Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey
Keywords:
soft computing, fuzzy logic, neural computing, genetic algorithm, intelligent industrial control systemAbstract
Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.
References
[1] Acikgoz, H. (2020). Real-time adaptive speed control of vector-controlled induction motor drive based on online-trained Type-2 fuzzy neural network controller, International Transactions on Electrical Energy Systems, 2020. DOI: 10.1002/2050-7038.12678. https://doi.org/10.1002/2050-7038.12678
[2] Ahmed, O.; Hocine, L.; Boubekeur, M.; Hervé, G. (2020). Supervisory control of building heating system with insulation changes using three architectures of neural networks, Journal of the Franklin Institute, 357(18), 13362-13385, 2020. https://doi.org/10.1016/j.jfranklin.2020.09.027
[3] Ali, O.A.M.; Ali, A.Y.; Sumait, B.S. (2015). Comparison between the effects of different types of membership functions on fuzzy logic controller performance, International Journal of Emerging Engineering Research and Technology, 3(3), 76-83, 2015.
[4] Arcos-Aviles, D.; Pascual, J.; Marroyo, L.; Sanchis, P.; Guinjoan, F. (2018). Fuzzy logic-based energy management system design for residential grid-connected microgrids, IEEE Transactions on Smart Grid, 9(2), 530-543, 2018. https://doi.org/10.1109/TSG.2016.2555245
[5] Ariza-Zambrano, W. C.; Serpa, A. L. (2020). Direct inverse control for active vibration suppression using artificial neural networks, Journal of Vibration and Control, 2020. DOI: 10.1177/1077546320924253. https://doi.org/10.1177/1077546320924253
[6] Bashir, A. O.; Rui, X. T.; Zhang, J. S. (2019). Ride comfort improvement of a semi-active vehicle suspension based on hybrid fuzzy and fuzzy-PID controller, Studies in Informatics and Control, 28(4), 421-430, 2019. https://doi.org/10.24846/v28i4y201906
[7] Ben Nasr, M.; Chtourou, M. (2014). Neural network control of nonlinear dynamic systems using hybrid algorithm, Applied Soft Computing, 24, 423-431, 2014. https://doi.org/10.1016/j.asoc.2014.07.023
[8] Bozorg-Haddad, O.; Solgi, M.; Loáiciga, H. A. (2017). Genetic algorithm, In: Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization, John Wiley & Sons, Chapter 004, 53-67, 2017. https://doi.org/10.1002/9781119387053.ch4
[9] Carvajal, J.; Chen, G. R.; Ogmen, H. (2000). Fuzzy PID controller: Design, performance evaluation, and stability analysis, Information Sciences, 123(3), 249-270, 2000. https://doi.org/10.1016/S0020-0255(99)00127-9
[10] Chang, M. K. (2010). An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators, Control Engineering Practice, 18(1), 13-22, 2010. https://doi.org/10.1016/j.conengprac.2009.08.005
[11] Chen, C. Y.; Li, T. H. S.; Yeh, Y. C. (2009). EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots, Information Sciences, 179(1), 180-195, 2009. https://doi.org/10.1016/j.ins.2008.09.012
[12] Dzitac, I. (2015). The fuzzification of classical structures: A general view, International Journal of Computers Communications & Control, 10(6), 772-788, 2015. https://doi.org/10.15837/ijccc.2015.6.2069
[13] Dzitac, I.; Filip, F. G.; Manolescu, M. J. (2017). Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, 12(6), 748-789, 2017. https://doi.org/10.15837/ijccc.2017.6.3111
[14] Eck, N.; Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84(2), 523-538, 2010. https://doi.org/10.1007/s11192-009-0146-3
[15] El Hamidi, K.; Mjahed, M.; El Kari, A.; Ayad, H. (2019). Neural network and fuzzy-logic-based self-tuning PID control for quadcopter path tracking, Studies in Informatics and Control, 28(4), 401-412, 2019. https://doi.org/10.24846/v28i4y201904
[16] Erb, R. J. (1993). Introduction to backpropagation neural network computation, Pharmaceutical Research, 10(2), 165-170, 1993. https://doi.org/10.1023/A:1018966222807
[17] Gao, S. G.; Dong, H. R.; Ning, B.; Chen, Y.; Sun, X. B. (2015). Adaptive fault-tolerant automatic train operation using RBF neural networks, Neural Computing and Applications, 26(1), 141-149, 2015. https://doi.org/10.1007/s00521-014-1705-y
[18] Gertler, J. J. (1988). Survey of model-based failure detection and isolation in complex plants, IEEE Control Systems Magazine, 8(6), 3-11, 1988. https://doi.org/10.1109/37.9163
[19] Gu, J.; Qiang, D. (2015). The design of intelligent washing machine controller based on FPGA, In: Proceedings of the 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 1529-1532, Qinhuangdao, China, Sep. 18-20, 2015. https://doi.org/10.1109/IMCCC.2015.324
[20] Guo, C.; Kwok, D. P.; Tam, P. K. S.; Yu, J. Z. (1997). GA-optimized multimode intelligent controller, In: Proceedings of the 23rd International Conference on Industrial Electronics, Control, and Instrumentation, 328-332, New Orleans, America, Nov. 09, 1997.
[21] Hacene, N.; Mendil, B. (2019). Fuzzy behavior-based control of three wheeled omnidirectional mobile robot, International Journal of Automation and Computing, 16(2), 163-185, 2019. https://doi.org/10.1007/s11633-018-1135-x
[22] Harzelli, I.; Menacer, A.; Ameid, T. (2020). A fault monitoring approach using model-based and neural network techniques applied to input-output feedback linearization control induction motor, Journal of Ambient Intelligence and Humanized Computing, 11(6), 2519-2538, 2020. https://doi.org/10.1007/s12652-019-01307-0
[23] Ho, G. T. S.; Lau, H. C. W.; Choy, K. L.; Lee, C. K. M.; Lam, H. Y. (2015). Providing decision support for replenishment operations using a genetic algorithms based fuzzy system, Expert Systems, 32(1), 23-38, 2015. https://doi.org/10.1111/exsy.12053
[24] Hornik, K.; Stinchcombe, M.; White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2(5), 359-366, 1989. https://doi.org/10.1016/0893-6080(89)90020-8
[25] Huang, S. A.; Tan, K. K.; Xiao, M. B. (2015). Automated fault diagnosis and accommodation control for mechanical systems, IEEE/ASME Transactions on Mechatronics, 20(1), 155-165, 2015. https://doi.org/10.1109/TMECH.2014.2322652
[26] Huang, Y.; Cao, F.; Ke, B. R.; Tang, T. (2016). Modelling and optimisation of train electric drive system based on fuzzy predictive control in urban rail transit, International Journal of Simulation and Process Modelling, 11(5), 363-373, 2016. https://doi.org/10.1504/IJSPM.2016.079198
[27] Hunt, K. J.; Sbarbaro, D; Zbikowski, R.; Gawthrop, P. J. (1992). Neural networks for control systems-a survey, Automatica, 28(6), 1083-1112, 1992. https://doi.org/10.1016/0005-1098(92)90053-I
[28] Jamshidi, M. (2003). Tools for intelligent control: Fuzzy controllers, neural networks and genetic algorithms, Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, 361(1809), 1781-1808, 2003. https://doi.org/10.1098/rsta.2003.1225
[29] Jandaghian, M.; Setayeshi, S.; Keymanesh, M.; Arabalibeik, H. (2008). Decision making strategies for intelligent control system of train speed & train dispatch in Iran railway, In: Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, 681-686, Beijing, China, Oct. 12-15, 2008. https://doi.org/10.1109/ITSC.2008.4732557
[30] Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685, 1993. https://doi.org/10.1109/21.256541
[31] Javadi-Moghaddam, J.; Bagheri, A. (2010). An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle, Expert Systems with Applications, 37(1), 647-660, 2010. https://doi.org/10.1016/j.eswa.2009.06.015
[32] Jeon, G. J.; Lee, I. (1996). Neural network indirect adaptive control with fast learning algorithm, Neurocomputing, 13(2-4), 185-199, 1996. https://doi.org/10.1016/0925-2312(95)00091-7
[33] Jia, Q. X.; Chen, W.; Zhang, Y. C.; Li, H. Y. (2015). Fault reconstruction and fault-tolerant control via learning observers in Takagi-Sugeno fuzzy descriptor systems with time delays, IEEE Transactions on Industrial Electronics, 62(6), 3885-3895, 2015.
[34] Kadlec, P.; Gabrys, B.; Strandt, S. (2009). Data-driven soft sensors in the process industry, Computers & Chemical Engineering, 33(4), 795-814, 2009. https://doi.org/10.1016/j.compchemeng.2008.12.012
[35] Karnik, N. N.; Mendel, J. M. (2001). Centroid of a type-2 fuzzy set, Information Sciences, 132(1- 4), 195-220, 2001. https://doi.org/10.1016/S0020-0255(01)00069-X
[36] Karnik, N. N.; Mendel, J. M.; Liang, Q. L. (1999). Type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems, 7(6), 643-658, 1999. https://doi.org/10.1109/91.811231
[37] Kataria, G.; Singh, K. (2018). Recurrent neural network based soft sensor for monitoring and controlling a reactive distillation column, Chemical Product and Process Modeling, 13(3), 1-12, 2018. https://doi.org/10.1515/cppm-2017-0044
[38] Kondakci, T.; Zhou, W. B. (2017). Recent applications of advanced control techniques in food industry, Food and Bioprocess Technology, 10(3), 522-542, 2017. https://doi.org/10.1007/s11947-016-1831-x
[39] Larrea, M.; Larzabal, E.; Irigoyen, E.; Valera, J. J.; Dendaluce, M. (2015). Implementation and testing of a soft computing based model predictive control on an industrial controller, Journal of Applied Logic, 13(2), 114-125, 2015. https://doi.org/10.1016/j.jal.2014.11.005
[40] Lasri, R.; Rojas, I.; Pomares, H.; Sadouq, Z. (2011). Explaining how intelligent control has improved the way we live: A survey on the use of fuzzy logic controllers in daily human life, In: Proceedings of the 2011 International Conference on Multimedia Computing and Systems, 1-6, Ouarzazate, Morocco, Apr. 07-09, 2011. https://doi.org/10.1109/ICMCS.2011.5945577
[41] Lee, H; Kim, E.; Kang, H. J.; Park, M. (2001). A new sliding-mode control with fuzzy boundary layer, Fuzzy Sets and Systems, 120(1), 135-143, 2001. https://doi.org/10.1016/S0165-0114(99)00072-X
[42] Leung, F. H. F.; Lam, H. K.; Ling, S. H.; Tam, P. K. S. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm, IEEE Transactions on Neural Networks, 14(1), 79-88, 2003. https://doi.org/10.1109/TNN.2002.804317
[43] Lin, C. T.; Lee, C. S. G. (1991). Neural-network-based fuzzy logic control and decision system, IEEE Transactions on Computers, 40(12), 1320-1336, 1991. https://doi.org/10.1109/12.106218
[44] Liu, J. X.; Yang, L. D.; Xu, M. J.; Zhang, Q.; Yan, R. Q.; Chen, X. F. (2021). Model-based detection of soft faults using the smoothed residual for a control system, Measurement Science and Technology, 32(1), 2021. DOI: 10.1088/1361-6501/abaf2b. https://doi.org/10.1088/1361-6501/abaf2b
[45] Liu, Y.; Zhou, Q.; Lan, T.; Lei, J. W. (2017). Adaptive fuzzy sliding mode controller design for saucer-shaped aircraft, In: Proceedings of the 2nd International Conference on Control and Robotics Engineering (ICCRE), 82-85, Bangkok, Thailand, Apr. 01-03, 2017. https://doi.org/10.1109/ICCRE.2017.7935047
[46] Lü, X. Q.; Wu, Y. B.; Lian, J.; Zhang, Y. Y.; Chen, C.; Wang, P. S.; Meng, L. Z. (2020). Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm, Energy Conversion and Management, 205, 1-26, 2020. https://doi.org/10.1016/j.enconman.2020.112474
[47] Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration, 6, 1-10, 2017. https://doi.org/10.1016/j.jii.2017.04.005
[48] Mamdani, E.; Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-machine Studies, 7(1), 1-13, 1975. https://doi.org/10.1016/S0020-7373(75)80002-2
[49] Mendel, J. M.; Hagras, H; Tan, W.; Melek, W. W.; Ying, H. (2014). Introduction to type-2 fuzzy logic control: Theory and applications, In: IEEE Press Series on Computational Intelligence, John Wiley & Sons, 2014. https://doi.org/10.1002/9781118886540
[50] Meng, W.; Sheng, L. H.; Qing, M.; Rong, B. G. (2014). Intelligent control algorithm for ship dynamic positioning, Archives of Control Sciences, 24(4), 479-497, 2014. https://doi.org/10.2478/acsc-2014-0026
[51] Mittal, K.; Jain, A.; Vaisla, K. S.; Castillo, O.; Kacprzyk, J. (2020). A comprehensive review on type 2 fuzzy logic applications: Past, present and future, Engineering Applications of Artificial Intelligence, 95, 2020. DOI: 10.1016/j.engappai.2020.103916. https://doi.org/10.1016/j.engappai.2020.103916
[52] Nahin, P. (2017). Boolean algebra, In: The Logician and the Engineer, Princeton University Press, Chapter 004, 43-65, 2017. https://doi.org/10.23943/princeton/9780691176000.003.0004
[53] Nguyen, D. H.; Widrow, B. (1990). Neural networks for self-learning control systems, IEEE Control Systems Magazine, 10(3), 18-23, 1990. https://doi.org/10.1109/37.55119
[54] Pei, D. W. (2014). A survey of fuzzy implication algebras and their axiomatization, International Journal of Approximate Reasoning, 55(8), 1643-1658, 2014. https://doi.org/10.1016/j.ijar.2014.05.008
[55] Precup, R.; Hellendoorn, H. (2011). A survey on industrial applications of fuzzy control, Computers in Industry, 62(3), 213-226, 2011. https://doi.org/10.1016/j.compind.2010.10.001
[56] Qin, Z. F.; Bai, M. Y.; Ralescu, D. (2011). A fuzzy control system with application to production planning problems, Information Sciences, 181(5), 1018-1027, 2011. https://doi.org/10.1016/j.ins.2010.10.029
[57] Radziszewska-Zielina, E. (2011). Fuzzy control of partnering relations of a construction enterprise, Journal of Civil Engineering and Management, 17(1), 5-15, 2011. https://doi.org/10.3846/13923730.2011.554172
[58] Rotenberg, V. (2013). Moravec's paradox: Consideration in the context of two brain hemisphere functions, Activitas Nervosa Superior, 55(3), 108-111, 2013. https://doi.org/10.1007/BF03379600
[59] Sardarmehni, T.; Ashtiani, A. A.; Menhaj, M. B. (2019). Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines, Soft Computing, 23(15), 6169-6182, 2019. https://doi.org/10.1007/s00500-018-3270-2
[60] Shang, C.; Yang, F.; Huang, D. X; Lyu, W. X. (2014). Data-driven soft sensor development based on deep learning technique, Journal of Process Control, 24(3), 223-233, 2014. https://doi.org/10.1016/j.jprocont.2014.01.012
[61] Shen, Y.; Liu, L. J.; Dowell, E. H. (2013). Adaptive fault-tolerant robust control for a linear system with adaptive fault identification, IET Control Theory and Applications, 7(2), 246-252, 2013. https://doi.org/10.1049/iet-cta.2012.0696
[62] Silva, J.; Marques, D.; Aquino, R.; Nobrega, O. (2019). A PLC-based fuzzy logic control with metaheuristic tuning, Studies in Informatics and Control, 28(3), 265-278, 2019. https://doi.org/10.24846/v28i3y201903
[63] Sitharthan, R.; Krishnamoorthy, S.; Sanjeevikumar, P.; Holm-Nielsen, J. B.; Singh, R. R.; Rajesh, M. (2020). Torque ripple minimization of PMSM using an adaptive Elman neural networkcontrolled feedback linearization-based direct torque control strategy, International Transactions on Electrical Energy Systems, 2020. DOI: 10.1002/2050-7038.12685. https://doi.org/10.1002/2050-7038.12685
[64] Sun, W. K.; Paiva, A. R. C.; Xu, P.; Sundaram, A.; Braatz, R. D. (2020). Fault detection and identification using Bayesian recurrent neural networks, Computers & Chemical Engineering, 141, 2020. DOI: 10.1016/j.compchemeng.2020.106991 https://doi.org/10.1016/j.compchemeng.2020.106991
[65] Su, T. J.; Lo, K. L.; Lee, F. C., Chang, Y. H. (2020). Aircraft approaching service of terminal control based on fuzzy control, International Journal of Modern Physics B, 34, 22-24, 2020. https://doi.org/10.1142/S0217979220401426
[66] Takagi, T.; Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, Smc-15, 116-132, 1985. https://doi.org/10.1109/TSMC.1985.6313399
[67] Talon, A.; Curt, C. (2017). Selection of appropriate defuzzification methods: Application to the assessment of dam performance, Expert Systems with Applications, 70, 160-174, 2017. https://doi.org/10.1016/j.eswa.2016.09.004
[68] Tang, K. S.; Kim, F. M.; Chen, G. R.; Kwong, S. (2001). An optimal fuzzy PID controller, IEEE Transactions on Industrial Electronics, 48(4), 757-765, 2001. https://doi.org/10.1109/41.937407
[69] Teng, L.; Gull, M. A.; Bai, S. P. (2020). PD-based fuzzy sliding mode control of a wheelchair exoskeleton robot, IEEE-ASME Transactions on Mechatronics, 25(5), 2546-2555, 2020. https://doi.org/10.1109/TMECH.2020.2983520
[70] Tham, M. T.; Montague, G. A.; Julian Morris, A.; Lant, P. A. (1991). Soft-sensors for process estimation and inferential control, Journal of Process Control, 1(1), 3-14, 1991. https://doi.org/10.1016/0959-1524(91)87002-F
[71] Tian, Z. D. (2017). Main steam temperature control based on GA-BP optimised fuzzy neural network, International Journal of Engineering Systems Modelling and Simulation, 9(3), 150-160, 2017. https://doi.org/10.1504/IJESMS.2017.10005495
[72] Tiwari, S.; Bhatt, A.; Unni, A. C.; Singh, J. G.; Ongsakul, W. (2018). Control of DC motor using genetic algorithm based PID controller, In: Proceedings of the 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), 1-6, Phuket, Thailand, Oct. 24-26, 2018. https://doi.org/10.23919/ICUE-GESD.2018.8635662
[73] Tong, S. C.; Huo, B. Y; Li, Y. M. (2014). Observer-based adaptive decentralized fuzzy faulttolerant control of nonlinear large-scale systems with actuator failures, IEEE Transactions on Fuzzy Systems, 22(1), 1-15, 2014. https://doi.org/10.1109/TFUZZ.2013.2241770
[74] Van Leekwijck, W.; Kerre, E. E. (1999). Defuzzification: Criteria and classification, Fuzzy Sets and Systems, 108(2), 159-178, 1999. https://doi.org/10.1016/S0165-0114(97)00337-0
[75] Varshney, K.; Panigrahi, P. K. (2005). Artificial neural network control of a heat exchanger in a closed flow air circuit, Applied Soft Computing, 5(4), 441-465, 2005. https://doi.org/10.1016/j.asoc.2004.10.004
[76] Vu, D. H.; Huang, S.; Tran, T. D.; Vu, T. Y.; Pham, V. C. (2019). A robust adaptive control using fuzzy neural network for robot manipulators with dead-zone, International Journal of Computers Communications & Control, 14(5), 692-710, 2019.
[77] Wai, R. J.; Lin, C. M.; Hsu, C. F. (2004). Adaptive fuzzy sliding-mode control for electrical servo drive, Fuzzy Sets and Systems, 143(2), 295-310, 2004. https://doi.org/10.1016/S0165-0114(03)00199-4
[78] Wai, R. J.; Tu, C. H. (2007). Design of total sliding-mode-based genetic algorithm control for hybrid resonant-driven linear piezoelectric ceramic motor, IEEE Transactions on Power Electronics, 22(2), 563-575, 2007. https://doi.org/10.1109/TPEL.2006.889988
[79] Wang, C. H.; Hung, K. N. (2013). Intelligent adaptive law for missile guidance using fuzzy neural networks, International Journal of Fuzzy Systems, 15(2), 182-191, 2013.
[80] Wang, Z. X.; Wang, G. J.; Guo, R. W. (2018). Design for vegetable waste fermentation control systems based on semi-tensor product fuzzy controller, Advances in Mechanical Engineering, 10(8), 1-11, 2018. https://doi.org/10.1177/1687814018793551
[81] Wu, B.; Cheng, T. T; Yip, T. L.; Wang, Y. (2020). Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes, Ocean Engineering, 197, 2020. DOI: 10.1016/j.oceaneng.2019.106909. https://doi.org/10.1016/j.oceaneng.2019.106909
[82] Wu, D. R.; Lin, C. T.; Huang, J.; Zeng, Z. G. (2020). On the functional equivalence of TSK fuzzy systems to neural networks, mixture of experts, CART, and stacking ensemble regression, IEEE Transactions on Fuzzy Systems, 28(10), 2570-2580, 2020. https://doi.org/10.1109/TFUZZ.2019.2941697
[83] Wu, L. B.; Park, J. H. (2020). Adaptive fault-tolerant control of uncertain switched nonaffine nonlinear systems with actuator faults and time delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(9), 3470-3480, 2020. https://doi.org/10.1109/TSMC.2019.2894750
[84] Xibilia, M. G.; Latino, M.; Marinkovic, Z.; Atanaskovic, A.; Donato, N. (2020). Soft sensors based on deep neural networks for applications in security and safety, IEEE Transactions on Instrumentation and Measurement, 69(10), 7869-7876, 2020. https://doi.org/10.1109/TIM.2020.2984465
[85] Xie, S. W.; Xie, Y. F.; Li, F. B.; Yang, C. H.; Gui, W. H. (2020). Optimal setting and control for iron removal process based on adaptive neural network soft-sensor, IEEE Transactions on Systems, Man, and Cybernetics, 50(7), 2408-2420, 2020. https://doi.org/10.1109/TSMC.2018.2815580
[86] Xing, S. Y.; Ju, J. G.; Xing, J. S. (2019). Research on hot-rolling steel products quality control based on BP neural network inverse model, Neural Computing and Applications, 31(5), 1577-1584, 2019. https://doi.org/10.1007/s00521-018-3547-5
[87] Xu, R.; Bai, H. (2011). Research of AC adjusting speed system based on DTC and neural network supervision control, In: Proceedings of the 2nd International Conference on Mechanic Automation and Control Engineering, 4279-4281, Hohhot, China, July 15-17, 2011.
[88] Yang, J. M.; Cheng, K. W. E.; Wu, J.; Dong, P.; Wang, B. (2004). The study of the energy management system based-on fuzzy control for distributed hybrid wind-solar power system, In: Proceedings of the 1st International Conference on Power Electronics Systems and Applications, 113-117, Hong Kong, China, Nov. 09-11, 2004.
[89] Yang, S. C.; Li, M.; Xu, B.; Guo, B.; Zhu. C. G. (2010). Optimization of fuzzy controller based on genetic algorithm, In: Proceedings of the 2010 International Conference on Intelligent System Design and Engineering Application, 2, 21-28, Changsha, China, Oct. 13-14, 2010. https://doi.org/10.1109/ISDEA.2010.159
[90] Yuan, M.; Wu, Y. T.; Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network, In: Proceedings of the 2016 IEEE International Conference on Aircraft Utility Systems, 135-140, Beijing, China, Oct. 10-12, 2016. https://doi.org/10.1109/AUS.2016.7748035
[91] Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8(3), 338-353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X
[92] Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics, 3(1), 28-44, 1973. https://doi.org/10.1109/TSMC.1973.5408575
[93] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I, Information Sciences, 8(3), 199-249, 1975. https://doi.org/10.1016/0020-0255(75)90036-5
[94] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-II, Information Sciences, 8(4), 301-357, 1975. https://doi.org/10.1016/0020-0255(75)90046-8
[95] Zadeh, L. A. (1994). Soft computing and fuzzy logic, IEEE Software, 11(6), 48-56, 1994. https://doi.org/10.1109/52.329401
[96] Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178(13), 2751-2779, 2008. https://doi.org/10.1016/j.ins.2008.02.012
[97] Zavadskas, E. K.; Bausys, R.; Lescauskiene, I.; Omran, J. (2020). M-generalised q-neutrosophic MULTIMOORA for decision making, Studies in Informatics and Control, 29(4), 389-398, 2020. https://doi.org/10.24846/v29i4y202001
[98] Zavadskas, E. K.; Stevic, Ž.; Turskis, Z.; Tomaševic, M. (2019). A novel extended EDAS in Minkowski space (EDAS-M) method for evaluating autonomous vehicles, Studies in Informatics and Control, 28(3), 255-264, 2019. https://doi.org/10.24846/v28i3y201902
[99] Zhang, G.; Gong, X. S.; Chen, X. R. (2017). PID control algorithm based on genetic algorithm and its application in electric cylinder control, International Journal of Information Technology and Web Engineering, 12(3), 51-61, 2017. https://doi.org/10.4018/IJITWE.2017070105
[100] Zhang, J. Y.; Li, Y. D. (2006). Application of genetic algorithm in optimization of fuzzy control rules, In: Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, 529-532, Jinan, China, OCT. 16-18, 2006.
[101] Zhang, X. Z.; Wang, Y. N. (2015). Design of robust fuzzy sliding-mode controller for a class of uncertain Takagi-Sugeno nonlinear systems, International Journal of Computers Communications & Control, 10(1), 136-146, 2015. https://doi.org/10.15837/ijccc.2015.1.1572
[102] Zhang, Y. C.; Zhao, W. Z.; Chen, J. L. (2012). The research and application of the fuzzy neural network control based on genetic algorithm, Advanced Materials Research, 403, 191-195, 2012. https://doi.org/10.4028/www.scientific.net/AMR.403-408.191
[103] Zhao, Y. N.; Collins, E. G. (2003). Fuzzy PI control design for an industrial weigh belt feeder, IEEE Transactions on Fuzzy Systems, 11(3), 311-319, 2003. https://doi.org/10.1109/TFUZZ.2003.812686
[104] Zhong, S. S.; Fu, S.; Lin, L. (2019). A novel gas turbine fault diagnosis method based on transfer learning with CNN, Measurement, 137, 435-453, 2019. https://doi.org/10.1016/j.measurement.2019.01.022
[105] Ziegler, J. G.; Nichols, N. B. (1993). Optimum settings for automatic controllers, Journal of Dynamic Systems, Measurement, and Control, 115(2B), 220-222, 1993. https://doi.org/10.1115/1.2899060
[2] Ahmed, O.; Hocine, L.; Boubekeur, M.; Hervé, G. (2020). Supervisory control of building heating system with insulation changes using three architectures of neural networks, Journal of the Franklin Institute, 357(18), 13362-13385, 2020. https://doi.org/10.1016/j.jfranklin.2020.09.027
[3] Ali, O.A.M.; Ali, A.Y.; Sumait, B.S. (2015). Comparison between the effects of different types of membership functions on fuzzy logic controller performance, International Journal of Emerging Engineering Research and Technology, 3(3), 76-83, 2015.
[4] Arcos-Aviles, D.; Pascual, J.; Marroyo, L.; Sanchis, P.; Guinjoan, F. (2018). Fuzzy logic-based energy management system design for residential grid-connected microgrids, IEEE Transactions on Smart Grid, 9(2), 530-543, 2018. https://doi.org/10.1109/TSG.2016.2555245
[5] Ariza-Zambrano, W. C.; Serpa, A. L. (2020). Direct inverse control for active vibration suppression using artificial neural networks, Journal of Vibration and Control, 2020. DOI: 10.1177/1077546320924253. https://doi.org/10.1177/1077546320924253
[6] Bashir, A. O.; Rui, X. T.; Zhang, J. S. (2019). Ride comfort improvement of a semi-active vehicle suspension based on hybrid fuzzy and fuzzy-PID controller, Studies in Informatics and Control, 28(4), 421-430, 2019. https://doi.org/10.24846/v28i4y201906
[7] Ben Nasr, M.; Chtourou, M. (2014). Neural network control of nonlinear dynamic systems using hybrid algorithm, Applied Soft Computing, 24, 423-431, 2014. https://doi.org/10.1016/j.asoc.2014.07.023
[8] Bozorg-Haddad, O.; Solgi, M.; Loáiciga, H. A. (2017). Genetic algorithm, In: Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization, John Wiley & Sons, Chapter 004, 53-67, 2017. https://doi.org/10.1002/9781119387053.ch4
[9] Carvajal, J.; Chen, G. R.; Ogmen, H. (2000). Fuzzy PID controller: Design, performance evaluation, and stability analysis, Information Sciences, 123(3), 249-270, 2000. https://doi.org/10.1016/S0020-0255(99)00127-9
[10] Chang, M. K. (2010). An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators, Control Engineering Practice, 18(1), 13-22, 2010. https://doi.org/10.1016/j.conengprac.2009.08.005
[11] Chen, C. Y.; Li, T. H. S.; Yeh, Y. C. (2009). EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots, Information Sciences, 179(1), 180-195, 2009. https://doi.org/10.1016/j.ins.2008.09.012
[12] Dzitac, I. (2015). The fuzzification of classical structures: A general view, International Journal of Computers Communications & Control, 10(6), 772-788, 2015. https://doi.org/10.15837/ijccc.2015.6.2069
[13] Dzitac, I.; Filip, F. G.; Manolescu, M. J. (2017). Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, 12(6), 748-789, 2017. https://doi.org/10.15837/ijccc.2017.6.3111
[14] Eck, N.; Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84(2), 523-538, 2010. https://doi.org/10.1007/s11192-009-0146-3
[15] El Hamidi, K.; Mjahed, M.; El Kari, A.; Ayad, H. (2019). Neural network and fuzzy-logic-based self-tuning PID control for quadcopter path tracking, Studies in Informatics and Control, 28(4), 401-412, 2019. https://doi.org/10.24846/v28i4y201904
[16] Erb, R. J. (1993). Introduction to backpropagation neural network computation, Pharmaceutical Research, 10(2), 165-170, 1993. https://doi.org/10.1023/A:1018966222807
[17] Gao, S. G.; Dong, H. R.; Ning, B.; Chen, Y.; Sun, X. B. (2015). Adaptive fault-tolerant automatic train operation using RBF neural networks, Neural Computing and Applications, 26(1), 141-149, 2015. https://doi.org/10.1007/s00521-014-1705-y
[18] Gertler, J. J. (1988). Survey of model-based failure detection and isolation in complex plants, IEEE Control Systems Magazine, 8(6), 3-11, 1988. https://doi.org/10.1109/37.9163
[19] Gu, J.; Qiang, D. (2015). The design of intelligent washing machine controller based on FPGA, In: Proceedings of the 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 1529-1532, Qinhuangdao, China, Sep. 18-20, 2015. https://doi.org/10.1109/IMCCC.2015.324
[20] Guo, C.; Kwok, D. P.; Tam, P. K. S.; Yu, J. Z. (1997). GA-optimized multimode intelligent controller, In: Proceedings of the 23rd International Conference on Industrial Electronics, Control, and Instrumentation, 328-332, New Orleans, America, Nov. 09, 1997.
[21] Hacene, N.; Mendil, B. (2019). Fuzzy behavior-based control of three wheeled omnidirectional mobile robot, International Journal of Automation and Computing, 16(2), 163-185, 2019. https://doi.org/10.1007/s11633-018-1135-x
[22] Harzelli, I.; Menacer, A.; Ameid, T. (2020). A fault monitoring approach using model-based and neural network techniques applied to input-output feedback linearization control induction motor, Journal of Ambient Intelligence and Humanized Computing, 11(6), 2519-2538, 2020. https://doi.org/10.1007/s12652-019-01307-0
[23] Ho, G. T. S.; Lau, H. C. W.; Choy, K. L.; Lee, C. K. M.; Lam, H. Y. (2015). Providing decision support for replenishment operations using a genetic algorithms based fuzzy system, Expert Systems, 32(1), 23-38, 2015. https://doi.org/10.1111/exsy.12053
[24] Hornik, K.; Stinchcombe, M.; White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2(5), 359-366, 1989. https://doi.org/10.1016/0893-6080(89)90020-8
[25] Huang, S. A.; Tan, K. K.; Xiao, M. B. (2015). Automated fault diagnosis and accommodation control for mechanical systems, IEEE/ASME Transactions on Mechatronics, 20(1), 155-165, 2015. https://doi.org/10.1109/TMECH.2014.2322652
[26] Huang, Y.; Cao, F.; Ke, B. R.; Tang, T. (2016). Modelling and optimisation of train electric drive system based on fuzzy predictive control in urban rail transit, International Journal of Simulation and Process Modelling, 11(5), 363-373, 2016. https://doi.org/10.1504/IJSPM.2016.079198
[27] Hunt, K. J.; Sbarbaro, D; Zbikowski, R.; Gawthrop, P. J. (1992). Neural networks for control systems-a survey, Automatica, 28(6), 1083-1112, 1992. https://doi.org/10.1016/0005-1098(92)90053-I
[28] Jamshidi, M. (2003). Tools for intelligent control: Fuzzy controllers, neural networks and genetic algorithms, Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, 361(1809), 1781-1808, 2003. https://doi.org/10.1098/rsta.2003.1225
[29] Jandaghian, M.; Setayeshi, S.; Keymanesh, M.; Arabalibeik, H. (2008). Decision making strategies for intelligent control system of train speed & train dispatch in Iran railway, In: Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, 681-686, Beijing, China, Oct. 12-15, 2008. https://doi.org/10.1109/ITSC.2008.4732557
[30] Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685, 1993. https://doi.org/10.1109/21.256541
[31] Javadi-Moghaddam, J.; Bagheri, A. (2010). An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle, Expert Systems with Applications, 37(1), 647-660, 2010. https://doi.org/10.1016/j.eswa.2009.06.015
[32] Jeon, G. J.; Lee, I. (1996). Neural network indirect adaptive control with fast learning algorithm, Neurocomputing, 13(2-4), 185-199, 1996. https://doi.org/10.1016/0925-2312(95)00091-7
[33] Jia, Q. X.; Chen, W.; Zhang, Y. C.; Li, H. Y. (2015). Fault reconstruction and fault-tolerant control via learning observers in Takagi-Sugeno fuzzy descriptor systems with time delays, IEEE Transactions on Industrial Electronics, 62(6), 3885-3895, 2015.
[34] Kadlec, P.; Gabrys, B.; Strandt, S. (2009). Data-driven soft sensors in the process industry, Computers & Chemical Engineering, 33(4), 795-814, 2009. https://doi.org/10.1016/j.compchemeng.2008.12.012
[35] Karnik, N. N.; Mendel, J. M. (2001). Centroid of a type-2 fuzzy set, Information Sciences, 132(1- 4), 195-220, 2001. https://doi.org/10.1016/S0020-0255(01)00069-X
[36] Karnik, N. N.; Mendel, J. M.; Liang, Q. L. (1999). Type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems, 7(6), 643-658, 1999. https://doi.org/10.1109/91.811231
[37] Kataria, G.; Singh, K. (2018). Recurrent neural network based soft sensor for monitoring and controlling a reactive distillation column, Chemical Product and Process Modeling, 13(3), 1-12, 2018. https://doi.org/10.1515/cppm-2017-0044
[38] Kondakci, T.; Zhou, W. B. (2017). Recent applications of advanced control techniques in food industry, Food and Bioprocess Technology, 10(3), 522-542, 2017. https://doi.org/10.1007/s11947-016-1831-x
[39] Larrea, M.; Larzabal, E.; Irigoyen, E.; Valera, J. J.; Dendaluce, M. (2015). Implementation and testing of a soft computing based model predictive control on an industrial controller, Journal of Applied Logic, 13(2), 114-125, 2015. https://doi.org/10.1016/j.jal.2014.11.005
[40] Lasri, R.; Rojas, I.; Pomares, H.; Sadouq, Z. (2011). Explaining how intelligent control has improved the way we live: A survey on the use of fuzzy logic controllers in daily human life, In: Proceedings of the 2011 International Conference on Multimedia Computing and Systems, 1-6, Ouarzazate, Morocco, Apr. 07-09, 2011. https://doi.org/10.1109/ICMCS.2011.5945577
[41] Lee, H; Kim, E.; Kang, H. J.; Park, M. (2001). A new sliding-mode control with fuzzy boundary layer, Fuzzy Sets and Systems, 120(1), 135-143, 2001. https://doi.org/10.1016/S0165-0114(99)00072-X
[42] Leung, F. H. F.; Lam, H. K.; Ling, S. H.; Tam, P. K. S. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm, IEEE Transactions on Neural Networks, 14(1), 79-88, 2003. https://doi.org/10.1109/TNN.2002.804317
[43] Lin, C. T.; Lee, C. S. G. (1991). Neural-network-based fuzzy logic control and decision system, IEEE Transactions on Computers, 40(12), 1320-1336, 1991. https://doi.org/10.1109/12.106218
[44] Liu, J. X.; Yang, L. D.; Xu, M. J.; Zhang, Q.; Yan, R. Q.; Chen, X. F. (2021). Model-based detection of soft faults using the smoothed residual for a control system, Measurement Science and Technology, 32(1), 2021. DOI: 10.1088/1361-6501/abaf2b. https://doi.org/10.1088/1361-6501/abaf2b
[45] Liu, Y.; Zhou, Q.; Lan, T.; Lei, J. W. (2017). Adaptive fuzzy sliding mode controller design for saucer-shaped aircraft, In: Proceedings of the 2nd International Conference on Control and Robotics Engineering (ICCRE), 82-85, Bangkok, Thailand, Apr. 01-03, 2017. https://doi.org/10.1109/ICCRE.2017.7935047
[46] Lü, X. Q.; Wu, Y. B.; Lian, J.; Zhang, Y. Y.; Chen, C.; Wang, P. S.; Meng, L. Z. (2020). Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm, Energy Conversion and Management, 205, 1-26, 2020. https://doi.org/10.1016/j.enconman.2020.112474
[47] Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration, 6, 1-10, 2017. https://doi.org/10.1016/j.jii.2017.04.005
[48] Mamdani, E.; Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-machine Studies, 7(1), 1-13, 1975. https://doi.org/10.1016/S0020-7373(75)80002-2
[49] Mendel, J. M.; Hagras, H; Tan, W.; Melek, W. W.; Ying, H. (2014). Introduction to type-2 fuzzy logic control: Theory and applications, In: IEEE Press Series on Computational Intelligence, John Wiley & Sons, 2014. https://doi.org/10.1002/9781118886540
[50] Meng, W.; Sheng, L. H.; Qing, M.; Rong, B. G. (2014). Intelligent control algorithm for ship dynamic positioning, Archives of Control Sciences, 24(4), 479-497, 2014. https://doi.org/10.2478/acsc-2014-0026
[51] Mittal, K.; Jain, A.; Vaisla, K. S.; Castillo, O.; Kacprzyk, J. (2020). A comprehensive review on type 2 fuzzy logic applications: Past, present and future, Engineering Applications of Artificial Intelligence, 95, 2020. DOI: 10.1016/j.engappai.2020.103916. https://doi.org/10.1016/j.engappai.2020.103916
[52] Nahin, P. (2017). Boolean algebra, In: The Logician and the Engineer, Princeton University Press, Chapter 004, 43-65, 2017. https://doi.org/10.23943/princeton/9780691176000.003.0004
[53] Nguyen, D. H.; Widrow, B. (1990). Neural networks for self-learning control systems, IEEE Control Systems Magazine, 10(3), 18-23, 1990. https://doi.org/10.1109/37.55119
[54] Pei, D. W. (2014). A survey of fuzzy implication algebras and their axiomatization, International Journal of Approximate Reasoning, 55(8), 1643-1658, 2014. https://doi.org/10.1016/j.ijar.2014.05.008
[55] Precup, R.; Hellendoorn, H. (2011). A survey on industrial applications of fuzzy control, Computers in Industry, 62(3), 213-226, 2011. https://doi.org/10.1016/j.compind.2010.10.001
[56] Qin, Z. F.; Bai, M. Y.; Ralescu, D. (2011). A fuzzy control system with application to production planning problems, Information Sciences, 181(5), 1018-1027, 2011. https://doi.org/10.1016/j.ins.2010.10.029
[57] Radziszewska-Zielina, E. (2011). Fuzzy control of partnering relations of a construction enterprise, Journal of Civil Engineering and Management, 17(1), 5-15, 2011. https://doi.org/10.3846/13923730.2011.554172
[58] Rotenberg, V. (2013). Moravec's paradox: Consideration in the context of two brain hemisphere functions, Activitas Nervosa Superior, 55(3), 108-111, 2013. https://doi.org/10.1007/BF03379600
[59] Sardarmehni, T.; Ashtiani, A. A.; Menhaj, M. B. (2019). Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines, Soft Computing, 23(15), 6169-6182, 2019. https://doi.org/10.1007/s00500-018-3270-2
[60] Shang, C.; Yang, F.; Huang, D. X; Lyu, W. X. (2014). Data-driven soft sensor development based on deep learning technique, Journal of Process Control, 24(3), 223-233, 2014. https://doi.org/10.1016/j.jprocont.2014.01.012
[61] Shen, Y.; Liu, L. J.; Dowell, E. H. (2013). Adaptive fault-tolerant robust control for a linear system with adaptive fault identification, IET Control Theory and Applications, 7(2), 246-252, 2013. https://doi.org/10.1049/iet-cta.2012.0696
[62] Silva, J.; Marques, D.; Aquino, R.; Nobrega, O. (2019). A PLC-based fuzzy logic control with metaheuristic tuning, Studies in Informatics and Control, 28(3), 265-278, 2019. https://doi.org/10.24846/v28i3y201903
[63] Sitharthan, R.; Krishnamoorthy, S.; Sanjeevikumar, P.; Holm-Nielsen, J. B.; Singh, R. R.; Rajesh, M. (2020). Torque ripple minimization of PMSM using an adaptive Elman neural networkcontrolled feedback linearization-based direct torque control strategy, International Transactions on Electrical Energy Systems, 2020. DOI: 10.1002/2050-7038.12685. https://doi.org/10.1002/2050-7038.12685
[64] Sun, W. K.; Paiva, A. R. C.; Xu, P.; Sundaram, A.; Braatz, R. D. (2020). Fault detection and identification using Bayesian recurrent neural networks, Computers & Chemical Engineering, 141, 2020. DOI: 10.1016/j.compchemeng.2020.106991 https://doi.org/10.1016/j.compchemeng.2020.106991
[65] Su, T. J.; Lo, K. L.; Lee, F. C., Chang, Y. H. (2020). Aircraft approaching service of terminal control based on fuzzy control, International Journal of Modern Physics B, 34, 22-24, 2020. https://doi.org/10.1142/S0217979220401426
[66] Takagi, T.; Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, Smc-15, 116-132, 1985. https://doi.org/10.1109/TSMC.1985.6313399
[67] Talon, A.; Curt, C. (2017). Selection of appropriate defuzzification methods: Application to the assessment of dam performance, Expert Systems with Applications, 70, 160-174, 2017. https://doi.org/10.1016/j.eswa.2016.09.004
[68] Tang, K. S.; Kim, F. M.; Chen, G. R.; Kwong, S. (2001). An optimal fuzzy PID controller, IEEE Transactions on Industrial Electronics, 48(4), 757-765, 2001. https://doi.org/10.1109/41.937407
[69] Teng, L.; Gull, M. A.; Bai, S. P. (2020). PD-based fuzzy sliding mode control of a wheelchair exoskeleton robot, IEEE-ASME Transactions on Mechatronics, 25(5), 2546-2555, 2020. https://doi.org/10.1109/TMECH.2020.2983520
[70] Tham, M. T.; Montague, G. A.; Julian Morris, A.; Lant, P. A. (1991). Soft-sensors for process estimation and inferential control, Journal of Process Control, 1(1), 3-14, 1991. https://doi.org/10.1016/0959-1524(91)87002-F
[71] Tian, Z. D. (2017). Main steam temperature control based on GA-BP optimised fuzzy neural network, International Journal of Engineering Systems Modelling and Simulation, 9(3), 150-160, 2017. https://doi.org/10.1504/IJESMS.2017.10005495
[72] Tiwari, S.; Bhatt, A.; Unni, A. C.; Singh, J. G.; Ongsakul, W. (2018). Control of DC motor using genetic algorithm based PID controller, In: Proceedings of the 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), 1-6, Phuket, Thailand, Oct. 24-26, 2018. https://doi.org/10.23919/ICUE-GESD.2018.8635662
[73] Tong, S. C.; Huo, B. Y; Li, Y. M. (2014). Observer-based adaptive decentralized fuzzy faulttolerant control of nonlinear large-scale systems with actuator failures, IEEE Transactions on Fuzzy Systems, 22(1), 1-15, 2014. https://doi.org/10.1109/TFUZZ.2013.2241770
[74] Van Leekwijck, W.; Kerre, E. E. (1999). Defuzzification: Criteria and classification, Fuzzy Sets and Systems, 108(2), 159-178, 1999. https://doi.org/10.1016/S0165-0114(97)00337-0
[75] Varshney, K.; Panigrahi, P. K. (2005). Artificial neural network control of a heat exchanger in a closed flow air circuit, Applied Soft Computing, 5(4), 441-465, 2005. https://doi.org/10.1016/j.asoc.2004.10.004
[76] Vu, D. H.; Huang, S.; Tran, T. D.; Vu, T. Y.; Pham, V. C. (2019). A robust adaptive control using fuzzy neural network for robot manipulators with dead-zone, International Journal of Computers Communications & Control, 14(5), 692-710, 2019.
[77] Wai, R. J.; Lin, C. M.; Hsu, C. F. (2004). Adaptive fuzzy sliding-mode control for electrical servo drive, Fuzzy Sets and Systems, 143(2), 295-310, 2004. https://doi.org/10.1016/S0165-0114(03)00199-4
[78] Wai, R. J.; Tu, C. H. (2007). Design of total sliding-mode-based genetic algorithm control for hybrid resonant-driven linear piezoelectric ceramic motor, IEEE Transactions on Power Electronics, 22(2), 563-575, 2007. https://doi.org/10.1109/TPEL.2006.889988
[79] Wang, C. H.; Hung, K. N. (2013). Intelligent adaptive law for missile guidance using fuzzy neural networks, International Journal of Fuzzy Systems, 15(2), 182-191, 2013.
[80] Wang, Z. X.; Wang, G. J.; Guo, R. W. (2018). Design for vegetable waste fermentation control systems based on semi-tensor product fuzzy controller, Advances in Mechanical Engineering, 10(8), 1-11, 2018. https://doi.org/10.1177/1687814018793551
[81] Wu, B.; Cheng, T. T; Yip, T. L.; Wang, Y. (2020). Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes, Ocean Engineering, 197, 2020. DOI: 10.1016/j.oceaneng.2019.106909. https://doi.org/10.1016/j.oceaneng.2019.106909
[82] Wu, D. R.; Lin, C. T.; Huang, J.; Zeng, Z. G. (2020). On the functional equivalence of TSK fuzzy systems to neural networks, mixture of experts, CART, and stacking ensemble regression, IEEE Transactions on Fuzzy Systems, 28(10), 2570-2580, 2020. https://doi.org/10.1109/TFUZZ.2019.2941697
[83] Wu, L. B.; Park, J. H. (2020). Adaptive fault-tolerant control of uncertain switched nonaffine nonlinear systems with actuator faults and time delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(9), 3470-3480, 2020. https://doi.org/10.1109/TSMC.2019.2894750
[84] Xibilia, M. G.; Latino, M.; Marinkovic, Z.; Atanaskovic, A.; Donato, N. (2020). Soft sensors based on deep neural networks for applications in security and safety, IEEE Transactions on Instrumentation and Measurement, 69(10), 7869-7876, 2020. https://doi.org/10.1109/TIM.2020.2984465
[85] Xie, S. W.; Xie, Y. F.; Li, F. B.; Yang, C. H.; Gui, W. H. (2020). Optimal setting and control for iron removal process based on adaptive neural network soft-sensor, IEEE Transactions on Systems, Man, and Cybernetics, 50(7), 2408-2420, 2020. https://doi.org/10.1109/TSMC.2018.2815580
[86] Xing, S. Y.; Ju, J. G.; Xing, J. S. (2019). Research on hot-rolling steel products quality control based on BP neural network inverse model, Neural Computing and Applications, 31(5), 1577-1584, 2019. https://doi.org/10.1007/s00521-018-3547-5
[87] Xu, R.; Bai, H. (2011). Research of AC adjusting speed system based on DTC and neural network supervision control, In: Proceedings of the 2nd International Conference on Mechanic Automation and Control Engineering, 4279-4281, Hohhot, China, July 15-17, 2011.
[88] Yang, J. M.; Cheng, K. W. E.; Wu, J.; Dong, P.; Wang, B. (2004). The study of the energy management system based-on fuzzy control for distributed hybrid wind-solar power system, In: Proceedings of the 1st International Conference on Power Electronics Systems and Applications, 113-117, Hong Kong, China, Nov. 09-11, 2004.
[89] Yang, S. C.; Li, M.; Xu, B.; Guo, B.; Zhu. C. G. (2010). Optimization of fuzzy controller based on genetic algorithm, In: Proceedings of the 2010 International Conference on Intelligent System Design and Engineering Application, 2, 21-28, Changsha, China, Oct. 13-14, 2010. https://doi.org/10.1109/ISDEA.2010.159
[90] Yuan, M.; Wu, Y. T.; Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network, In: Proceedings of the 2016 IEEE International Conference on Aircraft Utility Systems, 135-140, Beijing, China, Oct. 10-12, 2016. https://doi.org/10.1109/AUS.2016.7748035
[91] Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8(3), 338-353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X
[92] Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics, 3(1), 28-44, 1973. https://doi.org/10.1109/TSMC.1973.5408575
[93] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I, Information Sciences, 8(3), 199-249, 1975. https://doi.org/10.1016/0020-0255(75)90036-5
[94] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-II, Information Sciences, 8(4), 301-357, 1975. https://doi.org/10.1016/0020-0255(75)90046-8
[95] Zadeh, L. A. (1994). Soft computing and fuzzy logic, IEEE Software, 11(6), 48-56, 1994. https://doi.org/10.1109/52.329401
[96] Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178(13), 2751-2779, 2008. https://doi.org/10.1016/j.ins.2008.02.012
[97] Zavadskas, E. K.; Bausys, R.; Lescauskiene, I.; Omran, J. (2020). M-generalised q-neutrosophic MULTIMOORA for decision making, Studies in Informatics and Control, 29(4), 389-398, 2020. https://doi.org/10.24846/v29i4y202001
[98] Zavadskas, E. K.; Stevic, Ž.; Turskis, Z.; Tomaševic, M. (2019). A novel extended EDAS in Minkowski space (EDAS-M) method for evaluating autonomous vehicles, Studies in Informatics and Control, 28(3), 255-264, 2019. https://doi.org/10.24846/v28i3y201902
[99] Zhang, G.; Gong, X. S.; Chen, X. R. (2017). PID control algorithm based on genetic algorithm and its application in electric cylinder control, International Journal of Information Technology and Web Engineering, 12(3), 51-61, 2017. https://doi.org/10.4018/IJITWE.2017070105
[100] Zhang, J. Y.; Li, Y. D. (2006). Application of genetic algorithm in optimization of fuzzy control rules, In: Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, 529-532, Jinan, China, OCT. 16-18, 2006.
[101] Zhang, X. Z.; Wang, Y. N. (2015). Design of robust fuzzy sliding-mode controller for a class of uncertain Takagi-Sugeno nonlinear systems, International Journal of Computers Communications & Control, 10(1), 136-146, 2015. https://doi.org/10.15837/ijccc.2015.1.1572
[102] Zhang, Y. C.; Zhao, W. Z.; Chen, J. L. (2012). The research and application of the fuzzy neural network control based on genetic algorithm, Advanced Materials Research, 403, 191-195, 2012. https://doi.org/10.4028/www.scientific.net/AMR.403-408.191
[103] Zhao, Y. N.; Collins, E. G. (2003). Fuzzy PI control design for an industrial weigh belt feeder, IEEE Transactions on Fuzzy Systems, 11(3), 311-319, 2003. https://doi.org/10.1109/TFUZZ.2003.812686
[104] Zhong, S. S.; Fu, S.; Lin, L. (2019). A novel gas turbine fault diagnosis method based on transfer learning with CNN, Measurement, 137, 435-453, 2019. https://doi.org/10.1016/j.measurement.2019.01.022
[105] Ziegler, J. G.; Nichols, N. B. (1993). Optimum settings for automatic controllers, Journal of Dynamic Systems, Measurement, and Control, 115(2B), 220-222, 1993. https://doi.org/10.1115/1.2899060
Additional Files
Published
2021-01-17
Issue
Section
Articles
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
