Abstract
Great versatility can be achieved with the use of wireless networked control, but communication errors and channel congestion must be taken into account. Random delays and packet dropouts can degrade control performance. This paper describes a controller design strategy to reduce the effects of sensor data loss in control feedback loops. A fuzzy controller is compared to a classic PID controller to verify reference tracking performance in the presence of packet dropouts. Losses are treated by two common compensation methods: zero input and hold input. To avoid adjustment bias for any of the controllers, tuning is performed by a genetic algorithm that minimizes the difference between actual and desired output. The obtained fuzzy controller presents better tolerance to higher losses, which is observed in the reduction of response error peaks. Hard clipping the classic PID output also improves performance.
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References
Willig A, Matheus K, Wolisz A (2005) Wireless technology in industrial automation. Proc IEEE 93(6):1130–1151
Yan H, Yan S, Zhang H, Zhao X (2014) An overview of networked control of complex dynamic systems. Math Problems Eng 2014:1–10
Schenato L (2009) To zero or to hold control inputs with lossy links? IEEE Trans Autom Control 54(5):1093–1099
Zhang XM, Han QL, Yu X (2016) Survey on recent advances in networked control systems. IEEE Trans Industr Inf 12(5):1740–1752
Georges JP, Jämsä-Jounela SL, Aubrun C, Vatanski N, Rondeau E (2008) Networked control with delay measurement and estimation. Control Eng Practice 17(2):231–244
Morawski M, Ignaciuk P (2016) Reducing impact of network induced perturbations in remote control systems. Control Eng Practice 55:127–138
Lin CL, Chen CH, Huang HC (2008) Stabilizing control of networks with uncertain time varying communication delays. Control Eng Practice 16(1):56–66
Liu GP (2010) Predictive controller design of networked systems with communication delays and data loss. IEEE Trans Circuits Syst II Express Briefs 57(6):481–485
Yu JT, Fu LC (2015) An optimal compensation framework for linear quadratic gaussian control over lossy networks. IEEE Trans Autom Control 60(10):2692–2697
Björkbom M, Nethi S, Eriksson LM, Jäntti R (2011) Wireless control system design and co-simulation. Control Eng Practice 19(9):1075–1086
Khan N, Khattak MI, Khan MN, Khan F, Khan LU, Salam SA, Gu D (2013) Implementation of linear prediction techniques in state estimation. In: 10th International Bhurban conference on applied sciences & technology (IBCAST), pp 77–83
Shi Y, Fang H (2010) Kalman filter-based identification for systems with randomly missing measurements in a network environment. Int J Control 83(3):538–551
Khan N, Gu DW (2009) State estimation in the case of loss of observations. In: 2009 ICCAS-Sice, pp 1840–1845
Hajebi P, Almodarresi SMT (2013) Online adaptive fuzzy logic controller using genetic algorithm and neural network for networked control systems. Int Conf Adv Commun Technol ICACT 1(3):88–98
Blevins T, Nixon M, Wojsznis W (2014) PID control using wireless measurements. In: Proceedings of the American control conference, pp 790–795
Das S, Pan I, Das S (2013) Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time. ISA Trans 52(4):550–566
Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhang D, Shi P, Wang QG, Yu L (2017) Analysis and synthesis of networked control systems: a survey of recent advances and challenges. ISA Trans 66:376–392
Qiu J, Gao H, Ding SX (2016) Recent advances on fuzzy-model-based nonlinear networked control systems: a survey. IEEE Trans Industr Electron 63(2):1207–1217
Wang Y, Yang X, Yan H (2019) Reliable fuzzy tracking control of near-space hypersonic vehicle using aperiodic measurement information. IEEE Trans Ind Electron 11(1):1–1
Bai Y, Zhuang H, Wang D (2007) Advanced fuzzy logic technologies in industrial applications. Springer, Berlin
Michels K, Klawonn F, Kruse R, Nürnberger A (2006) Fuzzy control: fundamentals, stability and design of fuzzy controllers
Huang Y, Yasunobu S (2000) A general practical design method for fuzzy PID control from conventional PID control. IEEE Int Conf Fuzzy Syst 2:969–972
Cai S, Becherif M, Wack M, Ayad MY, Kebairi A (2011) Design of a wireless controller for an automotive actuator based on PID-fuzzy logic. In: Proceedings of the IEEE international conference on industrial technology, pp 53–58
Tian X, Wang X, Cheng Y (2007) A self-tuning fuzzy controller for networked control system. J Comput Sci 7(1):97–102
Chen TH (2015) H-infinity fuzzy control for a class of networked control system. Appl Math Inf Sci 9(1):133–139
Khanesar MA, Oniz Y, Kaynak O, Gao H (2016) Direct model reference adaptive fuzzy control of networked SISO nonlinear systems. IEEE/ASME Trans Mechatron 21(1):205–213
Mahmoud MS, Almutairi NB (2016) Feedback fuzzy control for quantized networked systems with random delays. Appl Math Comput 290:80–97
Zhang M, Shi P, Ma L, Cai J, Su H (2019) Network-based fuzzy control for nonlinear Markov jump systems subject to quantization and dropout compensation. Fuzzy Sets Syst 371:96–109
Singh J, Pesch D (2011) Stability of wireless networked control system using energy-efficient fuzzy based adaptive error control. In: Proceedings of 2011 4th joint IFIP wireless and mobile networking conference, WMNC 2011, pp 1–8
Gao H, Zhao Y, Chen T (2009) H-infinity fuzzy control of nonlinear systems under unreliable communication links. IEEE Trans Fuzzy Syst 17(2):265–278
Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan MI, Sastry SS (2004) Kalman filtering with intermittent observations. IEEE Trans Autom Control 49(9):1453–1464
Schenato L (2008) Kalman Filtering for networked control systems with random delay and packet loss. IEEE Trans Autom Control Kyoto 53:1311–1317
Messner B, Tilbury D (2011) Control Tutorials for MATLAB and Simulink. http://ctms.engin.umich.edu/CTMS/index.php?aux=Home
Siena W, Leandro GV, Ribeiro EP (2016) Comparação entre modelos de perdas de pacotes sobre um WNCS com protocolo IEEE 802.15.4. In: Congresso Brasileiro de Automtomática
Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller-part I. IEEE Trans Syst Man Cybern 20(2):419–435
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Tefili, D., Aoki, A.R., Leandro, G.V. et al. Performance improvement for networked control system with nonlinear control action. Int. J. Dynam. Control 9, 1100–1106 (2021). https://doi.org/10.1007/s40435-020-00745-5
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DOI: https://doi.org/10.1007/s40435-020-00745-5