Model-Based Fuzzy Control Results for Networked Control Systems
DOI:
https://doi.org/10.31181/rme200101010pKeywords:
fuzzy control; networked control systemAbstract
This paper discusses aspects concerning the design of model-based fuzzy controllers for Networked Control Systems (NCSs). The stability analysis is related to the characteristic equation of these control systems, where the variable time delays create numerical problems. These numerical problems are first briefly investigated, along with signal processing aspects concerning NCSs. The popular Hilbert-Huang transform is applied to smooth the signals and also the variable time delay, also called latency, due to the communication in the network. The design of Takagi-Sugeno-Kang Proportional-Integral-fuzzy controllers dedicated to temperature control applications is next carried out; the stability of fuzzy NCSs is guaranteed by computing the controller tuning parameters as solutions to linear matrix inequalities. Experimental results for a laboratory equipment that models a first-order plus time delay process are included to validate the theoretical findings.
References
Albu, A., Precup, R.-E., Teban, T.-A. (2019). Results and challenges of artificial neural networks used for decision-making in medical applications, Facta Universitatis, Series: Mechanical Engineering, 17(4), 285-308.
Alvarez Gil, R. P., Johanyák, Z. C., Kovács, T. (2018). Surrogate model based optimization of traffic lights cycles and green period ratios using microscopic simulation and fuzzy rule interpolation, International Journal of Artificial Intelligence, 16(1), 20–40.
Amira, A. (2002). Controlled air stream and temperature control plant LTR 701, Operating manual, Amira GmbH, Duisburg, Germany.
Andoga, R., Főző, L., Judičák, J., Bréda, R., Szabo, S., Rozenberg, R., Džunda, M. (2018). Intelligent situational control of small turbojet engines, International Journal of Aerospace Engineering, 8328792, 1-16.
Baranyi, P. (2004). TP model transformation as a way to LMI-based controller design, IEEE Transactions on Industrial Electronics, 51(2), 387-400.
Bojan-Dragos, C.-A., Hedrea, E.-L., Precup, R.-E., Szedlak-Stinean, A.-I., Roman, R.-C. (2019). MIMO fuzzy control solutions for the level control of vertical two tank systems, Proceedings of 16th International Conference on Informatics in Control, Automation and Robotics, Prague, Czech Republic, 1, 810-817.
Breda, D., Maset, S., Vermiglio, R. (2007). Pseudospectral approximation of eigenvalues of derivative operators with non-local boundary conditions, Applied Numerical Mathematics, 56(3-4), 318-331.
Bueler, E. (2007). Error bounds for approximate eigenvalues of periodic-coefficient linear delay differential equations, SIAM Journal of Numerical Analysis, 45(6), 2510-2536.
Castillo, O., Melin, P. (20014). A review on interval type-2 fuzzy logic applications in intelligent control, Information Sciences, 279, 615-631.
Castillo, O., Melin, P. (2012). A review on the design and optimization of interval type-2 fuzzy controllers, Applied Soft Computing, 12(4), 1267-1278.
Chae, S., Nguang, S. K. (2014). SOS based robust H fuzzy dynamic output feedback control of nonlinear networked control systems, IEEE Transactions on Cybernetics, 44(7), 1204-1213.
Du, D. (2012). Reliable H control for Takagi-Sugeno fuzzy systems with intermittent measurements, Nonlinear Analysis: Hybrid Systems, 6(4), 930-941.
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 and Control, 12(6), 748-789.
Gerov, R., Jovanović, Z. (2019a). Parameter estimation method for the unstable time delay process, Acta Polytechnica Hungarica, 16(3), 101-120.
Gerov, R., Jovanović, Z. (2019b). Tuning PD and PID controllers via the Lambert W function for double integrator plus dead time processes, Facta Universitatis, Series: Automatic Control and Robotics, 18(1), 1-18.
Gu, Y., Wang, H. O., Tanaka, K., Bushnell, L. G. (2001). Fuzzy control of nonlinear time-delay systems: stability and design issues, Proceedings of 2001 American Control Conference, Arlington, VA, USA, 6, 4771-4776.
Guechi, E. H., Lauber, J., Dambrine, M., Klančar, G., Blažič, S. (2010). PDC control design for non-holonomic wheeled mobile robots with delayed outputs, Journal of Intelligent and Robotic Systems, 60(3-4), 395-414.
Guerra, T. M., Sala, A., Tanaka, K. (2015). Fuzzy control turns 50: 10 years later, Fuzzy Sets and Systems, 281, 168-182.
Gupta, R. A., Chow, M.-Y. (2010). Networked control system: Overview and research trends, IEEE Transactions on Industrial Electronics, 57(7), 2527–2535.
Haidegger, T., Kovács, L., Preitl, S., Precup, R.-E., Benyó, B., Benyó, Z. (2011). Controller design solutions for long distance telesurgical applications, International Journal of Artificial Intelligence, 6, 48-71.
Hamdy, M., Abd-Elhaleem, S., Fkirin, M. A. (2017a). Time-varying delay compensation for a class of nonlinear control systems over network via H∞ adaptive fuzzy controller, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8) 2114-2124.
Hamdy, M., Abd-Elhaleem, S., Fkirin, M. A. (2017b). Design of adaptive fuzzy control for a class of networked nonlinear systems, Journal of Dynamic Systems, Measurement, and Control, 139(3), 031008, 1-9.
Hamdy, M., Abd-Elhaleem, S., Fkirin, M. A. (2018). Adaptive fuzzy predictive controller for a class of networked nonlinear systems with time-varying delay, IEEE Transactions on Fuzzy Systems, 26(4), 2135-2144.
Hedrea, E.-L., Precup, R.-E., Bojan-Dragos, C.-A., Petriu E. M., Roman, R.-C. (2019). Tensor product-based model transformation and sliding mode control of electromagnetic actuated clutch system, Proceedings of 2019 IEEE International Conference on Systems, Man, and Cybernetics, Bari, Italy, 1418-1423.
Hedrea, E.-L., Precup, R.-E., Bojan-Dragos, C.-A., Roman, R.-C., Tanasoiu, O., Marinescu, M. (2018). Cascade control solutions for maglev systems, Proceedings of 2018 22nd International Conference on System Theory, Control and Computing, Sinaia, Romania, 20-26.
Hedrea, L.-E., Bojan-Dragos, C.-A., Precup, R.-E., Roman, R.-C., Petriu, E. M., Hedrea, C. (2017). Tensor product-based model transformation for position control of magnetic levitation systems, Proceedings of 2017 IEEE International Symposium on Industrial Electronics, Edinburgh, UK, 1141-1146.
Huang, N. E., Shen, Z., Long, S. R. (1998). The empirical ODE decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903-995.
Huang, N. E., Shen, Z., Long, S. R. (1999). A new view of nonlinear water waves: the Hilbert spectrum, Annual Review of Fluid Mechanics, 31, 417-457.
Huang, N. E., Wu, M. C., Long, S. R., Shen, S. P., Qu, W., Gloersen, P., Fan, K. L. (2003). A confidence limit for the empirical mode decomposition and the Hilbert spectral analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 459, 2317-2345.
Jarlebring, E. (2008). The spectrum of delay-differential equations: numerical methods, stability and perturbation, PhD thesis, Inst. Comp. Math, Tech. Univ. Braunschweig, Braunschweig, Germany.
Jarlebring, E., Damm, T. (2007). The Lambert W function and the spectrum of some multidimensional time-delay systems, Automatica, 43(12), 2124-2128.
Kim, W. S., Hannaford, B., Bejczy, A. K. (1992). Force-reflection and shared compliant control in operating telemanipulators with time-delay, IEEE Transactions on Robotics and Automation, 8(2), 176-185.
Korondi, P., Hashimoto, H., Utkin, V. (1995). Discrete sliding mode control of two mass system, Proceedings of 1995 IEEE International Symposium on Industrial Electronics, Athens, Greece, 338-343.
Kovács, G., Precup, R.-E., Preitl, S., Gyurkó, Z. (2004). Time delay compensation for networked control systems, Proceedings of 3rd International INTER-ACADEMIA Conference, Budapest, Hungary, 1, 207-214.
La Fe-Perdomo, I., Beruvides, G., Quiza, R., Haber, R., Rivas, M. (2019). Automatic selection of optimal parameters based on simple soft-computing methods: A case study of micromilling processes, IEEE Transactions on Industrial Informatics, 15(2), 800-811.
Lam, H. K. (2018). A review on stability analysis of continuous-time fuzzy-model-based control systems: From membership-function-independent to membership-function-dependent analysis, Engineering Applications of Artificial Intelligence, 67, 390-408.
Lee, S., Lee, H. S. (1993). Modeling, design, and evaluation of advanced teleoperator control systems with short time delay, IEEE Transactions on Robotics and Automation, 9(5), 607-623.
Li, H., Wu, C., Feng, Z. (2015). Fuzzy dynamic output-feedback control of non-linear networked discrete-time system with missing measurements, IET Control Theory and Applications, 9(3), 327-335.
Li, H.-Y., Chen, Z., Wu, L.-G., Lam, H.-K. (2017). Event-triggered control for nonlinear systems under unreliable communication links, IEEE Transactions on Fuzzy Systems, 25(4), 813-824.
Luo, R. C., Chen, T. M., Yin, C.-C. (2000). Intelligent autonomous mobile robot control through the Internet, Proceedings of 2000 IEEE International Symposium on Industrial Electronics, Cholula, Puebla, Mexico, l, PL6-PL11.
Mahmoud, M. S., Xia, Y.-Q. (2019). Networked control systems: cloud control and secure control, Butterworth-Heinemann, Elsevier, Oxford, UK.
Mendez-Monroy, P. E., Sanchez Dominguez, I., Bassam, A., May Tzuc, O. (2018). Control-scheduling codesign for NCS based fuzzy systems, International Journal of Computers Communications and Control, 13(2), 251-267.
Pan, Y.-N., Yang, G.-H. (2017). Event-triggered fuzzy control for nonlinear networked control systems, Fuzzy Sets and Systems, 329, 91-107.
Peng, C., Li, F.-Q. (2018). A survey on recent advances in event-triggered communication and control, Information Sciences, 113-125.
Peng, C., Yang, T. C. (2010). Communication-delay-distribution-dependent networked control for a class of T-S fuzzy systems, IEEE Transactions on Fuzzy Systems, 18(2), 326-335.
Petrović, G., Mihajlović, J., Ćojbašić, Ž., Madić, M., Marinković, D. (2019). Comparison of three fuzzy MCDM methods for solving the supplier selection problem, Facta Universitatis, Series: Mechanical Engineering, 17(3), 455-469.
Pozna, C., Precup, R.-E. (2018). An approach to the design of nonlinear state-space control systems, Studies in Informatics and Control, 27(1), 5-14.
Pozna, C., Precup, R.-E., Tar, J. K., Škrjanc, I., Preitl, S. (2010). New results in modelling derived from Bayesian filtering, Knowledge-Based Systems, 23(2), 182-194.
Precup, R.-E., Angelov, P., Costa, B. S. J., Sayed-Mouchaweh, M. (2015). An overview on fault diagnosis and nature-inspired optimal control of industrial process applications, Computers in Industry, 74, 75-94.
Precup, R.-E., David, R.-C. (2019). Nature-inspired optimization algorithms for fuzzy controlled servo systems, Butterworth-Heinemann, Elsevier, Oxford, UK.
Precup, R.-E., David, R.-C., Petriu, E. M., Szedlak-Stinean, A.-I., Bojan-Dragos, C.-A. (2016). Grey wolf optimizer-based approach to the tuning of PI-fuzzy controllers with a reduced process parametric sensitivity, IFAC-Papers Online, 48, 55-60.
Precup, R.-E., Haidegger, T., Kovács, L. (2014). Stable hybrid fuzzy controller-based architecture for robotic telesurgery systems, International Journal of Computational Intelligence and Pattern Recognition, 1(1), 61-76.
Precup, R.-E., Hellendoorn, H. (2011). A survey on industrial applications of fuzzy control, Computers in Industry, 62(3), 213-226.
Precup, R.-E., Preitl, S. (2003). Development of fuzzy controllers with non-homogeneous dynamics for integral-type plants, Electrical Engineering, 85(3), 155-168.
Precup, R.-E., Preitl, S., Škrjanc, I., Ursache, I.-B., Clep, P. A., Dragos, C.-A. (2008). Signal processing issues in networked control systems, Scientific Bulletin of the Politehnica University of Timisoara, Transactions on Automatic Control and Computer Science, 53(67), 71-78.
Precup, R.-E., Tomescu, M.-L., Dragos, C.-A. (2014a). Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm, International Journal of General Systems, 43(5), 413-433.
Preitl, S., Precup, R.-E., Fodor, J., Takács, M. (2007). Hints in low cost solutions for networked control systems, Proceedings of 5th IEEE International Conference on Computational Cybernetics, Gammarth, Tunisia, 275-280.
Purcaru, C., Precup, R.-E., Iercan, D., Fedorovici, L.-O., David, R.-C., Dragan, F. (2013). Optimal robot path planning using gravitational search algorithm, International Journal of Artificial Intelligence, 10, S13, 1-20.
Qiu, J.-B., Gao, H.-J., Ding, S. X. (2016). Recent advances on fuzzy-model-based nonlinear networked control systems: A survey, IEEE Transactions on Industrial Electronics, 63(2), 1207-1217.
Roman, R.-C., Precup, R.-E., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I. (2019). Combined model-free adaptive control with fuzzy component by virtual reference feedback tuning for tower crane systems, Procedia Computer Science, 162, 267-274.
Roman, R.-C., Precup, R.-E., David, R.-C. (2018). Second order intelligent proportional-integral fuzzy control of twin rotor aerodynamic systems, Procedia Computer Science, 139, 372-380.
Roman, R.-C., Precup, R.-E., Petriu, E. M., Dragan, F. (2019a). Combination of data-driven active disturbance rejection and Takagi-Sugeno fuzzy control with experimental validation on tower crane systems, Energies, 12(8), 1548, 1-19.
Rotariu, C., Pasarica, A., Andruseac, G., Costin, H., Nemescu, D. (2014). Automatic analysis of the fetal heart rate variability and uterine contractions, Proceedings of 8th International Conference and Exposition on Electrical and Power Engineering, Iasi, Romania, 1-6.
Ruano, A. E., Ge, S. S., Guerra, T. M., Lewis, F. L., Principe, J. C., Colnarič, M. (2014). Computational intelligence in control, Annual Reviews in Control, 38(2), 233-242.
Sadeghi-Tehran, P., Cara, A. B., Angelov, P., Pomares, H., Rojas, U., Prieto, A. (2012). Self-evolving parameter-free rule-based controller, Proceedings of 2012 IEEE International Conference on Fuzzy Systems, Brisbane, QLD, Australia, 1-6.
Sheridan, T. B., Ferrell, W. R. (1963). Remote manipulative control with transmission delay, IEEE Transactions on Human Factors in Electronics, 4(1), 25-29.
Takács, Á., Kovács, L., Rudas, I. J., Precup, R.-E., Haidegger, T. (2015). Models for force control in telesurgical robot systems, Acta Polytechnica Hungarica, 12 (8), 95-114.
Tipsuwan, Y., Chow, M.-Y. (2003). Control methodologies in networked control systems, Control Engineering Practice, 11(10), 1099-1111.
Tong, S. W., Qian, D. W., Liu, G. P. (2014). Networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor, International Journal of Computers Communications and Control, 9(4), 471-481.
Vaščák, J., Hvizdoš, J., Puheim, M. (2016). Agent-based cloud computing systems for traffic management, Proceedings of 2016 International Conference on Intelligent Networking and Collaborative Systems, Ostrava, Czech Republic, 73-79.
Wang, T., Qiu, J.-B., Gao, H.-H., Wang, C.-H. (2017). Network-based fuzzy control for nonlinear industrial processes with predictive compensation strategy, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 2137-2147.
Yan, R., Gao, R. X. (2007). A tour of the Hilbert-Huang transform: an empirical tool for signal analysis, IEEE Instrumentation and Measurement Magazine, 10(5), 40-45.
Zhang, D., Shi, P., Wang, Q.-G., Yu, L. (2017). Analysis and synthesis of networked control systems: A survey of recent advances and challenges, ISA Transactions, 66, 376-392.
Zhao, Y., Gao, H., Chen, T. (2010). Fuzzy constrained predictive control of non-linear systems with packet dropouts, IET Control Theory and Applications, 4(9), 1665-1677.
