控制及应用．控制与决策，1992，7(1)：87-89
36 刘向杰，周孝信．模糊控制在电厂锅炉控制中的应用现状及前景．电网技术，1998，5(12)：784-786
37 H. Ying. Analytical Structure of a Two-input Two-output Fuzzy Controller And its Relation to PIand Multilevel Relay Controllers. Fuzzy Sets and Systems, 1994,(63):21-33
38 W. S. Mc Culloch, W. Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 1943, (5): 115-133
39 D. O. Hebb. The Organization of Behavior. New York: Wiley, 1949,(5): 187-193
40 F. Rosenblatt. The Perceptron: a Probabilistied model for Information Srorage and Organization in TlBrain, Psuchological Riview, 1958.(65):386-408
41 D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, MIT Press, Canbridge, 1986:145-169
42 B. Kosko. Neural Networks And Fuzzy Systems, Pretice-Hall, Englewood Cliffs, 1992:347-362
43 P. J. Werbos. Neural Control and Fuzzy Logic:Connection and Design, Proc.of IJCNN, 1991, (2):431-447
44 B. Kosko, S. G. Kong. Adaptive Fuzzy Systems for Backing up a Track-and-trailer. IEEE Trans Neural Networks, 1992, 3(2):211-233
45 E. Cox. Adaptive Fuzzy System. IEEE Spectrum, 1993, 30(2):27-31
46 T. Yamaguchi. Self-organizing Control using Fuzzy Neural Net- works. Int Jcontrol, 1992, 52(2):415-439
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