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Artículos de revistas sobre el tema "Predictive and Adaptive Control"

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Publicado: 14 de marzo de 2023

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1

Ray, W. D. "Adaptive prediction and predictive control". International Journal of Forecasting 12, n.º 4 (diciembre de 1996): 566. http://dx.doi.org/10.1016/s0169-2070(96)00688-7.

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2

Clarke, D. W. "Adaptive predictive control". Annual Reviews in Control 20 (enero de 1996): 83–94. http://dx.doi.org/10.1016/s1367-5788(97)00007-2.

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3

Clarke, D. "Adaptive predictive control". Annual Review in Automatic Programming 20 (1996): 83–94. http://dx.doi.org/10.1016/s0066-4138(97)00007-4.

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4

Clarke, D. W. "Adaptive Predictive Control". IFAC Proceedings Volumes 28, n.º 13 (junio de 1995): 43–54. http://dx.doi.org/10.1016/s1474-6670(17)45325-0.

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5

Esteban, Segundo, Jesus Μ. de la Cruz y Jose Μ. Girón-sierra. "Adaptive Predictive Flight Control". IFAC Proceedings Volumes 33, n.º 13 (junio de 2000): 239–44. http://dx.doi.org/10.1016/s1474-6670(17)37196-3.

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6

Rao, G. P. "Adaptive prediction and predictive control [Book Reviews]". IEEE Transactions on Automatic Control 44, n.º 7 (julio de 1999): 1482–83. http://dx.doi.org/10.1109/tac.1999.774128.

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7

Flor Unda, Omar. "Adaptive control systems for solar collectors". Athenea 2, n.º 4 (15 de junio de 2021): 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.

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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264. [2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September. [3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344. [4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall. [5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico. [6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141. [7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266. [8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática. [9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.
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8

Geng, Tao y Jin Zhao. "Adaptive Cascade Generalized Predictive Control". International Journal of Intelligence Science 04, n.º 03 (2014): 70–79. http://dx.doi.org/10.4236/ijis.2014.43009.

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9

Wang, Wei y Rolf Henriksen. "Direct adaptive generalized predictive control". Modeling, Identification and Control: A Norwegian Research Bulletin 14, n.º 4 (1993): 181–91. http://dx.doi.org/10.4173/mic.1993.4.1.

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10

MeVay, A. C. H. y R. Sarpeshkar. "Predictive comparators with adaptive control". IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 50, n.º 9 (septiembre de 2003): 579–88. http://dx.doi.org/10.1109/tcsii.2003.815026.

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11

Wong, C. H., S. L. Shah, M. M. Bourke y D. G. Fisher. "Adaptive fuzzy relational predictive control". Fuzzy Sets and Systems 115, n.º 2 (octubre de 2000): 247–60. http://dx.doi.org/10.1016/s0165-0114(98)00295-4.

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12

Zanini, A., R. Bocanera y R. Cordes. "Adaptive Predictive Control: Practical Aspects." IFAC Proceedings Volumes 28, n.º 19 (septiembre de 1995): 241–47. http://dx.doi.org/10.1016/s1474-6670(17)45087-7.

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13

Monti, F., A. Zanini, R. Cordes y R. Bocanera. "Switching Model Adaptive Predictive Control". IFAC Proceedings Volumes 31, n.º 22 (agosto de 1998): 47–52. http://dx.doi.org/10.1016/s1474-6670(17)35919-0.

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14

Heirung, Tor Aksel N., B. Erik Ydstie y Bjarne Foss. "Dual adaptive model predictive control". Automatica 80 (junio de 2017): 340–48. http://dx.doi.org/10.1016/j.automatica.2017.01.030.

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15

Krener, Arthur J. "Adaptive Horizon Model Predictive Control". IFAC-PapersOnLine 51, n.º 13 (2018): 31–36. http://dx.doi.org/10.1016/j.ifacol.2018.07.250.

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16

McDermott, Patrick E. "Adaptive multivariable optimal predictive control". International Journal of Adaptive Control and Signal Processing 1, n.º 2 (noviembre de 1987): 111–28. http://dx.doi.org/10.1002/acs.4480010203.

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17

Marino, Francesco, Fabio Leccese y Stefano Pizzuti. "Adaptive Street Lighting Predictive Control". Energy Procedia 111 (marzo de 2017): 790–99. http://dx.doi.org/10.1016/j.egypro.2017.03.241.

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18

Kokko, T., P. Lautala y T. Huhtelin. "ADAPTIVE MODEL PREDICTIVE CONTROL OF CONSISTENCY". IFAC Proceedings Volumes 35, n.º 1 (2002): 73–78. http://dx.doi.org/10.3182/20020721-6-es-1901.01565.

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19

Tsai, Ching-Chih, Chien-Cheng Yu y Chia-Ta Tsai. "Adaptive ORFWNN-Based Predictive PID Control". International Journal of Fuzzy Systems 21, n.º 5 (16 de mayo de 2019): 1544–59. http://dx.doi.org/10.1007/s40815-019-00650-w.

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20

Matko, D. "Continuous-Time Generalized Predictive Adaptive Control". IFAC Proceedings Volumes 24, n.º 1 (enero de 1991): 41–46. http://dx.doi.org/10.1016/s1474-6670(17)51294-x.

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21

Pavlechko, P. D., M. C. Wellons y T. F. Edgar. "An Approach for Adaptive-Predictive Control". IFAC Proceedings Volumes 18, n.º 15 (octubre de 1985): 39–44. http://dx.doi.org/10.1016/b978-0-08-033431-8.50012-9.

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22

MARTIN SANCHEZ, JUAN M. y JOSE RODELLAR. "ADAPTIVE PREDICTIVE CONTROL: LIMITS OF STABILITY". International Journal of Adaptive Control and Signal Processing 11, n.º 4 (junio de 1997): 263–83. http://dx.doi.org/10.1002/(sici)1099-1115(199706)11:4<263::aid-acs431>3.0.co;2-7.

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23

Romanov, Anton A., Aleksey A. Filippov y Nadezhda G. Yarushkina. "Adaptive Fuzzy Predictive Approach in Control". Mathematics 11, n.º 4 (9 de febrero de 2023): 875. http://dx.doi.org/10.3390/math11040875.

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This article studies the approach to solving the problem of controlling the complex organizational and technical systems based on hybrid models. We propose a new component of intelligent decision support that is integrated with control systems. The proposed component is based on fuzzy logic and knowledge engineering. We present a model of ontology to form the context of data analysis and time series modeling. The ontological context allows us to represent trends of the analyzed object indicators. An expert can add a set of fuzzy rules to the ontology for systems control based on the fuzzy inference. The proposed approach allows reducing the time of analysis and interpretation of the results. Experimental results confirm the correctness and effectiveness of the approach proposed in this article.
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24

G.S.S.S.S.V., Krishna Mohan. "Auto-tuning Smith-predictive Control of Delayed Processes based on Model Reference Adaptive Controller". Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31 de marzo de 2020): 1224–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201597.

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25

Vojtesek, Jiri, Petr Dostal y Vladimir Bobal. "Control of Nonlinear System – Adaptive and Predictive Control". IFAC Proceedings Volumes 42, n.º 11 (2009): 898–903. http://dx.doi.org/10.3182/20090712-4-tr-2008.00147.

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26

Yin, Fang Chen, Geng Sheng Ma, Ya Feng Ji, Jia Xue Yu, De Hao Gu y Dian Hua Zhang. "Fuzzy Adaptive Direct Generalized Predictive Control Algorithm in the Application of AWC Control". Advanced Materials Research 945-949 (junio de 2014): 2529–32. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2529.

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Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on generalized predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that generalized predictive controller achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the generalized predictive controller is not influenced by model parameter variations .
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27

Kandare, G. y A. Nevado Reviriego. "Adaptive predictive expert control of dissolved oxygen concentration in a wastewater treatment plant". Water Science and Technology 64, n.º 5 (1 de septiembre de 2011): 1130–36. http://dx.doi.org/10.2166/wst.2011.276.

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In this paper we present the application of adaptive predictive expert controllers to dissolved oxygen (DO) control in the aerobic reactors of a wastewater treatment plant. The control system described in this paper consists of adaptive predictive expert control loops complemented by optimisation logic. The controllers successfully cope with nonlinearity and changing operating conditions of the process by predicting the evolution of the controlled variable and adapting to changes in the process dynamics. This results in more precise and stable DO control, offering many benefits. The complementary optimisation logic maintains the air pressure in the common collector at the lowest possible level, enabling adequate DO control and thus considerably reducing energy consumption.
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28

白家納, 白家納 y 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發". 理工研究國際期刊 12, n.º 1 (abril de 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.

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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by MPC, which in turn, reduces computational load. The learning dataset is acquired from the simulation data of the input/output of the MPC controller. We designed the proposed deep learning controller using long short-term memory networks (LSTMs) and simulated it in MATLAB/Simulink using the vehicle’s characteristics from the advanced vehicle simulator (ADVISOR). Finally, the safety and driving comfort are compared with the PID-based control to demonstrate the performance of the proposed deep-learning architecture.</p> <p>&nbsp;</p>
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29

Lopes, António y Rui Esteves Araújo. "Model-based Predictive Control implementation for Cooperative Adaptive Cruise Control". U.Porto Journal of Engineering 2, n.º 1 (19 de marzo de 2018): 1–10. http://dx.doi.org/10.24840/2183-6493_002.001_0001.

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The automation of road vehicles has become a necessity to improve the efficiency and safety of this system. In a vehicle formation it is important to maintain a safety distance between the vehicles. The control of a vehicle spacing distance and longitudinal velocity can be achieved through the implementation of a model-based predictive controller. This implementation of a cooperative adaptive cruise control allows the access of another vehicle state information through vehicular communication technology and promote state prediction and ultimately system stability. The optimization algorithm performs the computation of the control input in a control horizon window and ensures that the spacing error takes only positive values. The results of the proposed controller are evaluated through the computational tool Simulink in the two-vehicle platoon. The controller is implemented in the precedent vehicle. To assess the performance of the proposed controller different control parameters and constraints were used.
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30

Chen, Zai Ping y Xue Wang. "Research on Networked Control Systems Based on Adaptive Predictive Control". Applied Mechanics and Materials 441 (diciembre de 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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31

Wang, Y., Y. Zhang, Y. Fu, T. Chai y J. Fu. "Adaptive decoupling switching control based on generalised predictive control". IET Control Theory & Applications 6, n.º 12 (16 de agosto de 2012): 1828–41. http://dx.doi.org/10.1049/iet-cta.2011.0053.

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32

Hartwich, Arndt, Martin Schlegel, Lynn Würth y Wolfgang Marquardt. "Adaptive control vector parameterization for nonlinear model-predictive control". International Journal of Robust and Nonlinear Control 18, n.º 8 (2008): 845–61. http://dx.doi.org/10.1002/rnc.1246.

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33

Mizumoto, Ikuro, Yotaro Fujimoto y Masataka Ikejiri. "Adaptive output predictor based adaptive predictive control with ASPR constraint". Automatica 57 (julio de 2015): 152–63. http://dx.doi.org/10.1016/j.automatica.2015.04.020.

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34

Chisci, L., G. Zappa y E. Mosca. "On the implementation of predictive adaptive control by adaptive predictors". International Journal of Adaptive Control and Signal Processing 4, n.º 3 (mayo de 1990): 187–206. http://dx.doi.org/10.1002/acs.4480040302.

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35

Sun, Guang, Jialei Chen, Yingqiong Yong y Yongyuan Li. "Generalized predictive control of spacecraft attitude with adaptive predictive period". International Journal of Adaptive Control and Signal Processing 36, n.º 3 (23 de noviembre de 2021): 596–606. http://dx.doi.org/10.1002/acs.3358.

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36

Dozal-Mejorada, Eduardo J., Priyesh Thakker y B. Erik Ydstie. "SUPERVISED ADAPTIVE PREDICTIVE CONTROL USING DUAL MODELS". IFAC Proceedings Volumes 40, n.º 5 (2007): 107–12. http://dx.doi.org/10.3182/20070606-3-mx-2915.00136.

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37

Cai, Zicheng y B. Erik Ydstie. "Dual Adaptive Model Predictive Control with Disturbances". IFAC-PapersOnLine 54, n.º 3 (2021): 206–11. http://dx.doi.org/10.1016/j.ifacol.2021.08.243.

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38

Mosca, E. "Optimal, Predictive, and Adaptive Control, Book Review". IEEE Control Systems 15, n.º 6 (diciembre de 1995): 92. http://dx.doi.org/10.1109/mcs.1995.476395.

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39

Nazaruddin, Yul Y. y M. Aria. "ADAPTIVE-PREDICTIVE CONTROL WITH INTELLIGENT VIRTUAL SENSOR". IFAC Proceedings Volumes 38, n.º 1 (2005): 263–68. http://dx.doi.org/10.3182/20050703-6-cz-1902.01122.

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40

Lie Tang y Robert G. Landers. "Predictive Contour Control With Adaptive Feed Rate". IEEE/ASME Transactions on Mechatronics 17, n.º 4 (agosto de 2012): 669–79. http://dx.doi.org/10.1109/tmech.2011.2119324.

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41

Eure, Kenneth, Jer‐Nan Juang y Richard Silcox. "Direct adaptive predictive control using gradient descent". Journal of the Acoustical Society of America 105, n.º 2 (febrero de 1999): 1300. http://dx.doi.org/10.1121/1.424826.

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42

Dumitrache, Ion, Nicolae Constantin y Monica Dragoicea. "Adaptive Predictive Control Based on Neural Networks". IFAC Proceedings Volumes 33, n.º 25 (septiembre de 2000): 335–39. http://dx.doi.org/10.1016/s1474-6670(17)39362-x.

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43

Szuda, J., M. Antoniová, B. Rohal’-Il’kiv y G. Hulkó. "Adaptive Predictive Control of Distributed Parameter Systems". IFAC Proceedings Volumes 30, n.º 21 (septiembre de 1997): 487–93. http://dx.doi.org/10.1016/s1474-6670(17)41486-8.

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44

Wang, D. H. y C. B. Soh. "Adaptive neural model-based decentralized predictive control". International Journal of Systems Science 31, n.º 1 (enero de 2000): 119–30. http://dx.doi.org/10.1080/002077200291523.

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45

Yoon, Tae-Woong y D. W. Clarke. "Adaptive Predictive Control of the Benchmark Plant". IFAC Proceedings Volumes 26, n.º 2 (julio de 1993): 125–28. http://dx.doi.org/10.1016/s1474-6670(17)48909-9.

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46

Xiao-hui, YANG, LIU He-sheng y LIU Guo-ping. "An Adaptive Teleoperation Based On Predictive Control". Procedia Engineering 16 (2011): 151–56. http://dx.doi.org/10.1016/j.proeng.2011.08.1065.

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47

Akouz, K., A. Benhammou y P. O. Malaterre. "Adaptive Predictive Control of an Irrigation Channel". IFAC Proceedings Volumes 30, n.º 5 (mayo de 1997): 239–44. http://dx.doi.org/10.1016/s1474-6670(17)44439-9.

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48

Liu, G. P. y S. Daley. "Adaptive Predictive Control of Combustor NOx Emissions". IFAC Proceedings Volumes 32, n.º 2 (julio de 1999): 7208–13. http://dx.doi.org/10.1016/s1474-6670(17)57230-4.

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49

Liu, G. P. y S. Daley. "Adaptive predictive control of combustor NOx emissions". Control Engineering Practice 9, n.º 6 (junio de 2001): 631–38. http://dx.doi.org/10.1016/s0967-0661(01)00019-3.

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50

Huang, Hsiao Ping, Yung Cheng Chao y Pea Hsien Liu. "Predictive adaptive control system for unmeasured disturbances". Industrial & Engineering Chemistry Process Design and Development 24, n.º 3 (julio de 1985): 666–73. http://dx.doi.org/10.1021/i200030a023.

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