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Journal articles on the topic 'Multiple model adaptive control'

Author: Grafiati

Published: 25 May 2024

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1

Kuipers, Matthew, and Petros Ioannou. "Multiple Model Adaptive Control With Mixing." IEEE Transactions on Automatic Control 55, no. 8 (August 2010): 1822–36. http://dx.doi.org/10.1109/tac.2010.2042345.

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2

Yang, Xinhao, and Ze Li. "Congestion Control Based on Multiple Model Adaptive Control." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/714320.

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The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio.

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3

Lourenco, J. M. A., and J. M. Lemos. "Learning in switching multiple model adaptive control." IEEE Instrumentation and Measurement Magazine 9, no. 3 (June 2006): 24–29. http://dx.doi.org/10.1109/mim.2006.1637975.

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4

Kim, Youngchol, Sunwook Choi, and Taeshin Cho. "Multiple model adaptive control for interval systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4699–704. http://dx.doi.org/10.1016/s1474-6670(17)56801-9.

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5

Zhang, Yingzhao, Weicun Zhang, Xiao Wang, and Handong Li. "Multiple Model Adaptive Control of Flexible Arm." Proceedings of International Conference on Artificial Life and Robotics 24 (January 10, 2019): 391–94. http://dx.doi.org/10.5954/icarob.2019.os14-2.

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6

Nasr, Tamer Ahmed, Hossam A. Abdel Fattah, and Adel A. R. Hanafy. "Multiple-model adaptive control for interval plants." International Journal of Control, Automation and Systems 10, no. 1 (February 2012): 11–19. http://dx.doi.org/10.1007/s12555-012-0102-5.

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7

Anderson, Brian D. O., Thomas Brinsmead, Daniel Liberzon, and A. Stephen Morse. "Multiple model adaptive control with safe switching." International Journal of Adaptive Control and Signal Processing 15, no. 5 (2001): 445–70. http://dx.doi.org/10.1002/acs.684.

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8

Hespanha, Jo�o, Daniel Liberzon, A. Stephen Morse, Brian D. O. Anderson, Thomas S. Brinsmead, and Franky De Bruyne. "Multiple model adaptive control. Part 2: switching." International Journal of Robust and Nonlinear Control 11, no. 5 (2001): 479–96. http://dx.doi.org/10.1002/rnc.594.

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9

Maybeck, P. S., and R. D. Stevens. "Reconfigurable flight control via multiple model adaptive control methods." IEEE Transactions on Aerospace and Electronic Systems 27, no. 3 (May 1991): 470–80. http://dx.doi.org/10.1109/7.81428.

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10

He, W. G., Howard Kaufman, and Rob Roy. "Multiple Model Adaptive Control Procedure for Blood Pressure Control." IEEE Transactions on Biomedical Engineering BME-33, no. 1 (January 1986): 10–19. http://dx.doi.org/10.1109/tbme.1986.325833.

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11

Wang, Kang, Xiaoli Li, and Yang Li. "Multiple Model Adaptive Tracking Control Based on Adaptive Dynamic Programming." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6023892.

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Adaptive dynamic programming (ADP) has been tested as an effective method for optimal control of nonlinear system. However, as the structure of ADP requires control input to satisfy the initial admissible control condition, the control performance may be deteriorated due to abrupt parameter change or system failure. In this paper, we introduce the multiple models idea into ADP, multiple subcontrollers run in parallel to supply multiple initial conditions for different environments, and a switching index is set up to decide the appropriate initial conditions for current system. By taking this strategy, the proposed multiple model ADP achieves optimal control for system with jumping parameters. The convergence of multiple model adaptive control based on ADP is proved and the simulation shows that the proposed method can improve the transient response of system effectively.

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12

Hassani, Vahid, João Pedro Hespanha, Michael Athans, and António M. Pascoal. "Stability Analysis of Robust Multiple Model Adaptive Control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 350–55. http://dx.doi.org/10.3182/20110828-6-it-1002.01194.

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13

WANG, Hui, and Wei Cun Zhang. "Weighted Multiple Model Adaptive Control Based on ADRC." Applied Mechanics and Materials 313-314 (March 2013): 412–17. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.412.

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Weighted multiple model adaptive control is a combination of off-line design and on-line decision, which combines a finite number of simple controllers by weighting algorithm. Its an effective means to solve the control problems of complex and uncertain systems. Active Disturbance Rejection Control (ADRC) is a new digital control technology with high accuracy and strong robustness. This paper introduces ADRC as local controller in the weighted multiple model adaptive control system. The simulation results show that the proposed system has strong robustness in a wide range.

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14

Irving, E., C. M. Falinower, and C. Fonte. "Adaptive Generalized Predictive Control with Multiple Reference Model." IFAC Proceedings Volumes 20, no. 2 (July 1987): 67–72. http://dx.doi.org/10.1016/s1474-6670(17)55939-x.

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15

Brend, Oliver, Chris Freeman, and Mark French. "Multiple-Model Adaptive Control of Functional Electrical Stimulation." IEEE Transactions on Control Systems Technology 23, no. 5 (September 2015): 1901–13. http://dx.doi.org/10.1109/tcst.2015.2394508.

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16

Rosa, Paulo, and Carlos Silvestre. "Multiple-model adaptive control using set-valued observers." International Journal of Robust and Nonlinear Control 24, no. 16 (May 3, 2013): 2490–511. http://dx.doi.org/10.1002/rnc.3005.

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17

Narendra, K. S., and J. Balakrishnan. "Adaptive control using multiple models." IEEE Transactions on Automatic Control 42, no. 2 (1997): 171–87. http://dx.doi.org/10.1109/9.554398.

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18

Jung, Bokyung, Youdan Kim, Cheolkeun Ha, and Min-Jea Tahk. "NONLINEAR RECONFIGURABLE FLIGHT CONTROL SYSTEM USING MULTIPLE MODEL ADAPTIVE CONTROL." IFAC Proceedings Volumes 40, no. 7 (2007): 171–76. http://dx.doi.org/10.3182/20070625-5-fr-2916.00030.

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19

Schiller, Gregory J., and Peter S. Maybeck. "Space Structure Control using Multiple Model Adaptive Estimation And Control." IFAC Proceedings Volumes 27, no. 13 (September 1994): 219–24. http://dx.doi.org/10.1016/s1474-6670(17)45803-4.

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20

Schott, Kevin D., and B. Wayne Bequette. "Control of Chemical Reactors Using Multiple-Model Adaptive Control (MMAC)." IFAC Proceedings Volumes 28, no. 9 (June 1995): 345–50. http://dx.doi.org/10.1016/s1474-6670(17)47061-3.

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21

Liu, J., and R. Li. "Hierarchical adaptive interacting multiple model algorithm." IET Control Theory & Applications 2, no. 6 (June 1, 2008): 479–87. http://dx.doi.org/10.1049/iet-cta:20070340.

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22

Peng, Shu Xing, Hua Xue, and Dong Dong Li. "Study on Fuzzy Multiple Reference Model Adaptive Control Strategies." Advanced Materials Research 962-965 (June 2014): 2932–38. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2932.

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A new fuzzy multiple reference model adaptive control method combined with fuzzy select and conventional adaptive control is presented. To overcome the control difficulties which due to significant and unpredictable system parameter variations, fuzzy logic rules are designed to choose the suitable reference model. The new method is applied to control the speed servo system of dynamic model of BLDCM, and the simulation results show it works well with high dynamic performance and control precision under the condition of great change in reference speed and load torque.

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23

N. Xu, Y., and W. W. Deng. "Research of Multiple Sensors Adaptive Fault-Tolerant Control Based on T-S Fuzzy Model for EMB System." International Journal of Engineering and Technology 7, no. 1 (February 2015): 65–69. http://dx.doi.org/10.7763/ijet.2015.v7.768.

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24

Yu, Clement, W. G. He, James M. So, Rob Roy, Howard Kaufman, and Jonathan C. Newell. "Improvement in Arteral Oxygen Control Using Multiple-Model Adaptive Control Procedures." IEEE Transactions on Biomedical Engineering BME-34, no. 8 (August 1987): 567–74. http://dx.doi.org/10.1109/tbme.1987.326067.

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25

Gundala, R., K. A. Hoo, and M. J. Piovoso. "Multiple Model Adaptive Control Design for a Multiple-Input Multiple-Output Chemical Reactor." Industrial & Engineering Chemistry Research 39, no. 6 (June 2000): 1554–64. http://dx.doi.org/10.1021/ie990496x.

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26

GUO, Yu-Ying, and Bin JIANG. "Multiple Model-based Adaptive Reconfiguration Control for Actuator Fault." Acta Automatica Sinica 35, no. 11 (December 24, 2009): 1452–58. http://dx.doi.org/10.3724/sp.j.1004.2009.01452.

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27

Zhang, Weicun, and Qing Li. "Weighted Multiple Model Adaptive Control of Time-varying Systems." Journal of Robotics, Networking and Artificial Life 1, no. 4 (2015): 291. http://dx.doi.org/10.2991/jrnal.2015.1.4.8.

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28

WATANABE, KEIGO, and SPYROS G. TZAFESTAS. "Multiple-model adaptive control for jump-linear stochastic systems." International Journal of Control 50, no. 5 (November 1989): 1603–17. http://dx.doi.org/10.1080/00207178908953454.

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29

Baldi, S., P. Ioannou, and E. Mosca. "Multiple Model Adaptive Mixing Control: The Discrete-Time Case." IEEE Transactions on Automatic Control 57, no. 4 (April 2012): 1040–45. http://dx.doi.org/10.1109/tac.2011.2169620.

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30

Li, Xiaoli, Shuning Wang, and Wei Wang. "IMPROVED MULTIPLE MODEL ADAPTIVE CONTROL AND ITS STABILITY ANALYSIS." IFAC Proceedings Volumes 35, no. 1 (2002): 97–102. http://dx.doi.org/10.3182/20020721-6-es-1901.00504.

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31

Guo, Y., B. Jiang, Y. Zhang, and Z.-W. Zhu. "Multiple model adaptive reconfiguration control of state delayed systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 226, no. 3 (October 13, 2011): 325–37. http://dx.doi.org/10.1177/0959651811420046.

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32

Brown, D., O. R. Tutty, and E. Rogers. "Enhancements to non-linear multiple model adaptive control schemes." International Journal of Control 79, no. 9 (September 2006): 1010–25. http://dx.doi.org/10.1080/00207170600627485.

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33

GUO, Yu-Ying, and Bin JIANG. "Multiple Model-based Adaptive Reconfiguration Control for Actuator Fault." Acta Automatica Sinica 35, no. 11 (November 2009): 1452–58. http://dx.doi.org/10.1016/s1874-1029(08)60117-2.

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34

Teles, Francisco F., and João M. Lemos. "Cancer therapy optimization based on multiple model adaptive control." Biomedical Signal Processing and Control 48 (February 2019): 255–64. http://dx.doi.org/10.1016/j.bspc.2018.09.016.

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35

Bendtsen, Jan, and Klaus Trangbaek. "Multiple Model Adaptive Control Using Dual Youla-Kucera Factorisation." IFAC Proceedings Volumes 45, no. 13 (2012): 63–68. http://dx.doi.org/10.3182/20120620-3-dk-2025.00143.

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36

Anderson, B. D. O., T. S. Brinsmead, F. De Bruyne, J. Hespanha, D. Liberzon, and A. S. Morse. "Multiple model adaptive control. Part 1: Finite controller coverings." International Journal of Robust and Nonlinear Control 10, no. 11-12 (2000): 909–29. http://dx.doi.org/10.1002/1099-1239(200009/10)10:11/12<909::aid-rnc532>3.0.co;2-z.

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37

Fekri, Sajjad, Michael Athans, and Antonio Pascoal. "Robust multiple model adaptive control (RMMAC): a case study." International Journal of Adaptive Control and Signal Processing 21, no. 1 (2007): 1–30. http://dx.doi.org/10.1002/acs.944.

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38

Sohège, Yves, Marcos Quinones-Grueiro, and Gregory Provan. "Automated controller tuning for Weighted Multiple Model Adaptive Control." IFAC-PapersOnLine 56, no. 2 (2023): 4114–19. http://dx.doi.org/10.1016/j.ifacol.2023.10.1744.

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39

Dougherty, Danielle, and Doug Cooper. "A practical multiple model adaptive strategy for multivariable model predictive control." Control Engineering Practice 11, no. 6 (June 2003): 649–64. http://dx.doi.org/10.1016/s0967-0661(02)00170-3.

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40

NANIWA, Tomohide, and Suguru ARIMOTO. "Learning Control and Model-Based Adaptive Control for Coordination of Multiple Manipulators." Transactions of the Society of Instrument and Control Engineers 32, no. 5 (1996): 706–13. http://dx.doi.org/10.9746/sicetr1965.32.706.

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41

Slavov, Tsonyo. "Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model." Cybernetics and Information Technologies 12, no. 1 (March 1, 2012): 13–33. http://dx.doi.org/10.2478/cait-2012-0002.

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Abstract An algorithm for multiple model adaptive control of a time-variant plant in the presence of measurement noise is proposed. This algorithm controls the plant using a bank of PID controllers designed on the base of time invariant input/output models. The control signal is formed as weighting sum of the control signals of local PID controllers. The main contribution of the paper is the objective function minimized to determine the weighting coefficients. The proposed algorithm minimizes the sum of the square general error between the model bank output and the plant output. An equation for on-line determination of the weighting coefficients is obtained. They are determined by the current value of the general error covariance matrix. The main advantage of the algorithm is that the derived general error covariance matrix equation is the same as this in the recursive least square algorithm. Thus, most of the well known RLS modifications for the tracking timevariant parameters can be directly implemented. The algorithm performance is tested by simulation. Results with both SISO and MIMO time variant plants are obtained.

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42

Xie, Jing, Shujiang Li, Hua Yan, and Dong Yang. "Model reference adaptive control for switched linear systems using switched multiple models control strategy." Journal of the Franklin Institute 356, no. 5 (March 2019): 2645–67. http://dx.doi.org/10.1016/j.jfranklin.2018.10.036.

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43

Li, Wen Jun, Bai Ling An, and Hong Kun Zhang. "Adaptive Multiple Impedance Control Based on Passivity." Applied Mechanics and Materials 34-35 (October 2010): 265–70. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.265.

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Adaptive multiple impedance control based on passivity is studied about two robot manipulators cooperating an object which interacts with external environment actively. The dynamic model is derived by Newton-Euler equation and the relations between the forces are analyzed. The relations between stiffness coefficient and convergence are explained by solving the differential equation when the stiffness coefficient is known. The adaptive impedance controller based on passivity is designed combining adaptive control and generalized impedance control when the stiffness coefficient is unknown. The impedance control based on internal force is adopted for the cooperative system. The simulation results prove the validity of the method.

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44

Tjahyadi, Hendra, Fangpo He, and Karl Sammut. "Vibration control of a cantilever beam using multiple model adaptive resonant control." IFAC Proceedings Volumes 37, no. 12 (August 2004): 409–13. http://dx.doi.org/10.1016/s1474-6670(17)31503-3.

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45

Huang, Quanzhen, Suxia Chen, Mingming Huang, and Zhuangzhi Guo. "Adaptive Active Noise Suppression Using Multiple Model Switching Strategy." Shock and Vibration 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/7289076.

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Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

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46

Raafat, Safanah M., Ban K. Abd-AL Amear, and Ayman Al-Khazraji. "Multiple model adaptive postprandial glucose control of type 1 diabetes." Engineering Science and Technology, an International Journal 24, no. 1 (February 2021): 83–91. http://dx.doi.org/10.1016/j.jestch.2020.11.007.

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47

Yang, Chao, and Yingmin Jia. "Adaptive Multiple-Model Control of A Class of Nonlinear Systems." Journal of Robotics, Networking and Artificial Life 2, no. 2 (2015): 69. http://dx.doi.org/10.2991/jrnal.2015.2.2.1.

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48

WATANABE, Keigo. "A Method of Hierarchical Multiple Model Adaptive Estimation and Control." Transactions of the Institute of Systems, Control and Information Engineers 1, no. 4 (1988): 127–36. http://dx.doi.org/10.5687/iscie.1.127.

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49

Boskovic, Jovan D., Joseph A. Jackson, Raman K. Mehra, and Nhan T. Nguyen. "Multiple-Model Adaptive Fault-Tolerant Control of a Planetary Lander." Journal of Guidance, Control, and Dynamics 32, no. 6 (November 2009): 1812–26. http://dx.doi.org/10.2514/1.42719.

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50

Jung, Bokyung, Seong-Kyun Jeong, Dong-Hyun Lee, and Youdan Kim. "ADAPTIVE RECONFIGURABLE FLIGHT CONTROL SYSTEM USING MULTIPLE MODEL MODE SWITCHING." IFAC Proceedings Volumes 38, no. 1 (2005): 115–20. http://dx.doi.org/10.3182/20050703-6-cz-1902.01980.

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