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Journal articles on the topic 'Networked Model Predictive Control'

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Author: Grafiati

Published: 11 March 2023

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1

Wu, Jing, Liqian Zhang, and Tongwen Chen. "Model predictive control for networked control systems." International Journal of Robust and Nonlinear Control 19, no. 9 (June 2009): 1016–35. http://dx.doi.org/10.1002/rnc.1361.

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2

Onat, Ahmet, A. Teoman Naskali, and Emrah Parlakay. "Model Based Predictive Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.

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3

Vaccarini, Massimo, Sauro Longhi, and M. Reza Katebi. "Unconstrained networked decentralized model predictive control." Journal of Process Control 19, no. 2 (February 2009): 328–39. http://dx.doi.org/10.1016/j.jprocont.2008.03.005.

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4

He, Fang, Xiao Li, and Qiang Wang. "Study of Predictive Control in Industrial Networked Control System." Advanced Materials Research 201-203 (February 2011): 2087–90. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2087.

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In order to improve the performance of networked control system which has the characteristic of uncertain time delay, predictive control is adopted. The model of networked control system is analyzed. Take into account uncertain network time delay caused by various factors and problem of model mismatch, predictive controller is designed and simulation model of system is built using software of Matlab. A method of reducing model mismatch is proposed. Study results show that predictive control can make networked control system with good performance.

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5

Ulusoy, Alphan, Ahmet Onat, and Ozgur Gurbuz. "Wireless Model Based Predictive Networked Control System." IFAC Proceedings Volumes 42, no. 3 (2009): 40–47. http://dx.doi.org/10.3182/20090520-3-kr-3006.00007.

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6

Ewald, Grzegorz, and Mietek A. Brdys. "Model Predictive Controller for Networked Control Systems." IFAC Proceedings Volumes 43, no. 8 (2010): 274–79. http://dx.doi.org/10.3182/20100712-3-fr-2020.00046.

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7

Chen, Zai Ping, and Xue Wang. "Research on Networked Control Systems Based on Adaptive Predictive Control." Applied Mechanics and Materials 441 (December 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.441.833.

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According to the random time-delay exist in sensor-controller channel and controller-actuator channel in networked control systems, an adaptive predictive control strategy was proposed. In this control strategy, an improved generalized predictive control algorithm is adopted to compensate the networked random time-delay. In addition, using the recursive least squares with a variable forgetting factor algorithm to indentify the model parameters of controlled object on-line, through the way, it could adjust the systems with unknown parameters adaptively. Simulation results show that the adaptive predictive control proposed could solve random time-delay of networked control systems effectively.

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8

Zhang, Yingwei, Xue Chen, and Renquan Lu. "Performance of Networked Control Systems." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/382934.

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Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs) with dual-network are designed by model predictive control method. The contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed.

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9

Kamal, Faria, and Badrul Chowdhury. "Model predictive control and optimization of networked microgrids." International Journal of Electrical Power & Energy Systems 138 (June 2022): 107804. http://dx.doi.org/10.1016/j.ijepes.2021.107804.

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10

Bernardini, D., M. C. F. Donkers, A. Bemporad, and W. P. M. H. Heemels. "A Model Predictive Control Approach for Stochastic Networked Control Systems." IFAC Proceedings Volumes 43, no. 19 (2010): 7–12. http://dx.doi.org/10.3182/20100913-2-fr-4014.00007.

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11

Xue, B., S. Li, N. Li, and Q. Zhu. "Robust model predictive control for networked control systems with quantisation." IET Control Theory & Applications 4, no. 12 (December 1, 2010): 2896–906. http://dx.doi.org/10.1049/iet-cta.2009.0496.

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12

Zhang, Langwen, Jingcheng Wang, Bohui Wang, and Yang Ge. "Robust distributed model predictive control for uncertain networked control systems." IET Control Theory & Applications 8, no. 17 (November 20, 2014): 1843–51. http://dx.doi.org/10.1049/iet-cta.2014.0311.

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13

Onat, Ahmet, Teoman Naskali, Emrah Parlakay, and Ozan Mutluer. "Control Over Imperfect Networks: Model-Based Predictive Networked Control Systems." IEEE Transactions on Industrial Electronics 58, no. 3 (March 2011): 905–13. http://dx.doi.org/10.1109/tie.2010.2051932.

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14

Yun-Bo Zhao, Guo-Ping Liu, and D. Rees. "Networked Predictive Control Systems Based on the Hammerstein Model." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 5 (May 2008): 469–73. http://dx.doi.org/10.1109/tcsii.2007.914423.

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15

Zhou, Xuping, Huaicheng Yan, Hao Zhang, and Chen Peng. "Model predictive control with feedback correction for optimal energy dispatch of a networked microgrid." Transactions of the Institute of Measurement and Control 41, no. 6 (May 3, 2017): 1540–52. http://dx.doi.org/10.1177/0142331217701807.

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The novel model predictive control with feedback correction designed in this paper aims to optimize the energy dispatch and minimize the operation costs of a microgrid, which contributes to the improvement of pollution emissions and economic growth. The microgrid communication is based on power line communication, thus more accurate prediction models of photovoltaic and wind power generations of a networked microgrid can be designed from weather forecast information transmitted by power line communication. The prediction model for micro gas turbines and the loads of a microgrid are also proposed for optimization of the model predictive control. The rolling optimization model is updated by the latest forecast information to get minimization costs and optimal energy dispatch. The feedback correction designs predictions of generation and loads prediction errors to give an adjustment of the prediction model. Then the energy optimization dispatch will be updated by the adjusted prediction, so the most optimal dispatch will be obtained. Finally, the data of a microgrid in the Zhejiang province is applied in simulation and the minimization costs are compared with ideal costs to verify the performance and effectiveness of the proposed model predictive control strategy.

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16

Tang, Bin, Guo-Ping Liu, Wei-Hua Gui, and Ya-Lin Wang. "State-Space Model Based Generalized Predictive Control for Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13006–11. http://dx.doi.org/10.3182/20080706-5-kr-1001.02199.

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17

Zhao, Y. B., G. P. Liu, and J. Kim. "Offline model predictive control-based gain scheduling for networked control systems." IET Control Theory & Applications 6, no. 16 (November 1, 2012): 2585–91. http://dx.doi.org/10.1049/iet-cta.2012.0504.

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18

ZHENG, Yi, and Shao-Yuan LI. "Networked Cooperative Distributed Model Predictive Control for Dynamic Coupling Systems." Acta Automatica Sinica 39, no. 11 (2013): 1778. http://dx.doi.org/10.3724/sp.j.1004.2013.01778.

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19

Su, Baili, Yanan Zhao, and Jinming Huang. "Networked Cooperative Distributed Model Predictive Control Based on State Observer." Applied Mathematics 07, no. 10 (2016): 1148–64. http://dx.doi.org/10.4236/am.2016.710103.

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20

Song, Hongbo. "Design and Stability Analysis of Uncertain Networked Predictive Control Systems with Multiple Forward Channels." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/403137.

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This paper is concerned with the design and stability of networked predictive control for uncertain systems with multiple forward channels. The delays and packet dropouts are distributed such that the classic networked predictive control (NPC) needs modifications to be implemented. An improved control signal selection scheme with distributed prediction length is proposed to increase the prediction accuracy and hence achieve better control performance. Moreover, stability analysis results are obtained for both constant and random cases. Interestingly, it is shown that the stability of the closed-loop NPC system is not related to the distributed delays when they are constant and the system model is accurate. Finally, a two-axis milling machine example is given to illustrate the effectiveness of the proposed method.

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21

KOBAYASHI, Koichi, and Kunihiko HIRAISHI. "Self-Triggered Model Predictive Control with Delay Compensation for Networked Control Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E96.A, no. 5 (2013): 861–68. http://dx.doi.org/10.1587/transfun.e96.a.861.

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22

Sakr, Ahmad, Ahmad M. El-Nagar, Mohammad El-Bardini, and Mohammed Sharaf. "Model Predictive Control Based on Modified Smith Predictor for Networked Control Systems." Menoufia Journal of Electronic Engineering Research 27, no. 2 (July 1, 2018): 237–58. http://dx.doi.org/10.21608/mjeer.2018.63249.

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23

Lu, Qing, Peng Shi, Hak-Keung Lam, and Yuxin Zhao. "Interval Type-2 Fuzzy Model Predictive Control of Nonlinear Networked Control Systems." IEEE Transactions on Fuzzy Systems 23, no. 6 (December 2015): 2317–28. http://dx.doi.org/10.1109/tfuzz.2015.2417975.

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24

Nguyen, Quang T., Vojtech Veselý, Alena Kozáková, and Pavel Pakshin. "NETWORKED ROBUST PREDICTIVE CONTROL SYSTEMS DESIGN WITH PACKET LOSS." Journal of Electrical Engineering 65, no. 1 (January 1, 2014): 3–11. http://dx.doi.org/10.2478/jee-2014-0001.

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Abstract The paper addresses problem of designing a robust output feedback model predictive control for uncertain linear systems over networks with packet-loss. The packet-loss process is arbitrary and bounded by the control horizon of model predictive control. Networked predictive control systems with packet loss are modeled as switched linear systems. This enables us to apply the theory of switched systems to establish the stability condition. The stabilizing controller design is based on sufficient robust stability conditions formulated as a solution of bilinear matrix inequality. Finally, a benchmark numerical example-double integrator is given to illustrate the effectiveness of the proposed method.

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25

Chen, Wenying, and Jingyu Li. "Design of H∞ Predictive Controller for Networked Control System." Journal of Engineering Mechanics and Machinery 7, no. 1 (2022): 38–48. http://dx.doi.org/10.23977/jemm.2022.070106.

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For a class of network control systems with both data packet dropout and network communication delay problems, a new robust model predictive control method with compensation function is proposed. Considering that the system has interference problems, in the two cases of long-delay and short-delay, the packet loss problem is established as a Bernoulli sequence, and then a discrete NCS model based on the state observer is obtained. The state observer in the model can deal with the data packet dropout compensate and predict the state of the long-delay problem. Through linear matrix inequality and Lyapunov method, the controller is designed to obtain sufficient conditions for the closed-loop system to be exponentially stable and meet the specified performance indicators. Finally, compared with the method without any compensation measures, the method in this paper can get better control effect.

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26

Kouki, Rihab, Hichem Salhi, and Faouzi Bouani. "Embedded predictive control strategy based on Internet of Things technology: Application to a thermal process under imperfect wireless network." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 7 (December 6, 2019): 775–91. http://dx.doi.org/10.1177/0959651819890954.

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This article is concerned with the design of wireless-networked control framework based on Internet of Things technology and predictive control strategy to remote control a thermal benchmark system. In order to improve the control performance of systems, an autonomous real-time solution is proposed for handling network problems. The adopted control strategy is divided into two cooperative parts under a master–slave architecture, in which two STM32 microcontrollers are investigated. The slave board is connected closely to the process and the master one is a distant controller. The microcontrollers communicate wirelessly through the Transmission Control Protocol/Internet Protocol. In the master board, a model predictive output-estimator-based controller is designed to control wirelessly the benchmark system, even though the incoming outputs from the slave board are lost. However, a buffered structure is implemented on the slave board to compensate the input losses of the arrived control sequences. The performance of the proposed wireless-networked predictive control compensation strategy for packet loss and perturbation handling in the wireless-networked control system in this work is verified through different experimentation conditions. Also, a comparative study with a wireless-networked proportional integral controller is performed to demonstrate the effectiveness of wireless-networked predictive control strategy for practical Internet of Things applications.

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27

Kobayashi, Koichi, and Kunihiko Hiraishi. "Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/916040.

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Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems and is extensively studied in the field of control theory of networked systems and cyber-physical systems. In this paper, a new approach for self-triggered control is proposed from the viewpoint of model predictive control (MPC). First, the difficulty of self-triggered MPC is explained. To overcome this difficulty, two problems, that is, (i) the one-step input-constrained problem and (ii) theN-step input-constrained problem are newly formulated. By repeatedly solving either problem in each sampling period, the control input and the sampling period can be obtained, that is, self-triggered MPC can be realized. Next, an iterative solution method for the latter problem and an approximate solution method for the former problem are proposed. Finally, the effectiveness of the proposed approach is shown by numerical examples.

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28

Tian, Zhongda. "Networked control system time-delay compensation based on PI-based dynamic matrix control." at - Automatisierungstechnik 69, no. 1 (January 1, 2021): 41–51. http://dx.doi.org/10.1515/auto-2020-0020.

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Abstract The network-induced time-delay caused by the introduction of the communication network in a networked control system has a great negative impact on the stability and performance of the system. In order to compensate for the performance degradation of the networked control system caused by time-delay, a time-delay compensation method based on PI-based dynamic matrix control for networked control system is proposed. In this study, autoregressive integrated moving average model is used to predict the future time-delay. The predictive time-delay replaces the actual time-delay as a parameter of the controller. In order to improve the compensation effect of dynamic matrix control, the feedback structure of the PI control and the predictive ability of dynamic matrix control are combined. The new objective function of dynamic matrix control is combined with PI structure to obtain the optimal control increment value. The PI controller can correct the output of dynamic matrix control and reduce the deviation between the actual output and the predicted output. The effect of the model mismatch and interference on the system is reduced. The robustness and anti-interference performance of the system is improved. The controller can select the appropriate control value to transmit to the actuator to compensate for the effect of the random time-delay in the networked control system. The stability of the compensation method is proved. Through the simulation results, the effectiveness of the proposed time-delay compensation method is verified.

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29

Lu, Hong Mei, and Li Xin Zeng. "Predictive Method of Networked Control of Industrial Automation Systems." Advanced Materials Research 1022 (August 2014): 402–5. http://dx.doi.org/10.4028/www.scientific.net/amr.1022.402.

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A New method for real-time prediction of uncertain network transmission time delay and the closed-loop control method through the network manufacturing and industrial plant are introduced. Put forward delay prediction method is based on the multilayer perceptron neural model. To reduce the amount of the first layer of neurons network, thus reducing the computational burden a real-time implementation, a method for the determination of Markov delay sequence order. Used to predict delay and a zero order equivalent discrete time model of the plant, a kind of time-varying state feedback control algorithm is put forward a strategy to obtain real-time update. The stability of the closed-loop switch sufficient conditions is derived using the theorem of linear system. It shows that the method studies of industrial network by two cases, i.e., a DC motor driven transport roll paper and a milling machine. Simulation study describes the effectiveness of the method to control these challenging problems.

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30

Patrinos, Panagiotis, Pantelis Sopasakis, and Haralambos Sarimveis. "Stochastic Model Predictive Control for Constrained Networked Control Systems with Random Time Delay." IFAC Proceedings Volumes 44, no. 1 (January 2011): 12626–31. http://dx.doi.org/10.3182/20110828-6-it-1002.02575.

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31

Mao, Yawen, Su Liu, and Jinfeng Liu. "Robust economic model predictive control of nonlinear networked control systems with communication delays." International Journal of Adaptive Control and Signal Processing 34, no. 5 (March 2, 2020): 614–37. http://dx.doi.org/10.1002/acs.3103.

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32

Shi, Ting, Peng Shi, and Huiyan Zhang. "Model predictive control of distributed networked control systems with quantization and switching topology." International Journal of Robust and Nonlinear Control 30, no. 12 (April 28, 2020): 4584–99. http://dx.doi.org/10.1002/rnc.5002.

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33

Li, Zhijun, Dehui Sun, Yuntao Shi, and Lifeng Wang. "A stabilizing model predictive control for networked control system with data packet dropout." Journal of Control Theory and Applications 7, no. 3 (August 2009): 281–84. http://dx.doi.org/10.1007/s11768-009-7224-1.

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34

Franzè, Giuseppe, Francesco Tedesco, and Domenico Famularo. "Model predictive control for constrained networked systems subject to data losses." Automatica 54 (April 2015): 272–78. http://dx.doi.org/10.1016/j.automatica.2015.02.018.

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35

Ulusoy, Alphan, Ozgur Gurbuz, and Ahmet Onat. "Wireless Model-Based Predictive Networked Control System Over Cooperative Wireless Network." IEEE Transactions on Industrial Informatics 7, no. 1 (February 2011): 41–51. http://dx.doi.org/10.1109/tii.2010.2089059.

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36

Zheng, Yi, Shaoyuan Li, and Hai Qiu. "Networked Coordination-Based Distributed Model Predictive Control for Large-Scale System." IEEE Transactions on Control Systems Technology 21, no. 3 (May 2013): 991–98. http://dx.doi.org/10.1109/tcst.2012.2196280.

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37

Feng, Zhen, Jia Liu, and Jing Jing Xiong. "Application of GPC Algorithm Based on Network Delay." Advanced Materials Research 546-547 (July 2012): 972–76. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.972.

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Aiming at the problem of the network delay, this paper presents a kind of application of generalized predictive control algorithm in networked control systems. The algorithm applys future control signal predicted by MPC (model predictive control) to compensating for the delay or interruption in forward channel, and the delay in feedback channel with a predictor at the same time. The paper describes the control characteristic and discusses the stability of network control system, and verificates the algorithm's feasible and effective characteristics in networked control systems through the simulation.

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38

Chen, Qiuxia, Ying Liu, and Haoqi Zhu. "Robust Output Feedback Model Predictive Control for a Class of Networked Control Systems with Nonlinear Perturbation." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/154158.

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This paper is concerned with the design problem of robust dynamic output feedback model predictive controllers for a class of discrete-time systems with time-varying network-induced delays and nonlinear perturbation. The designed controllers achieve on-line suboptimal receding horizon guaranteed cost such that the system can be stabilized for all admissible uncertainties. A novel delay compensation strategy is proposed to eliminate the effects of the time-varying network-induced delays. By using multistep prediction and the receding optimization, the delay-dependent sufficient condition is derived for the existence of delay compensation controllers. By employing the cone complementarity linearization (CCL) idea, a nonlinear minimization problem with linear matrix inequality (LMI) constraints is formulated to design the desired output feedback controllers, and an iterative algorithm involving convex optimization is presented to solve the nonlinear minimization problem. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed results.

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39

Yao, Deyin, Hamid Reza Karimi, Yiyong Sun, and Qing Lu. "Robust Model Predictive Control of Networked Control Systems under Input Constraints and Packet Dropouts." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/478567.

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This paper deals with the problem of robust model predictive control (RMPC) for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI) constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.

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40

Liu, Andong, Li Yu, and Wen-an Zhang. "Switched model predictive control for networked control systems with time delays and packet disordering." IFAC Proceedings Volumes 47, no. 3 (2014): 3764–69. http://dx.doi.org/10.3182/20140824-6-za-1003.01116.

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41

He, Ning, Dawei Shi, and Tongwen Chen. "Self-triggered model predictive control for networked control systems based on first-order hold." International Journal of Robust and Nonlinear Control 28, no. 4 (September 20, 2017): 1303–18. http://dx.doi.org/10.1002/rnc.3953.

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42

Wang, Le, Haipeng Pan, Jinfeng Gao, and Dongdong Chen. "Predictive Compensation for Wireless Networked System with Time Delay and Packet Dropout Based on T-S Model." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/953039.

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Based on the T-S model, a predictive compensation scheme including timer and counter for wireless networked system with long time delay and data packet dropout is proposed in this paper. By the separation principle, the state observation predictor and the state feedback controller are designed separately. For the case of fixed delay, the stability of the closed-loop networked control systems is discussed. Simulation by inverted pendulum system illustrates the effectiveness of the proposed method in wireless networked system based on T-S model.

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43

Zhang, Yu, Shousheng Xie, Ledi Zhang, and Litong Ren. "Robust Sliding Mode Predictive Control of Uncertain Networked Control System with Random Time Delay." Discrete Dynamics in Nature and Society 2018 (July 19, 2018): 1–11. http://dx.doi.org/10.1155/2018/6959250.

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This paper proposes a sliding mode predictive controller with a new robust global sliding surface for a certain networked control system with random time delay, mismatched parametric uncertainty, and external disturbances. First, the model of the networked control system is established, based on which linear transformation is made to get a new form of the system which does not have time delay term in expression. Then a global sliding surface is proposed followed by the sufficient condition given in the form of linear matrix inequality (LMI) to guarantee system stability and robustness. Subsequently, a sliding mode predictive controller is proposed with modified reaching law as its reference trajectory and the rolling optimization method is combined to provide optimal control input for each step so that chattering can be minimized. Finally, simulations have been made and the results indicate the advantages of the proposed controller in the aspect of convergence speed, chattering suppression, and robustness to uncertainties.

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44

Bai, Ting, Shaoyuan Li, and Yi Zheng. "Distributed model predictive control for networked plant-wide systems with neighborhood cooperation." IEEE/CAA Journal of Automatica Sinica 6, no. 1 (January 2019): 108–17. http://dx.doi.org/10.1109/jas.2019.1911333.

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45

Yin, Xiuxia, Dong Yue, Songlin Hu, Chen Peng, and Yusheng Xue. "Model-Based Event-Triggered Predictive Control for Networked Systems with Data Dropout." SIAM Journal on Control and Optimization 54, no. 2 (January 2016): 567–86. http://dx.doi.org/10.1137/130950951.

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46

Yang, Hongjiu, Shuang Ju, Jinhui Zhang, and Huanhuan Yuan. "Model predictive control for cloud-integrated networked multiagent systems under bandwidth allocation." Information Sciences 500 (October 2019): 156–72. http://dx.doi.org/10.1016/j.ins.2019.05.060.

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47

Lu, Qing, Peng Shi, Jianxing Liu, and Ligang Wu. "Model predictive control under event-triggered communication scheme for nonlinear networked systems." Journal of the Franklin Institute 356, no. 5 (March 2019): 2625–44. http://dx.doi.org/10.1016/j.jfranklin.2019.01.031.

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48

Nedić, Angelia, and Ji Liu. "Distributed Optimization for Control." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 77–103. http://dx.doi.org/10.1146/annurev-control-060117-105131.

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Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.

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49

Wang, Dazhong, Shujing Wu, and Shigenori Okubo. "Design of the State Predictive Model Following Control System with Time-Delay." International Journal of Applied Mathematics and Computer Science 19, no. 2 (June 1, 2009): 247–54. http://dx.doi.org/10.2478/v10006-009-0020-8.

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Design of the State Predictive Model Following Control System with Time-DelayTime-delay systems exist in many engineering fields such as transportation systems, communication systems, process engineering and, more recently, networked control systems. It usually results in unsatisfactory performance and is frequently a source of instability, so the control of time-delay systems is practically important. In this paper, a design of the state predictive model following control system (PMFCS) with time-delay is discussed. The bounded property of the internal states for the control is given, and the utility of this control design is guaranteed. Finally, examples are given to illustrate the effectiveness of the proposed method, and state predictive control techniques are applied to congestion control synthesis problems for a TCP/AQM network.

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Zhang, Hui, Fang He, and Chun Yan Han. "Prediction of Network Utilization Based on ARMA Model and RELS." Advanced Materials Research 945-949 (June 2014): 2780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2780.

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Abstract:

This paper focused on predictive algorithm of network utilization for networked control system (NCS). Auto-Regressive and Moving Average (ARMA) model was presented for general network utilization, which with fixed constant and known white noise. ARMA model parameters are estimated using parameter estimation algorithm of Recursive Extended Least Squares (RELS). Finally, a simulation example was given to realize RELS of ARMA model. Predictive output of network utilization can be obtained and converge to real state.

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