液压伺服位置系统的智能控制
|
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的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、外干扰和伺服阀的死区非线性等棘手的问题，而模糊控制不需要精确的数学模型，而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1250频率特性测试仪所测的实际液压伺服位置系统被控对象的高阶模型，然后结合实测数据得到被控对象的简化机理数学模型，并以此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，分别设计了自调整模糊控制器、模型参考模糊自适应控制器、基于模糊逐级误差逼近算法的模糊神经网络控制器和神经网络变结构控制器。其中，神经网络变结构控制器是在利用神经网络对伺服阀摩擦死区非线性进行了辨识，并利用神经网络拟合的死区非线性的逆模型进行了补偿，然后再用变结构控制对液压伺服位置系统进行了控制器的设计，解决了液压伺服系统中伺服阀的死区非线性问题。通过对所设计控制器的仿真研究，结果表明，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、较好的跟踪精度，证明了所设计控制器的可行性。
This paper is a theoretical research that is based on the having been finished project of the state ''the ninth five years" tackling key scientific and technological problems and facing the continuous casting analogy device in the laboratory of our school. It is difficult for the hydraulic servo position system to solve the problem that is caused by the parameters variety, outside disturbance and the friction nonlinear dead zone of the valve. It is well known that it is not necessary to get an accurate model of the system for fuzzy control. We also know that neural network can solve the problems caused by non-linear effectively. So the research that two methods are applied to control the hydraulic servo position system has practical meaning and theory value.
According to analysis the frequency graph tested by 1250 frequency detector, the high order mathematical model can be deduced. Then we get a simplified mechanism model by combining the practical model and use the simplified model to design the controllers. On the basis of the summary of the characteristics and application of fuzzy control and neural network theory, a self-tuning fuzzy control, a model reference fuzzy adaptive controller, a fuzzy neural network based on fuzzy hierarchy error approach and a neural network variable structure controller are proposed. In the fourth controller, a neural network is used to identify the friction nonlinear dead zone of the valve, and then another neural network is used to fit the inverse model of the nonlinear for compensating the nonlinear and a variable structure controller is designed to control the hydraulic servo position system. And the problem of the friction nonlinear dead zone of the valve is solved. The result of simulation shows that hydraulic servo position control system has good performances in the dynamic and static state control accuracy, which shows the feasibility of these control methods.
本文首先利用1250频率特性测试仪所测的实际液压伺服位置系统被控对象的高阶模型，然后结合实测数据得到被控对象的简化机理数学模型，并以此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，分别设计了自调整模糊控制器、模型参考模糊自适应控制器、基于模糊逐级误差逼近算法的模糊神经网络控制器和神经网络变结构控制器。其中，神经网络变结构控制器是在利用神经网络对伺服阀摩擦死区非线性进行了辨识，并利用神经网络拟合的死区非线性的逆模型进行了补偿，然后再用变结构控制对液压伺服位置系统进行了控制器的设计，解决了液压伺服系统中伺服阀的死区非线性问题。通过对所设计控制器的仿真研究，结果表明，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、较好的跟踪精度，证明了所设计控制器的可行性。
This paper is a theoretical research that is based on the having been finished project of the state ''the ninth five years" tackling key scientific and technological problems and facing the continuous casting analogy device in the laboratory of our school. It is difficult for the hydraulic servo position system to solve the problem that is caused by the parameters variety, outside disturbance and the friction nonlinear dead zone of the valve. It is well known that it is not necessary to get an accurate model of the system for fuzzy control. We also know that neural network can solve the problems caused by non-linear effectively. So the research that two methods are applied to control the hydraulic servo position system has practical meaning and theory value.
According to analysis the frequency graph tested by 1250 frequency detector, the high order mathematical model can be deduced. Then we get a simplified mechanism model by combining the practical model and use the simplified model to design the controllers. On the basis of the summary of the characteristics and application of fuzzy control and neural network theory, a self-tuning fuzzy control, a model reference fuzzy adaptive controller, a fuzzy neural network based on fuzzy hierarchy error approach and a neural network variable structure controller are proposed. In the fourth controller, a neural network is used to identify the friction nonlinear dead zone of the valve, and then another neural network is used to fit the inverse model of the nonlinear for compensating the nonlinear and a variable structure controller is designed to control the hydraulic servo position system. And the problem of the friction nonlinear dead zone of the valve is solved. The result of simulation shows that hydraulic servo position control system has good performances in the dynamic and static state control accuracy, which shows the feasibility of these control methods.
引文
1 王占林．近代液压控制．北京：机械工业出版社，1997：1-5
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 Zadeh L A. Fuzzy Sets. Informat Control.1965,(8):338-353
7 Zadeh L A. Fuzzy Algorithm. Informat Control.1968.12:94-102
8 Zadeh L A. A Rationale for Fuzzy Control. Trans. ASME. J. Dynam. Syst Measur Control 1972,94:3-4
9 Mamdani E h. Advances in the Linguistic Synthesis of Fuzzy Controllers. Fuzzy Reasoning and its Applications, New York, Academic Press, 1981:325-334
10 Procyk T J, Mamdani E H. A Linguistic Self-organizing Process Controller. Automatica,1979,15(1): 15-30
11 Scharf E M. Mandic N J. The Application of a Fuzzy Controller to the Control of a Multi-degree Freedom Robot Arm. Industrial Application for Fuzzy Control M Sugeno(ed), North-Holland, 1985:102-117
12 Shao S. Fuzzy Self-organizing Controller and its Application for Dynamic Processes. Fuzzy Sets and Systems.1988.(26): 151-164
13 Dalay S. K F Gill. A Design Study of a Self-organizing Fuzzy Logic Controller. Proc Inst Mech Engrs.1986.200(cl):59-69
14 Z Q Wu etal. 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, Vander F Rhee, Van Na Alemke. H Retal. Knowledge Based Fuzzy Control of Systems. IEEE Trans on Automatic Control, 1990,35(2):148-155
17 T C Chen, A Tsao. Description of the Dynamical Behavior of Fuzzy Systems, IEEE, Trans. on Systems, Man, and Cybernetics, 1989,19(4):745-75
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 H Ying, W Siler, JJA Buckley 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 B Kosko. Neural Networks and Fuzzy Systems, Pretice-Hall, Englewood Cliffs, 1992:347-362
22 Werbos P J.Neural Control and Fuzzy Logic:Connection and Design, Proc.of IJCNN,1991,(2):431-447
23 B Kosko, S G Kong. Adaptive Fuzzy Systems for Backing up a Track-and-Trailer. IEEE Trans Neural Networks, 1992,3(2): 211-233
24 E cox. Adaptive Fuzzy System. IEEE Spectrum,1993,30(2):27-31
25 T Yamaguchi. Self-organizing Control Using Fuzzy Neural Networks, Int Jcontrol, 1992,52(2):415-439
26 王耀南．模糊神经网络模型参考自适应控制及其应用．湖南大学学报，1997(24)：465-73
27 李青茹，吕振肃等．基于神经网络的模糊控制器．兰州大学学报，1999．35(1)：81-86
28 Chin-Teng Lin, Neural-network-based Fuzzy Logic Control and Decision System. IEEE Trans. Computers, 1991,40(12)
29 CL Chen, W C Chen. Self-organizing Neural Control System Design for Dynam ic Processes. Int JSystem Sci, 1993,24(8): 1487-1507
30 K JHunt. Neural Networks for Control Systems-A Survey. Automatica, 1992,28(6):1083-1112
31 H R Bernji, P Khedkar. Learning and Tuning Fuzzy Logic Controllers Through Reinforcements. IEEE Trans on Neural Network, 1992,3(5):724-740
32 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
33 R Jang. Self-learning Controller Based on Temp Back Propagation. IEEE Trans Neural Network. 1992.3(5):714-723
34 Jnubong Nie, D A Linkens. Learning Control using Fuzzy Self-organizing Radial Basis Function Network. IEEE Trans on Fuzzy System. 1993,1(4):281-287
35 M.Dasgupta. Varying Frequency. The36th IEEE Conference on Decision and Control San Diego, California USA. December, 1997:242-274
36 Arabashahi Payrran. Fuzzy Control of Back Propagation. IEEE'92 Fuzzy, 1992:967-972
37 Choi Jai etal. Fuzzy Parameter Adaptation in Neural systems. IJC-NN' 92, Baltimoro,1992:232-238
38 A.Wu and P.K.S.Tam. A Fuzzy Neural Network Based on Fuzzy Hierarchy Error Approach. IEEE Transactions on Fuzzy Systems. 2000.8(6):808-816
39 Keller JM, Yager R Retal. Neural Network Implementation of Fuzzy Logic, Fuzzy Sets and Systems, 1992,(45):1-12
40 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
41 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
42 A Ollero, A JGarcia-Cerezo. Direct Digital Control Auto-tuning and Supervision using Fuzzy Logic, Fuzzy Sets and Systems. 1989,(30):35-153
43 Tseng V Hwang, S L Lui. Fuzzy Servo Controller:The Hierarchical Approach, Proc. of the 1st IEEE. Int. Conf. On Fuzzy Systems. 1992,(25):623-631
44 Tzafestas, N P Papanikolopoulos. Increm ental Fuzzy Expert PID Control, IEEE Transactions on Industrial Electronics.1990, 37(5):365-371
45 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
46 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
47 D Filev, P Angelov. Fuzzy Optimal Control, Fuzzy Sets and Systems.1992.(47):151-156
48 Jang D 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
49 Sastri R N, 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
50 Shoureshi, K Rahmani. Derivation and Application of an expert Fuzzy Optimal Control System, Fuzzy Sets and Systems 1992, (49):93-101
51 Palm. Sliding Mode Fuzzy Control, Proc. Internet. IEEE Conf. on Fuzzy Systems, San Diego.1992:519-526
52 P Dimitar, Filev. R Y Ronald. On the Analysis of Fuzzy Logic Controllers. Fuzzy Sets and Systems. 1994,(68):39-66
53 Zong-Mu Yeh. A Performance Approach to Fuzzy Control Designed for Nonlinear System s.1994,(64):339-352
54 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
55 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
56 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
57 W S Mc Culloch, W Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics,1943,(5):115-133
58 D O Hebb. The Organization of Behavior, New York:Wiley, 1949,(5):187-193
59 F. Rosenblatt. The Perceptron:a Probabilistied model for Information Storage and Organization in TlBrain, Psuchological Riview 1958.(65):386-408
60 D.E.Rumelhart and J.L.McClelland. Parallel Distributed Processing, MIT Press. Canbridge. 1986:145-169
61 甄敏，袁艳，张泰山．三维控制规则自修正模糊算法的研究．计算技术与自动化．2000，19(1)：16-18
62 刘向杰，柴天佑，张焕水．三维模糊控制器的结构研究．自动化学报，1998，24(2)：230-235
63 胡包钢．关于模糊 PID 控制器推理维数的研究．自动化学报，1998，24(5)：608-614
64 吴介一．提高模糊控制器精度的研究．控制理论与应用，1996，13(2)：264-267
65 诸静．模糊控制原理与应用．北京：机械工业出版社，1999：250-251
66 张健民，杨华甬．路甬祥．基于工程整定法的模糊 PID．信息与控制，1998．27(1)：187-190
67 张琦，冯培恩．模糊参数自整定 PID 控制技术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
68 吕建虹等．模糊 PID 控制器及在汽温控制系统中的应用．中国机电工程学报，1995，(1)：16-22
69 李卓，萧德云，何世忠．基于 Fuzzy 推理的自调整 PID 控制器．控制理论与应用，1997，14(2)：238-242
70 章卫国，杨向忠著．模糊控制原理与应用．西安：西北工业大学出版社，1999：67-69
71 Li w, Slotine JJE Neural network control of unknown nonlinear systems. In: Proceedings of the American Control Conference, Pittsburg,PA, 1989(2):1136-1141
72 PM Mills, AY Zomaya. MO Tade. Adaptive Model-Based Controll using neural networks. Int Journal of Control 1994,60(6):1163-92
73 H Cho. E Bai. Convergence Results for an Adaptive Dead-zone Inverse. Int Journal of Adaptive Control and Signal Processing. 1998,12(5):451-466
74 T.Knohl, H.Unbehauen. Adaptive Position Control of Electro Hydraulic Servo Systems Using ANN. Mechatronics.2000,(10): 127-143
75 蔡季冰．系统辨识．北京：北京理工大学出版社，1989：26-27
76 蒙以正．柳成茂．MATLAB 5．X 应用技巧．北京：科学出版社．1999：25-27
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 Zadeh L A. Fuzzy Sets. Informat Control.1965,(8):338-353
7 Zadeh L A. Fuzzy Algorithm. Informat Control.1968.12:94-102
8 Zadeh L A. A Rationale for Fuzzy Control. Trans. ASME. J. Dynam. Syst Measur Control 1972,94:3-4
9 Mamdani E h. Advances in the Linguistic Synthesis of Fuzzy Controllers. Fuzzy Reasoning and its Applications, New York, Academic Press, 1981:325-334
10 Procyk T J, Mamdani E H. A Linguistic Self-organizing Process Controller. Automatica,1979,15(1): 15-30
11 Scharf E M. Mandic N J. The Application of a Fuzzy Controller to the Control of a Multi-degree Freedom Robot Arm. Industrial Application for Fuzzy Control M Sugeno(ed), North-Holland, 1985:102-117
12 Shao S. Fuzzy Self-organizing Controller and its Application for Dynamic Processes. Fuzzy Sets and Systems.1988.(26): 151-164
13 Dalay S. K F Gill. A Design Study of a Self-organizing Fuzzy Logic Controller. Proc Inst Mech Engrs.1986.200(cl):59-69
14 Z Q Wu etal. 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, Vander F Rhee, Van Na Alemke. H Retal. Knowledge Based Fuzzy Control of Systems. IEEE Trans on Automatic Control, 1990,35(2):148-155
17 T C Chen, A Tsao. Description of the Dynamical Behavior of Fuzzy Systems, IEEE, Trans. on Systems, Man, and Cybernetics, 1989,19(4):745-75
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 H Ying, W Siler, JJA Buckley 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 B Kosko. Neural Networks and Fuzzy Systems, Pretice-Hall, Englewood Cliffs, 1992:347-362
22 Werbos P J.Neural Control and Fuzzy Logic:Connection and Design, Proc.of IJCNN,1991,(2):431-447
23 B Kosko, S G Kong. Adaptive Fuzzy Systems for Backing up a Track-and-Trailer. IEEE Trans Neural Networks, 1992,3(2): 211-233
24 E cox. Adaptive Fuzzy System. IEEE Spectrum,1993,30(2):27-31
25 T Yamaguchi. Self-organizing Control Using Fuzzy Neural Networks, Int Jcontrol, 1992,52(2):415-439
26 王耀南．模糊神经网络模型参考自适应控制及其应用．湖南大学学报，1997(24)：465-73
27 李青茹，吕振肃等．基于神经网络的模糊控制器．兰州大学学报，1999．35(1)：81-86
28 Chin-Teng Lin, Neural-network-based Fuzzy Logic Control and Decision System. IEEE Trans. Computers, 1991,40(12)
29 CL Chen, W C Chen. Self-organizing Neural Control System Design for Dynam ic Processes. Int JSystem Sci, 1993,24(8): 1487-1507
30 K JHunt. Neural Networks for Control Systems-A Survey. Automatica, 1992,28(6):1083-1112
31 H R Bernji, P Khedkar. Learning and Tuning Fuzzy Logic Controllers Through Reinforcements. IEEE Trans on Neural Network, 1992,3(5):724-740
32 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
33 R Jang. Self-learning Controller Based on Temp Back Propagation. IEEE Trans Neural Network. 1992.3(5):714-723
34 Jnubong Nie, D A Linkens. Learning Control using Fuzzy Self-organizing Radial Basis Function Network. IEEE Trans on Fuzzy System. 1993,1(4):281-287
35 M.Dasgupta. Varying Frequency. The36th IEEE Conference on Decision and Control San Diego, California USA. December, 1997:242-274
36 Arabashahi Payrran. Fuzzy Control of Back Propagation. IEEE'92 Fuzzy, 1992:967-972
37 Choi Jai etal. Fuzzy Parameter Adaptation in Neural systems. IJC-NN' 92, Baltimoro,1992:232-238
38 A.Wu and P.K.S.Tam. A Fuzzy Neural Network Based on Fuzzy Hierarchy Error Approach. IEEE Transactions on Fuzzy Systems. 2000.8(6):808-816
39 Keller JM, Yager R Retal. Neural Network Implementation of Fuzzy Logic, Fuzzy Sets and Systems, 1992,(45):1-12
40 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
41 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
42 A Ollero, A JGarcia-Cerezo. Direct Digital Control Auto-tuning and Supervision using Fuzzy Logic, Fuzzy Sets and Systems. 1989,(30):35-153
43 Tseng V Hwang, S L Lui. Fuzzy Servo Controller:The Hierarchical Approach, Proc. of the 1st IEEE. Int. Conf. On Fuzzy Systems. 1992,(25):623-631
44 Tzafestas, N P Papanikolopoulos. Increm ental Fuzzy Expert PID Control, IEEE Transactions on Industrial Electronics.1990, 37(5):365-371
45 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
46 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
47 D Filev, P Angelov. Fuzzy Optimal Control, Fuzzy Sets and Systems.1992.(47):151-156
48 Jang D 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
49 Sastri R N, 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
50 Shoureshi, K Rahmani. Derivation and Application of an expert Fuzzy Optimal Control System, Fuzzy Sets and Systems 1992, (49):93-101
51 Palm. Sliding Mode Fuzzy Control, Proc. Internet. IEEE Conf. on Fuzzy Systems, San Diego.1992:519-526
52 P Dimitar, Filev. R Y Ronald. On the Analysis of Fuzzy Logic Controllers. Fuzzy Sets and Systems. 1994,(68):39-66
53 Zong-Mu Yeh. A Performance Approach to Fuzzy Control Designed for Nonlinear System s.1994,(64):339-352
54 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
55 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
56 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
57 W S Mc Culloch, W Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics,1943,(5):115-133
58 D O Hebb. The Organization of Behavior, New York:Wiley, 1949,(5):187-193
59 F. Rosenblatt. The Perceptron:a Probabilistied model for Information Storage and Organization in TlBrain, Psuchological Riview 1958.(65):386-408
60 D.E.Rumelhart and J.L.McClelland. Parallel Distributed Processing, MIT Press. Canbridge. 1986:145-169
61 甄敏，袁艳，张泰山．三维控制规则自修正模糊算法的研究．计算技术与自动化．2000，19(1)：16-18
62 刘向杰，柴天佑，张焕水．三维模糊控制器的结构研究．自动化学报，1998，24(2)：230-235
63 胡包钢．关于模糊 PID 控制器推理维数的研究．自动化学报，1998，24(5)：608-614
64 吴介一．提高模糊控制器精度的研究．控制理论与应用，1996，13(2)：264-267
65 诸静．模糊控制原理与应用．北京：机械工业出版社，1999：250-251
66 张健民，杨华甬．路甬祥．基于工程整定法的模糊 PID．信息与控制，1998．27(1)：187-190
67 张琦，冯培恩．模糊参数自整定 PID 控制技术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
68 吕建虹等．模糊 PID 控制器及在汽温控制系统中的应用．中国机电工程学报，1995，(1)：16-22
69 李卓，萧德云，何世忠．基于 Fuzzy 推理的自调整 PID 控制器．控制理论与应用，1997，14(2)：238-242
70 章卫国，杨向忠著．模糊控制原理与应用．西安：西北工业大学出版社，1999：67-69
71 Li w, Slotine JJE Neural network control of unknown nonlinear systems. In: Proceedings of the American Control Conference, Pittsburg,PA, 1989(2):1136-1141
72 PM Mills, AY Zomaya. MO Tade. Adaptive Model-Based Controll using neural networks. Int Journal of Control 1994,60(6):1163-92
73 H Cho. E Bai. Convergence Results for an Adaptive Dead-zone Inverse. Int Journal of Adaptive Control and Signal Processing. 1998,12(5):451-466
74 T.Knohl, H.Unbehauen. Adaptive Position Control of Electro Hydraulic Servo Systems Using ANN. Mechatronics.2000,(10): 127-143
75 蔡季冰．系统辨识．北京：北京理工大学出版社，1989：26-27
76 蒙以正．柳成茂．MATLAB 5．X 应用技巧．北京：科学出版社．1999：25-27