A non-uniform predictor-observer for a networked control system

The final publication is available at Springer via http://dx.doi.org/10.1007/s12555-011-0621-5This paper presents a Non-Uniform Predictor-Observer (NUPO) based control approach in order to deal with two of the main problems related to Networked Control Systems (NCS) or Sensor Networks (SN): time-varying delays and packet loss. In addition, if these delays are longer than the sampling period, the packet disordering phenomenon can appear. Due to these issues, a (scarce) nonuniform, delayed measurement signal could be received by the controller. But including the NUPO proposal in the control system, the delay will be compensated by the prediction stage, and the nonavailable data will be reconstructed by the observer stage. So, a delay-free, uniformly sampled controller design can be adopted. To ensure stability, the predictor must satisfy a feasibility problem based on a time-varying delay-dependent condition expressed in terms of Linear Matrix Inequalities (LMI). Some aspects like the relation between network delay and robustness/performance trade-off are empirically studied. A simulation example shows the benefits (robustness and control performance improvement) of the NUPO approach by comparison to another similar proposal. © ICROS, KIEE and Springer 2011.This work was supported by the Spanish Ministerio de Ciencia y Tecnologia Projects DPI2008-06737-C02-01 and DPI2009-14744-C03-03, by Generalitat Valenciana Project GV/2010/018, by Universidad Politecnica de Valencia Project PAID06-08.Cuenca Lacruz, ÁM.; García Gil, PJ.; Albertos Pérez, P.; Salt Llobregat, JJ. (2011). A non-uniform predictor-observer for a networked control system. International Journal of Control, Automation and Systems. 9(6):1194-1202. doi:10.1007/s12555-011-0621-5S1194120296K. Ogata, Discrete-time Control Systems, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1987.Y. Tipsuwan and M. Chow, “Control methodologies in networked control systems,” Control Eng. Practice, vol. 11, no. 10, pp. 1099–1111, 2003.T. Jia, Y. Niu, and X. Wang, “H ∞ control for networked systems with data packet dropout,” Int. J. Control, Autom., and Syst., vol. 8, no. 2, pp. 198–203, 2010.Y. Wang and G. Yang, “Robust H ∞ model reference tracking control for networked control systems with communication constraints,” Int. J. Control, Autom., and Syst., vol. 7, no. 6, pp. 992–1000, 2009.H. Gao and T. Chen, “Network-based H ∞ output tracking control,” IEEE Trans. Autom. Control, vol. 53, no. 3, pp. 655–667, 2008.H. Karimi, “Robust H ∞ filter design for uncertain linear systems over network with network-induced delays and output quantization,” Modeling, Identification and Control, vol. 30, no. 1, pp. 27–37, 2009.H. R. Karimi, “Delay-range-dependent linear matrix inequality approach to quantized H ∞ control of linear systems with network-induced delays and norm-bounded uncertainties,” Proc. of the Inst. of Mech. Eng., Part I: J. of Syst. and Control Eng., vol. 224, no. 6, pp. 689–700, 2010.K. Lee, S. Lee, and M. Lee, “Remote fuzzy logic control of networked control system via Profibus-DP,” IEEE Trans. Ind. Electron., vol. 50, no. 4, pp. 784–792, 2003.Y. Tipsuwan and M.-Y. Chow, “Gain scheduler middleware: a methodology to enable existing controllers for networked control and teleoperationpart I: networked Control,” IEEE Trans. on Industrial Electronics, vol. 51, no. 6, pp. 1218–1227, December 2004.A. Sala, A. Cuenca, and J. Salt, “A retunable PID multi-rate controller for a networked control system,” Inform. Sci., vol. 179, no. 14, pp. 2390–2402, June 2009.A. Cuenca, J. Salt, V. Casanova, and R. Piza, “An approach based on an adaptive multi-rate Smith predictor and gain scheduling for a networked control system: implementation over Profibus-DP,” Int. J. Control, Autom., and Syst., vol. 8, no. 2, pp. 473–481, April 2010.A. Cuenca, J. Salt, A. Sala, and R. Piza, “A delay-dependent dual-rate PID controller over an Ethernet network,” IEEE Trans. Ind. Informat., vol. 7, no. 1, pp. 18–29, Feb. 2011.Y. Tian and D. Levy, “Compensation for control packet dropout in networked control systems,” Inform. Sci., vol. 178, no. 5, pp. 1263–1278, 2008.Y. Zhao, G. Liu, and D. Rees, “Modeling and stabilization of continuous-time packet-based networked control systems.” IEEE Trans. Syst., Man, Cybern. B, vol. 39, no. 6, pp. 1646–1652, Dec. 2009.X. Zhao, S. Fei, and C. Sun, “Impulsive controller design for singular networked control systems with packet dropouts,” Int. J. Control, Autom., and Syst., vol. 7, no. 6, pp. 1020–1025, 2009.H. Gao and T. Chen, “H ∞ estimation for uncertain systems with limited communication capacity,” IEEE Trans. Autom. Control, vol. 52, no. 11, pp. 2070–2084, 2007.S. Oh, L. Schenato, P. Chen, and S. Sastry, “Tracking and coordination of multiple agents using sensor networks: System design, algorithms and experiments,” Proc. of the IEEE, vol. 95, no. 1, pp. 234–254, 2007.M. Moayedi, Y. Foo, and Y. Soh, “Optimal and suboptimal minimum-variance filtering in networked systems with mixed uncertainties of random sensor delays, packet dropouts and missing measurements,” Int. J. Control, Autom., and Syst., vol. 8, no. 6, pp. 1179–1188, 2010.W. Zhang, M. Branicky, and S. Phillips, “Stability of networked control systems,” IEEE Control Syst. Mag., vol. 21, no. 1, pp. 84–99, 2001.J. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent results in networked control systems,” Proc. of the IEEE, vol. 95, no. 1, pp. 138–162, 2007.J. Baillieul and P. Antsaklis, “Control and communication challenges in networked real-time systems,” Proc. of the IEEE, vol. 95, no. 1, pp. 9–28, 2007.P. Garcia, P. Castillo, R. Lozano, and P. Albertos, “Robustness with respect to delay uncertainties of a predictor-observer based discrete-time controller,” Proc. of the 45th IEEE Conf. on Decision and Control, pp. 199–204, 2006.C. Lien, “Robust observer-based control of systems with state perturbations via LMI approach,” IEEE Trans. Autom. Control, vol. 49, no. 8, pp. 1365–1370, 2004.A. Sala, “Computer control under time-varying sampling period: an LMI gridding approach,” Automatica, vol. 41, no. 12, pp. 2077–2082, Dec. 2005.J. Li, Q. Zhang, Y. Wang, and M. Cai, “H ∞ control of networked control systems with packet disordering,” IET Control Theory Appl., vol. 3, no. 11, pp. 1463–1475, March 2009.Y. Zhao, G. Liu, and D. Rees, “Improved predictive control approach to networked control systems,” IET Control Theory Appl., vol. 2, no. 8, pp. 675–681, Aug. 2008.K. Astrom, “Event based control,” Analysis and Design of Nonlinear Control Systems, pp. 127–147, 2007.A. Cuenca, P. García, K. Arzén, and P. Albertos, “A predictor-observer for a networked control system with time-varying delays and non-uniform sampling,” Proc. Eur. Control Conf., pp. 946–951, 2009.J. Xiong and J. Lam, “Stabilization of linear systems over networks with bounded packet loss,” Automatica, vol. 43, no. 1, pp. 80–87, 2007.H. Song, L. Yu, and A. Liu, “H ∞ filtering for network-based systems with communication constraints and packet dropouts,” Proc. of the 7th Asian Control Conf., pp. 220–225, 2009.P. Garcia, A. Gonzalez, P. Castillo, R. Lozano, and P. Albertos, “Robustness of a discrete-time predictor-based controller for time-varying measurement delay,” Proc. of the 9th IFAC Workshop on Time Delay Systems, 2010.J. Sturm, “Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones,” Optimization methods and software, vol. 11, no. 1, pp. 625–653, 1999.T. Henningsson and K. Astrom, “Log-concave observers,” Proc. of the 17th Int. Symp. on Mathematical Theory of Networks and Systems, pp. 2163–2170, 2006.D. Davison and E. Hwang, “Automating radiotherapy cancer treatment: use of multirate observer-based control,” Proc. of American Control Conf., vol. 2, pp. 1194–1199, 2003

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Time-and event-driven communication process for networked control systems: A survey

Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Networked PID control design : a pseudo-probabilistic robust approach

Networked Control Systems (NCS) are feedback/feed-forward control systems where control components (sensors, actuators and controllers) are distributed across a common communication network. In NCS, there exist network-induced random delays in each channel. This paper proposes a method to compensate the effects of these delays for the design and tuning of PID controllers. The control design is formulated as a constrained optimization problem and the controller stability and robustness criteria are incorporated as design constraints. The design is based on a polytopic description of the system using a Poisson pdf distribution of the delay. Simulation results are presented to demonstrate the performance of the proposed method

Fuzzy-model-based robust fault detection with stochastic mixed time-delays and successive packet dropouts

This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEThis paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi–Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.This work was supported in part by the National Natural Science Foundation of China under Grant 61028008, 60825303, 61004067, National 973 Project under Grant 2009CB320600, the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University), Ministry of Education, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the University of Hong Kong under Grant HKU/CRCG/200907176129 and the Alexander von Humboldt Foundation of Germany

Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.Comment: 6 pages, 12 figure

Multirate control with incomplete information over Profibus-DP network

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 2014, available online:http://www.tandfonline.com/10.1080/00207721.2013.844286When a process ¿eld bus-decentralized peripherals (Pro¿bus-DP) network is used in an industrial environment, a deterministic behaviour is usually claimed. However, due to some concerns such as bandwidth limitations, lack of synchronisation among different clocks and existence of time-varying delays, a more complex problem must be faced. This problem implies the transmission of irregular and, even, random sequences of incomplete information. The main consequence of this issue is the appearance of different sampling periods at different network devices. In this paper, this aspect is checked by means of a detailed Pro¿bus-DP timescale study. In addition, in order to deal with the different periods, a delay-dependent dual-rate proportional-integral-derivative control is introduced. Stability for the proposed control system is analysed in terms of linear matrix inequalitiesThe authors are grateful to the financial support of the Spanish Ministry of Economy and Competitivity [Research Grant TEC2012-31506].Salt Llobregat, JJ.; Casanova Calvo, V.; Cuenca Lacruz, ÁM.; Pizá Fernández, R. (2014). Multirate control with incomplete information over Profibus-DP network. International Journal of Systems Science. 45(7):1589-1605. https://doi.org/10.1080/00207721.2013.844286S15891605457Alves, M., & Tovar, E. (2007). Real-time communications over wired/wireless PROFIBUS networks supporting inter-cell mobility. Computer Networks, 51(11), 2994-3012. doi:10.1016/j.comnet.2007.01.001Boyd, S., El Ghaoui, L., Feron, E., & Balakrishnan, V. (1994). Linear Matrix Inequalities in System and Control Theory. doi:10.1137/1.9781611970777Bucher, R., & Balemi, S. (2006). Rapid controller prototyping with Matlab/Simulink and Linux. Control Engineering Practice, 14(2), 185-192. doi:10.1016/j.conengprac.2004.09.009Casanova, V., & Salt, J. (2003). Multirate control implementation for an integrated communication and control system. Control Engineering Practice, 11(11), 1335-1348. doi:10.1016/s0967-0661(02)00256-3Lee, J., Jung, W., Kang, I., Kim, Y., & Lee, G. (2004). Design of filter to reject motion artifact of pulse oximetry. Computer Standards & Interfaces, 26(3), 241-249. doi:10.1016/s0920-5489(03)00077-1Cuenca, Á., Pizá, R., Salt, J., & Sala, A. (2012). Linear Matrix Inequalities in Multirate Control over Networks. Mathematical Problems in Engineering, 2012, 1-22. doi:10.1155/2012/768212Cuenca, A., & Salt, J. (2012). RST controller design for a non-uniform multi-rate control system. Journal of Process Control, 22(10), 1865-1877. doi:10.1016/j.jprocont.2012.09.010Cuenca, Á., Salt, J., & Albertos, P. (2006). Implementation of algebraic controllers for non-conventional sampled-data systems. Real-Time Systems, 35(1), 59-89. doi:10.1007/s11241-006-9001-2Halevi, Y., & Ray, A. (1988). Integrated Communication and Control Systems: Part I—Analysis. Journal of Dynamic Systems, Measurement, and Control, 110(4), 367-373. doi:10.1115/1.3152698Khargonekar, P., Poolla, K., & Tannenbaum, A. (1985). Robust control of linear time-invariant plants using periodic compensation. IEEE Transactions on Automatic Control, 30(11), 1088-1096. doi:10.1109/tac.1985.1103841Lall, S., & Dullerud, G. (2001). An LMI solution to the robust synthesis problem for multi-rate sampled-data systems. Automatica, 37(12), 1909-1922. doi:10.1016/s0005-1098(01)00167-4Lee, I. W. C., & Dash, P. K. (2003). S-transform-based intelligent system for classification of power quality disturbance signals. IEEE Transactions on Industrial Electronics, 50(4), 800-805. doi:10.1109/tie.2003.814991Lee, C. K., Ron Hui, S. Y., & Henry Shu-Hung Chung. (2002). A 31-level cascade inverter for power applications. IEEE Transactions on Industrial Electronics, 49(3), 613-617. doi:10.1109/tie.2002.1005388Performance evaluation of control networks: Ethernet, ControlNet, and DeviceNet. (2001). IEEE Control Systems, 21(1), 66-83. doi:10.1109/37.898793Feng-Li Lian, Moyne, J., & Tilbury, D. (2002). Network design consideration for distributed control systems. IEEE Transactions on Control Systems Technology, 10(2), 297-307. doi:10.1109/87.987076Lin, J., Fei, S., & Gao, Z. (2013). Control discrete-time switched singular systems with state delays under asynchronous switching. International Journal of Systems Science, 44(6), 1089-1101. doi:10.1080/00207721.2011.652230Liou, L.-W., & Ray, A. (1991). A Stochastic Regulator for Integrated Communication and Control Systems: Part I—Formulation of Control Law. Journal of Dynamic Systems, Measurement, and Control, 113(4), 604-611. doi:10.1115/1.2896464Lorand, C., & Bauer, P. H. (2006). On Synchronization Errors in Networked Feedback Systems. IEEE Transactions on Circuits and Systems I: Regular Papers, 53(10), 2306-2317. doi:10.1109/tcsi.2006.882824Moayedi, M., Foo, Y. K., & Soh, Y. C. (2011). Filtering for networked control systems with single/multiple measurement packets subject to multiple-step measurement delays and multiple packet dropouts. International Journal of Systems Science, 42(3), 335-348. doi:10.1080/00207720903513335Peñarrocha, I., Sanchis, R., & Romero, J. A. (2012). State estimator for multisensor systems with irregular sampling and time-varying delays. International Journal of Systems Science, 43(8), 1441-1453. doi:10.1080/00207721.2011.625482Piza, R., Salt, J., Sala, A., & Cuenca, A. (2014). Hierarchical Triple-Maglev Dual-Rate Control Over a Profibus-DP Network. IEEE Transactions on Control Systems Technology, 22(1), 1-12. doi:10.1109/tcst.2012.2222883Ray, A. (1989). Introduction to networking for integrated control systems. IEEE Control Systems Magazine, 9(1), 76-79. doi:10.1109/37.16755Ray, A., & Halevi, Y. (1988). Integrated Communication and Control Systems: Part II—Design Considerations. Journal of Dynamic Systems, Measurement, and Control, 110(4), 374-381. doi:10.1115/1.3152699Sala, A., Cuenca, Á., & Salt, J. (2009). A retunable PID multi-rate controller for a networked control system. Information Sciences, 179(14), 2390-2402. doi:10.1016/j.ins.2009.02.017Salt, J., & Albertos, P. (2005). Model-based multirate controllers design. IEEE Transactions on Control Systems Technology, 13(6), 988-997. doi:10.1109/tcst.2005.857410Salt, J., Sala, A., & Albertos, P. (2011). A Transfer-Function Approach to Dual-Rate Controller Design for Unstable and Non-Minimum-Phase Plants. IEEE Transactions on Control Systems Technology, 19(5), 1186-1194. doi:10.1109/tcst.2010.2076386Schickhuber, G., & McCarthy, O. (1997). Distributed Fieldbus and control network systems. Computing & Control Engineering Journal, 8(1), 21-32. doi:10.1049/cce:19970106Sturm, J. F. (1999). Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones. Optimization Methods and Software, 11(1-4), 625-653. doi:10.1080/10556789908805766Tipsuwan, Y., & Chow, M.-Y. (2003). Control methodologies in networked control systems. Control Engineering Practice, 11(10), 1099-1111. doi:10.1016/s0967-0661(03)00036-4Xie, L. B., Ozkul, S., Sawant, M., Shieh, L. S., Tsai, J. S. H., & Tsai, C. H. (2013). Multi-rate digital redesign of cascaded and dynamic output feedback systems. International Journal of Systems Science, 45(8), 1757-1768. doi:10.1080/00207721.2012.752546Yang, T. C. (2006). Networked control system: a brief survey. IEE Proceedings - Control Theory and Applications, 153(4), 403-412. doi:10.1049/ip-cta:2005017