Implementation of Model Based Networked Predictive Control System

Networked control systems are made up of several computer nodes communicating over a communication channel, cooperating to control a plant. The stability of the plant depends on the end to end delay from sensor to the actuator. Although computational delays within the computer nodes can be made bounded, delays through the communication network are generally unpredictable. A method which aims to protect the stability of the plant under communication delays and data loss, Model Based Predictive Networked Control System (MBPNCS), has previously been proposed by the authors. This paper aims to demonstrate the implementation of this type of networked control system on a non-real-time communication network; Ethernet. In this paper, we first briefly describe the MBPNCS method, then discuss the implementation, detailing the properties of the operating system, communications and hardware, and later give the results on the performance of the Model Based Predictive Networked Control System implementation controlling a DC motor. This work was supported in part by the Scientific and Technological Re search Council of Turkey, project code 106E155

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. 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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. 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H

Compensation scheme-based H∞ control is investigated for networked control systems with packet disordering and packet loss. Since the existence of packet disordering and packet loss inevitably degrades the control performance of networked control systems, it is worth studying a control scheme to compensate for them, such that the control performance can be improved. Thus, a compensation control strategy is first proposed following this direction. Next, a mathematical model of networked control systems with Markovian property is constructed due to the signals executed by the plant subject to Markovian chain. Based on it, a sufficient condition for stochastic stability of networked control systems with uncertain parameters as well as compensation strategy is presented, and an adaptive controller is designed based on linear matrix inequality (LMI) technique. Finally, a numerical example and simulations are given to illustrate the effectiveness of the proposed method

A new model for time-delay systems with application to network based control

This paper proposes a new model for time-delay systems, which contains multiple successive delay components in the state and has important applications in remote control and network based control. New results on stability and H∞ performance are obtained for systems with two successive delay components. The second part of this paper applies the proposed new model to network based control, which has emerged as a topic of significant interest in the control community. A sampled-data networked control system with simultaneous consideration of network induced delays, data packet dropouts and measurement quantization is modelled as a time-delay system with two successive delay components in the state and, the problem of network based H∞ control is solved accordinglypublished_or_final_versio

Networked Supervisor Synthesis Against Lossy Channels with Bounded Network Delays as Non-Networked Synthesis

In this work, we study the problem of supervisory control of networked discrete event systems. We consider lossy communication channels with bounded network delays, for both the control channel and the observation channel. By a model transformation, we transform the networked supervisor synthesis problem into the classical (non-networked) supervisor synthesis problem (for non-deterministic plants), such that the existing supervisor synthesis tools can be used for synthesizing networked supervisors. In particular, we can use the (state-based) normality property for the synthesis of the supremal networked supervisors, whose existence is guaranteed by construction due to our consideration of command non-deterministic supervisors. The effectiveness of our approach is illustrated on a mini-guideway example that is adapted from the literature, for which the supremal networked supervisor has been synthesized in the synthesis tools SuSyNA and TCT.Comment: This paper is under review for Automatic