Intelligent control systems and fuzzy controllers. II: Trained fuzzy controllers, fuzzy PID controllers. (English. Russian original) Zbl 1455.93109
Autom. Remote Control 81, No. 5, 922-934 (2020); translation from Datchiki Sist. 2017, No. 2, 3-12 (2017).
Summary: The problems of control systems intellectualization are observed. The necessity of intellectualization of a wide range of systems and control methods is proved. The hierarchy of levels of intellectual control observed and comparison analysis of different artificial intelligence devices given. Importance of target setting’s automation problems’ solving in control systems is pointed out, as well as intellectualization of anthropocentric systems, including the ones based on fuzzy logic and case-based reasoning. The logical-linguistic, analytical, learned and PID fuzzy controllers are considered, based on fuzzy logics of Zadeh. An overview of the Mamdani-type controllers, controllers based on TS-model and the ANFIS architecture, using neural network structure is provided. The conditions of optimality and stability of control systems with Mamdani fuzzy controllers are analyzed. The Sugeno dynamic models and the ANFIS adaptive models and the methods of learning developed on the basis of fuzzy controllers are considered. The structure of a Mamdani fuzzy controller and its implementation by means of the Simulink is described. An example of application of Simulink to determine the optimal parameters of a fuzzy controller is shown. The examples of the fuzzy controllers use are given.
For Part I, see [the authors, Autom. Remote Control 81, No. 1, 171-191 (2020; Zbl 1455.93108); translation from Datchiki Sist. 2017, No. 5, 4-19 (2017)].
For Part I, see [the authors, Autom. Remote Control 81, No. 1, 171-191 (2020; Zbl 1455.93108); translation from Datchiki Sist. 2017, No. 5, 4-19 (2017)].
MSC:
| 93C42 | Fuzzy control/observation systems |
Keywords:
intelligent control; logical-linguistic controllers; stability conditions; the sugeno and mamdani controllers; simulink; TS-model; the ANFIS structure; learning fuzzy controllers; genetics controllers; membership function; fuzzification; defuzzificationCitations:
Zbl 1455.93108Software:
SimulinkReferences:
| [1] | Vassilyev, SN; Kudinov, YuI; Pashchenko, FF, Intelligent Control Systems and Fuzzy Controllers. I. Fuzzy Models, Logical-Linguistic and Analytical Regulators, Autom. Remote Control, 81, 1, 171-191 (2020) · Zbl 1455.93108 · doi:10.1134/S0005117920010142 |
| [2] | Pupkov, KA, Some Results of Development of Scientific-Technical Program “Intellectual Systems” of FKP “Universities of Russia, Proc. 2nd Int. Symp. “Intellectual Systems” (INTELS’96), 1, 5-9 (1996) |
| [3] | Russell, SJ; Norvig, P., Artificial Intelligence: A Modern Approach (1995), Upper Saddle River: Prentice Hall, Upper Saddle River · Zbl 0835.68093 |
| [4] | Takagi, H.; Hayashi, I., NN-Driven Fuzzy Reasoning, Int. J. Appr. Reasoning, 191-212 (1991) · Zbl 0733.68074 |
| [5] | Yager, RR, Implementing Fuzzy logic Controllers using a Neural Network framework, Fuzzy Sets Syst., 53-64 (1992) |
| [6] | Bukcley, JJ; Hayashi, I., Neural Nets for Fuzzy Systems, Fuzzy Sets Syst., 265-276 (1995) |
| [7] | Lin, CT; Lee, CSG, Neural-network-based Fuzzy Logic Control and Decision System, IEEE Trans. Comput., 40, 12, 1320-1336 (1991) · Zbl 1395.93324 · doi:10.1109/12.106218 |
| [8] | Jang, J-SR, ANFIS Adaptive-network-based Fuzzy System, IEEE Trans. Systems Man Cybernet., 23, 6, 665-685 (1993) · doi:10.1109/21.256541 |
| [9] | Bukcley, JJ; Hayashi, I.; Czogala, E., Fuzzy Neural Controller, Proc. IEEE Int. Conf. on Fuzzy Systems, 197-202 (1992) |
| [10] | Tang, JR; Sun, C., Neuro-Fuzzy Modeling and Control, Proc. IEEE, 83, 3, 378-406 (1995) · doi:10.1109/5.364486 |
| [11] | Shi, Y.; Mizumoto, M.; Yubazaki, N.; Otani, M., A Method of Fuzzy Rules Generation based on Neuro-fuzzy Learning Algorithm, J. Japan Soc. Fuzzy Theory Syst., 8, 4, 695-705 (1996) · doi:10.3156/jfuzzy.8.4_103 |
| [12] | Juang, CF; Lin, C-T, An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications, IEEE Trans. Fuzzy Syst., 5, 1, 12-32 (1998) · doi:10.1109/91.660805 |
| [13] | Ishigami, H.; Fukuda, T.; Shibata, T.; Arai, F., Structure Optimization of Fuzzy Neural Network by Genetic Algorithm, Fuzzy Sets Syst., 257-264 (1995) |
| [14] | Francisco, H.; Luis, M., Genetic Fuzzy Systems, Tatra Mount. Math. Publ., 93-121 (1997) · Zbl 0914.94034 |
| [15] | Chin, TC, Genetic Algorithms for Learning the Rule Base of Fuzzy logic Controller, Fuzzy Sets Syst., 1-7 (1998) |
| [16] | Juang, C-F; Lin, J-Y, Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design, IEEE Trans. Syst., Man Cybern., Part B, Cybern., 30, 2, 290-302 (2000) · doi:10.1109/3477.836377 |
| [17] | Rajapakse, A.; Furuta, K.; Kondo, A., Evolutionary Learning of Fuzzy Logic Controllers and Their Adaptation through Perpetual Evolution, IEEE Trans. Fuzzy Syst., 10, 3, 309-321 (2002) · doi:10.1109/TFUZZ.2002.1006434 |
| [18] | Chen, C.; Wong, C-C, Self-Generating Rule-Mapping Fuzzy Controller Design using a Genetic Algorithm, IEE Proc. Control Theory Appl., 149, 2, 143-148 (2002) · doi:10.1049/ip-cta:20020253 |
| [19] | Karr, CL, Genetic Algorithms for Fuzzy Controllers, AI Expert, 6, 1, 26-33 (1991) |
| [20] | IEEE Trans. Fuzzy Syst.199311 |
| [21] | Linkens, DA; Nyongesa, HO, Genetic Algorithms for Fuzzy Control, IEE Proc. Control Theory Appl., 142, 3, 161-176 (1995) · Zbl 0834.93027 · doi:10.1049/ip-cta:19951766 |
| [22] | Kudinov, YI; Chalov, EA; Kudinov, IU, The Development of a Fuzzy Learning Control System, Promyshl. ASU Kontroller., 25-29 (2004) |
| [23] | Patel, AV; Mohan, BM, Analytical Structures and Analysis of the Simplest Fuzzy PI Controllers, Automatica, 38, 981-993 (2002) · Zbl 1012.93034 · doi:10.1016/S0005-1098(01)00297-7 |
| [24] | Ying, H., Fuzzy Control and Modeling (2000), New York: IEEE, New York |
| [25] | Passino, KM; Yurkovich, S., Fuzzy Control (1998), New York: Addison Wesley, New York |
| [26] | Mann, GKI; Hu, B-G; Gosine, RG, Analysis of Direct Action Fuzzy PID Controller Structures, IEEE Trans. Syst., Man, Cybern., Part B, 29, 3, 371-388 (1999) · doi:10.1109/3477.764871 |
| [27] | Johnson, MA; Moradi, MH, Fuzzy PID Control: New Identification and Design Methods (2005), London: Springer-Verlag, London |
| [28] | Jantzen, J., Foundations of Fuzzy Control (2007), New York: Wiley, New York |
| [29] | Ding, Y.; Ying, H.; Shao, S., Typical Takagi-Sugeno PI and PD Fuzzy Controllers: Analytical Atructures and Stability Analysis, Inform. Sci., 151, 245-262 (2003) · Zbl 1032.93043 · doi:10.1016/S0020-0255(02)00302-X |
| [30] | Kumar, V.; Mittal, AP, Parallel Fuzzy P + Fuzzy I + Fuzzy D Controller: Design and Performance Evaluation, Int. J. Automat. Comput., 7, 4, 463-471 (2010) · doi:10.1007/s11633-010-0528-2 |
| [31] | Fadaei, F.; Shahbazian, M.; Aghajani, M.; Jazayeri-Rad, H., A Novel Hybrid Fuzzy PID Controller Based on Cooperative Coevolutionary Genetic Algorithm, J. Basic. Appl. Sci. Res., 3, 3, 337-344 (2013) |
| [32] | Li, H-X; Tso, SK, Quantitative Design Analysis of Fuzzy Proportional-Integral-Derivative Control—A Step Towards Autotuning, Int. J. Sci., 31, 5, 545-553 (2000) · Zbl 1080.93571 |
| [33] | Kovacic, Z.; Bogdan, S., Fuzzy Controller Design. Theory and Applications (2006), Arlington: Taylor and Francis, Arlington · Zbl 1123.93001 |
| [34] | Michels, K.; Klawonn, F.; Kruse, R.; Nürnberger, A., Fuzzy Control. Fundamentals, Stability and Design of Fuzzy Controllers (2006), Berlin: Springer-Verlag, Berlin · Zbl 1099.93025 |
| [35] | Vaishnav, SR; Khan, ZJ, Performance of Tuned PID Controller and a New Hybrid Fuzzy PD + I Controller, World J. Modeling Simulat., 6, 2, 141-149 (2010) |
| [36] | Kudinov, YI; Kelina, AY, Simplified Method of Determining the Parameters of Fuzzy PID Controllers, Mekhatron., Avtomatiz., Upravlen., 12-22 (2013) |
| [37] | Rotach, VYa, Expert Evaluation of Control Algorithms by Methods of Fuzzy Logic and Probability Theory, Teploenergetika, 51-56 (2002) |
| [38] | Pivonka, P.; Drejl, M., Use of Fuzzy PID Controllers in Fuzzy Control of Coal Power Plants, Proc. Fuzzy-96 (1996), Germany: Zittau, Germany |
| [39] | Guilamo, PG, Fuzzy Control, Measurem. Control (1987) |
| [40] | Rotach, VYa, About Fazzi-PID-Regulators, Teploenergetika, 32-36 (1999) |
| [41] | Rotach, VYa, Is It Possible to Synthesize a Fuzzy Controller using the Fuzzy Sets Theory, Promyshl. ASU Kontroller., 33-34 (2004) |
| [42] | Prangishvili, IV; Pashchenko, FF; Busygin, BP, System Laws and Regularities in Electrodynamics, Nature and Society (2001), Moscow: Nauka, Moscow |
| [43] | Zhang, J.; Zhang, P.; Li, R., Analysis and Design of Fuzzy Controller based on Fuzzy Reaching Law, Proc. the 14th World Congress IFAC, 189-194 (1999) |
| [44] | Kudinov, YI; Kudinov, IY; Pashchenko, FF; Pashchenko, AF, Program Complex for Identification of Fuzzy Models, Proc. the 25th Chinese Control and Decision Conference (CCDC-2013), 2492-2494 (2013), USA: Guigang, China, Piscataway, IEEE, USA |
| [45] | Pashchenko, FF; Kudinov, IY; Kudinov, YI, Identification of Multivariable Fuzzy Systems, Proc. the 2011 Chinese Control and Decision Conference (CCDC-2011), 1572-1575 (2011), China: Mianyang, China |
| [46] | Hu, B.; Mann, G.; Gosine, R., How to Evaluate Fuzzy PID Controllers without Using Process Information, Proc. the 14th World Congress IFAC, 177-182 (1999) |
| [47] | Zhang, H.; Chan, C.; Chenng, K.; Ye, Y., Fuzzy Art Map Neural Network and Its Application to Fault Diagnosis of Navigation Systems, Automatica, 37, 1065-1070 (2001) · Zbl 0989.93042 · doi:10.1016/S0005-1098(01)00058-9 |
| [48] | Choi, HH, Robust Stabilization of Uncertain Fuzzy Systems using Variable Structure System Approach, IEEE Trans. Fuzzy Syst., 16, 3, 715-724 (2008) · doi:10.1109/TFUZZ.2007.905913 |
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