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Mathematical analysis of a simplified general type-2 fuzzy PID controller

  • Received: 09 September 2020 Accepted: 28 October 2020 Published: 12 November 2020
  • For the type reduction of general type-2 fuzzy PID controller is time consuming and the mathematical expression of general type-2 fuzzy PID controller is difficult to derived. So, a simplified general type-2 fuzzy PID (SGT2-FPID) controller is studied in this article. The SGT2-FPID controller adopts triangular function as the primary and secondary membership function. Then the primary membership degree of apex for the secondary membership degree will be applied to get the output of SGT2-FPID controller, which can reduce the computation complexity of general type-2 fuzzy controller type reduction. Furthermore, the mathematical expressions of SGT2-FPID controller, type-1 fuzzy PID controller and interval type-2 fuzzy PID controller are discussed. Finally, 4 plants are applied to demonstrate the effectiveness and robustness of SGT2-FPID controller. The simulation results show that when the plants have uncertainty in model structure, measurement and external disturbance, the SGT2-FPID controller can achieve better control performances in contrast to compared controllers.

    Citation: Jianzhong Shi, Ying Song. Mathematical analysis of a simplified general type-2 fuzzy PID controller[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7994-8036. doi: 10.3934/mbe.2020406

    Related Papers:

  • For the type reduction of general type-2 fuzzy PID controller is time consuming and the mathematical expression of general type-2 fuzzy PID controller is difficult to derived. So, a simplified general type-2 fuzzy PID (SGT2-FPID) controller is studied in this article. The SGT2-FPID controller adopts triangular function as the primary and secondary membership function. Then the primary membership degree of apex for the secondary membership degree will be applied to get the output of SGT2-FPID controller, which can reduce the computation complexity of general type-2 fuzzy controller type reduction. Furthermore, the mathematical expressions of SGT2-FPID controller, type-1 fuzzy PID controller and interval type-2 fuzzy PID controller are discussed. Finally, 4 plants are applied to demonstrate the effectiveness and robustness of SGT2-FPID controller. The simulation results show that when the plants have uncertainty in model structure, measurement and external disturbance, the SGT2-FPID controller can achieve better control performances in contrast to compared controllers.


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    [1] A. Kumar, V. Kumar, A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning, ISA Trans., 68 (2017), 251-275. doi: 10.1016/j.isatra.2017.03.022
    [2] D. R. Wu, W. W. Tan, A simplified type-2 fuzzy logic controller for real-time control, ISA Trans., 45 (2006), 503-516. doi: 10.1016/S0019-0578(07)60228-6
    [3] J. Huang, M. H. Ri, D. R. Wu, S. Ri, Interval type-2 fuzzy logic modeling and control of a mobile two-wheeled inverted pendulum, IEEE Trans. Fuzzy Syst., 26 (2018), 2030-2038. doi: 10.1109/TFUZZ.2017.2760283
    [4] T. Kumbasar, I. Eksin, M. Guzelkaya, E. Yesil, Interval type-2 fuzzy inverse controller design in nonlinear IMC structure, Eng. Appl. Artif. Intell., 24 (2011), 996-1005. doi: 10.1016/j.engappai.2011.04.016
    [5] T. Kumbasar, I. Eksin, M. Guzelkaya, E. Yesil, An inverse controller design method for interval type-2 fuzzy models, Soft Comput., 21 (2017), 2665-2686. doi: 10.1007/s00500-015-1966-0
    [6] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-I, Inf. Sci., 8 (1975), 199-249. doi: 10.1016/0020-0255(75)90036-5
    [7] N. N. Karnik, J. M. Mendel, Centroid of a type-2 fuzzy set, Inf. Sci., 132 (2001), 195-220. doi: 10.1016/S0020-0255(01)00069-X
    [8] P. Melin, O. Mendoza, O. Castillo, Face recognition with an improved interval type-2 fuzzy logic sugeno integral and modular neural networks, IEEE Trans. Syst., Man, Cybern. A, Syst. Humans., 41 (2011), 1001-1012. doi: 10.1109/TSMCA.2010.2104318
    [9] O. Castillo, J. R. Castro, P. Melin, A. Rodriguez-Diaz, Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction, Soft Comput., 18 (2014), 1213-1224. doi: 10.1007/s00500-013-1139-y
    [10] V. Uslan, H. Seker, R. John, Overlapping clusters and support vector machines based interval type-2 fuzzy system for the prediction of peptide binding affinity, IEEE Access, 7 (2019), 49756-49764. doi: 10.1109/ACCESS.2019.2910078
    [11] I. Eyoh, R. John, G. D. Maere, Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems, IEEE Trans. Fuzzy Syst., 26 (2018), 2672-2685. doi: 10.1109/TFUZZ.2018.2803751
    [12] P. Melin, O. Castillo, A review on the applications of type-2 fuzzy logic in classification and pattern recognition, Expert Syst. Appl., 40 (2013), 5413-5423. doi: 10.1016/j.eswa.2013.03.020
    [13] P. Melin, O. Castillo, A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition, Appl. Soft Comput., 21 (2014), 568-577. doi: 10.1016/j.asoc.2014.04.017
    [14] O. Castillo, P. Melin, A review on interval type-2 fuzzy logic applications in intelligent control, Inf. Sci., 279 (2014), 615-631. doi: 10.1016/j.ins.2014.04.015
    [15] D. Türkay, A. Baykasoglu, K. Altun K, A. Durmusoglu, I. B. Türksen, Industrial applications of type-2 fuzzy sets and systems: a concise review, Comput. Ind., 62 (2011), 125-137.
    [16] S. Hassa, M. A. Khanesar, E. Kayacan, J. Jaafar, A. Khosravi, Optimal design of adaptive type-2 neuro-fuzzy systems: a review, Appl. Soft Comput., 44 (2016), 134-143. doi: 10.1016/j.asoc.2016.03.023
    [17] T. Kumbasar, A simple design method for interval type-2 fuzzy pid controllers, Soft Comput., 18 (2014), 1293-1304. doi: 10.1007/s00500-013-1144-1
    [18] T. Kumbasar, H. Hagra, Big bang-big crunch optimization based interval type-2 fuzzy PID cascade controller design strategy, Inf. Sci., 282 (2014), 277-295. doi: 10.1016/j.ins.2014.06.005
    [19] J. M. Mendel, R. Chimatapu, H. Hagras, Comparing the performance potentials of singleton and non-singleton type-1 and interval type-2 fuzzy systems in terms of sculpting the state space. IEEE Trans. Fuzzy Syst., 28 (2020), 783-794.
    [20] G. Acampora, D. Alghazzawi, H. Hagras, An interval type-2 fuzzy logic based framework for reputation management in peer-to-peer e-commerce, Inf. Sci., 333 (2016), 88-107. doi: 10.1016/j.ins.2015.11.015
    [21] M. Antonelli, D. Bernardo, H. Hagras, Multiobjective evolutionary optimization of type-2 fuzzy rule-based systems for financial data classification, IEEE Trans. Fuzzy Syst., 25 (2017), 249-264. doi: 10.1109/TFUZZ.2016.2578341
    [22] E. Ramirez, P. Melin, G. Prado-Arechiga, Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification, Expert Syst. Appl., 126 (2019), 295-307. doi: 10.1016/j.eswa.2019.02.035
    [23] I. Eyoh, R. John, G. D. Maere, Interval type-2 a-intuitionistic fuzzy logic for regression problems, IEEE Trans. Fuzzy Syst., 26 (2018), 2396-2408. doi: 10.1109/TFUZZ.2017.2775599
    [24] E. Ontiveros-Robles, P. Melin, A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems, Eng. Appl. Artif. Intell., 85 (2019), 43-55.
    [25] O. Castillo, P. Melin, E. Ontiveros, C. Peraza, P. Ochoa, F. Valdez, et al., A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics, Eng. Appl. Artif. Intell., 85 (2019), 666-680. doi: 10.1016/j.engappai.2019.07.020
    [26] Olivas, F. Valdez, P. Melin, A. Sombra, O. Castillo, Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm, Inf. Sci., 476 (2019), 159-175. doi: 10.1016/j.ins.2018.10.025
    [27] A. C. Tolga, I. B. Parlak, O. Castillo, Finite-interval-valued type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem, Eng. Appl. Artif. Intell., 87 (2020), 103352. doi: 10.1016/j.engappai.2019.103352
    [28] A. Sarabakha, C. H. Fu, E. Kayacan, T. Kumbasar, Type-2 fuzzy logic controllers made even simpler: from design to deployment for UAVs, IEEE Trans. Ind. Electron., 65 (2018), 5069-5077. doi: 10.1109/TIE.2017.2767546
    [29] A. Beke, T. Kumbasar, learning with type-2 fuzzy activation functions to improve the performance of deep neural networks, Eng. Appl. Artif. Intell., 85 (2019), 372-384. doi: 10.1016/j.engappai.2019.06.016
    [30] A. Beke, T. Kumbasar, Type-2 fuzzy logic based linguistic pursuing strategy design and its deployment to a real-world pursuit evasion game, IEEE T. Cybern., 50 (2020), 211-221. doi: 10.1109/TCYB.2018.2868405
    [31] H. B. Zhou, H. Ying, A method for deriving the analytical structure of a broad class of typical interval type-2 Mamdani fuzzy controllers. IEEE Trans. Fuzzy Syst., 21 (2013), 447-458.
    [32] X. Y. Du, H. Ying, Derivation and analysis of the analytical structures of the interval type-2 fuzzy-PI and PD controllers. IEEE Trans. Fuzzy Syst., 18 (2010), 802-814.
    [33] H. B. Zhou, H. Ying, C. L. Zhang, Effects of increasing the footprints of uncertainty on analytical structure of the classes of interval type-2 mamdani and TS fuzzy controllers. IEEE Trans. Fuzzy Syst., 27 (2019), 1881-1890.
    [34] F. L. Liu, An efficient centroid type-reduction strategy for general type-2 fuzzy logic system, Inf. Sci., 178 (2008), 2224-2236. doi: 10.1016/j.ins.2007.11.014
    [35] J. M. Mendel, F. L. Liu, D. Y. Zhai, α-Plane representation for type-2 fuzzy sets: theory and applications, IEEE Trans. Fuzzy Syst., 17 (2009), 1189-1207. doi: 10.1109/TFUZZ.2009.2024411
    [36] J. M. Mendel, Comments on "α-Plane representation for type-2 fuzzy sets: theory and applications", IEEE Trans. Fuzzy Syst., 18 (2010), 229-230. doi: 10.1109/TFUZZ.2009.2039368
    [37] C. Wagner, H. Hagras, zSlices-towards bridging the gap between interval and general type-2 fuzzy logic, IEEE Int. Conf. Fuzzy Syst., (2008), 489-457.
    [38] C. Wagner, H. Hagras, Towards general type-2 fuzzy logic systems based on zSlices, IEEE Trans. Fuzzy Syst., 18 (2010), 637-660. doi: 10.1109/TFUZZ.2010.2045386
    [39] S. Greenfield, F. Chiclana, R. John, S. Coupland, The sampling method of defuzzification for type-2 fuzzy sets: Experimental evaluation, Inf. Sci., 189 (2012), 77-92. doi: 10.1016/j.ins.2011.11.042
    [40] S. Coupland, R. John, Geometric type-1 and type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 15 (2007), 3-15. doi: 10.1109/TFUZZ.2006.889764
    [41] S. Coupland, R. John, A fast geometric method for defuzzification of type-2 fuzzy sets, IEEE Trans. Fuzzy Syst., 16 (2008), 929-941. doi: 10.1109/TFUZZ.2008.924345
    [42] A. D. Torshizi, M. H. F. Zarandi, Hierarchical collapsing method for direct defuzzification of general type-2 fuzzy sets, Inf. Sci., 277 (2014), 842-861. doi: 10.1016/j.ins.2014.03.018
    [43] D. R. Wu, J. M. Mendel, Enhanced Karnik-Mendel algorithms, IEEE Trans. Fuzzy Syst., 17 (2009), 923-934. doi: 10.1109/TFUZZ.2008.924329
    [44] K. Duran, H. Bernal, M. Melgarejo, Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set, IEEE Fuzzy Infor. Process. Soc., (2008), 1-5.
    [45] D. R. Wu, M. Nie, Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems, IEEE Int. Conf. Fuzzy Syst., (2011), 2131-2138.
    [46] T. Kumbasar, H. Hagras, A self-tuning zSlices-based general type-2 fuzzy PI controller, IEEE Trans. Fuzzy Syst., 23 (2015), 991-1013. doi: 10.1109/TFUZZ.2014.2336267
    [47] M. A.S anchez, O. Castillo, J. R. Castro, Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems, Expert Syst. Appl., 42 (2015), 5904-5914. doi: 10.1016/j.eswa.2015.03.024
    [48] L. Amador-Angulo, O. Castillo, J. R. Castro, A generalized type-2 fuzzy logic system for the dynamic adaptation the parameters in a bee colony optimization algorithm applied in an autonomous mobile robot control, IEEE Int. Conf. Fuzzy Syst., (2016), 537-544.
    [49] F. Baghbani, M.-R. A T, A. Alireza, Indirect adaptive robust mixed H2/H general type-2 fuzzy control of uncertain nonlinear systems, Appl. Soft Comput., 72 (2018), 392-418. doi: 10.1016/j.asoc.2018.06.049
    [50] T. Zhao, Q. Yu, S. Y. Dian, R. Guo, S. C. Li, Non-singleton general type-2 fuzzy control for a two-wheeled self-balancing robot, Int. J. Fuzzy Syst., 21 (2019), 1724-1737. doi: 10.1007/s40815-019-00664-4
    [51] S. Y. Dian, J. Han, R. Guo, S. C. Li, T. Zhao, Y. Hu, et al., Double closed-loop general type-2 fuzzy sliding model control for trajectory tracking of wheeled mobile robots, Int. J. Fuzzy Syst., 21 (2019), 2032-2042. doi: 10.1007/s40815-019-00685-z
    [52] O. Castillo, L. Amador-Angulo, J. R. Castro, M. Garcia-Valdez, A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems, Inf. Sci., 354 (2016), 257-274. doi: 10.1016/j.ins.2016.03.026
    [53] M. H. Khooban, N. V afamand, A. Liaghat, T. Dragicevic, An optimal general type-2 fuzzy controller for urban traffic network, ISA Trans., 66 (2016), 335-343.
    [54] E. Ontiveros, P. Melin, O. Castillo, High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems, Eng. Appl. Artif. Intell., 74 (2018), 186-197. doi: 10.1016/j.engappai.2018.06.013
    [55] A. Mohammadzadeh, O. Kaynak, A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology, J. Franklin Inst., 36 (2019), 5151-5171.
    [56] M. H. Khooban, T. Niknam, M. Sha-Sadeghi, A time-varying general type-II fuzzy sliding mode controller for a class of nonlinear power systems, J. Intell. Fuzzy Syst., 30 (2016), 2927-2937. doi: 10.3233/IFS-151796
    [57] E. Ontiveros-Robles, P. Melin, O. Castillo, Comparative analysis of noise robustness of type 2 fuzzy logic controllers, Kybernetika., 54 (2008), 175-201.
    [58] O. Castillo, L. Cervantes, J. Soria, M. Sanchez, J. R. Castro, A generalized type-2 fuzzy granular approach with applications to aerospace, Inf. Sci., 354 (2016), 165-177. doi: 10.1016/j.ins.2016.03.001
    [59] L. Cervantes, O. Castillo, Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control, Inf. Sci., 324 (2015), 247-256. doi: 10.1016/j.ins.2015.06.047
    [60] J. Z. Shi, S. H. Liang, Y. Yang, R. Li, An improved general type 2 fuzzy sets type reduction and its application in general type 2 fuzzy controller design, Soft Comput., 23 (2019), 13513-13530. doi: 10.1007/s00500-019-03889-5
    [61] T. Zhao, Y. Chen, S. Y. Dian, R. Guo, S. C. Li, General type-2 fuzzy gain scheduling PID controller with application to power-line inspection robots, Int. J. Fuzzy Syst., 22 (2020), 181-200. doi: 10.1007/s40815-019-00780-1
    [62] T. Zhao, J. Liu, S. Y. Dian, R. Guo, S. C. Li, Sliding-mode-control-theory-based adaptive general type-2 fuzzy neural network control for power-line inspection robots, Neurocomputing, 401 (2020), 281-294. doi: 10.1016/j.neucom.2020.03.050
    [63] A. Mohammadzadeh, O. Kaynak, A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology, J. Franklin Inst., 356 (2019), 5151-5171. doi: 10.1016/j.jfranklin.2019.05.006
    [64] J. Z. Shi, A fractional order general type-2 fuzzy PID controller design algorithm, IEEE Access., 8 (2020), 52151-52172. doi: 10.1109/ACCESS.2020.2980686
    [65] E. Ontiveros, P. Melin, O. Castillo, Comparative study of interval type-2 and general type-2 fuzzy systems in medical diagnosis, Inf. Sci., 525 (2020), 37-53. doi: 10.1016/j.ins.2020.03.059
    [66] A. Mohammadzadeh, T. Kumbasar, A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation, Appl. Soft Comput., 91 (2020), 106241. doi: 10.1016/j.asoc.2020.106241
    [67] M. H. Fazel Zarandi, S. Soltanzadeh, A. Mohammadi, O. Castillo, Designing a general type-2 fuzzy expert system for diagnosis of depression, Appl. Soft Comput., 80 (2019), 329-341. doi: 10.1016/j.asoc.2019.03.027
    [68] H. Shahparas, E. G. Mansoori, Developing an online general type-2 fuzzy classifier using evolving type-1 rules, Int. J. Approx. Reason., 113 (2019), 336-353. doi: 10.1016/j.ijar.2019.07.011
    [69] S. M. M. Golsefid, M. H. Fazel Zarandia, I. B. Turksen, Multi-central general type-2 fuzzy clustering approach for pattern recognitions, Inf. Sci., 328 (2016), 172-188. doi: 10.1016/j.ins.2015.08.027
    [70] J. M. Mendel, Comparing the performance potentials of interval and general type-2 rule-based fuzzy systems in terms of sculpting the state space, IEEE Trans. Fuzzy Syst., 27 (2019), 58-71. doi: 10.1109/TFUZZ.2018.2856184
    [71] D. R. Wu, J. M. Mendel, Similarity measures for closed general type-2 fuzzy sets: overview, comparisons, and a geometric approach, IEEE Trans. Fuzzy Syst., 27 (2019), 515-526. doi: 10.1109/TFUZZ.2018.2862869
    [72] Y. Chen, D. Z. Wang, Forecasting by general type-2 fuzzy logic systems optimized with QPSO algorithms, Int. J. Control Autom., 15 (2017), 2950-2958. doi: 10.1007/s12555-017-0793-0
    [73] J. Andreu-Perez, F. Cao, H. Hagras, G. Yang, A self-adaptive online brain-machine interface of a humanoid robot through a general type-2 fuzzy inference system, IEEE Trans. Fuzzy Syst., 26 (2018), 101-116. doi: 10.1109/TFUZZ.2016.2637403
    [74] K. Mittal, A. Jain, K. S. Vaisla, O. Castillo, J. Kacprzyk, A comprehensive review on type 2 fuzzy logic applications: Past, present and future, Eng. Appl. Artif. Intell., 95 (2020), 103916. doi: 10.1016/j.engappai.2020.103916
    [75] J. M. Mendel, H. Hagras, W. W. Tan, W. W. Melek, H. Ying, Introduction to Type-2 Fuzzy Logic Control: Theory and Applications, John Wiley and IEEE Press, Hoboken, NJ, 2014.
    [76] J. M. Mendel, Uncertain Rule Based Fuzzy Logic Systems: Introduction and New Directions: 2nd edition, Springer Press, New York, 2017.
    [77] J. M. Mendel, M. R. Rajati, P. Sussner, On clarifying some definitions and notations used for type-2 fuzzy sets as well as some recommended changes, Inf. Sci., 340 (2016), 347-345.
    [78] M. Nie, W. W. Tan, Towards an efficient type-reduction method for interval type-2 fuzzy logic systems, IEEE Int. Conf. Fuzzy Syst., (2008), 1425-1432.
    [79] J. M. Mendel, X. W. Liu, Simplified interval type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 21 (2013), 1056-1069. doi: 10.1109/TFUZZ.2013.2241771
    [80] A. M. El-Nagar, M. El-Bardini M, Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system, ISA Trans., 53 (2014), 732-743.
    [81] A. M. El-Nagar, M. El-Bardini M, Practical realization for the interval type-2 fuzzy PD+I controller using a low-cost microcontroller, Arabian J. Sci. Eng., 39 (2014), 6463-6476.
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