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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Pannocchia, Gabriele. "ROBUST OFFSET-FREE MODEL PREDICTIVE CONTROL." IFAC Proceedings Volumes 35, no. 1 (2002): 297–302. http://dx.doi.org/10.3182/20020721-6-es-1901.00618.

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2

Maeder, Urban, Francesco Borrelli, and Manfred Morari. "Linear offset-free Model Predictive Control." Automatica 45, no. 10 (October 2009): 2214–22. http://dx.doi.org/10.1016/j.automatica.2009.06.005.

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3

Morari, M., and U. Maeder. "Nonlinear offset-free model predictive control." Automatica 48, no. 9 (September 2012): 2059–67. http://dx.doi.org/10.1016/j.automatica.2012.06.038.

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4

Pannocchia, Gabriele, and James B. Rawlings. "Disturbance models for offset-free model-predictive control." AIChE Journal 49, no. 2 (February 2003): 426–37. http://dx.doi.org/10.1002/aic.690490213.

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5

Belda, Květoslav. "Model Predictive Control for Offset-Free Reference Tracking." TRANSACTIONS ON ELECTRICAL ENGINEERING 5, no. 1 (March 30, 2020): 8–13. http://dx.doi.org/10.14311/tee.2016.1.008.

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Анотація:

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">The paper deals with the offset-free reference tracking problem of the Model Predictive Control (MPC). That problem is considered for a class of the constant or occasionally changed constant reference signals. Proposed solution arises from a simple subtraction of the ARX model <br /> of two consecutive time steps. The solution is adapted <br /> to a state-space form and it corresponds to usual predictive control design without increase of the design complexity. The construction of the prediction equations and predictive controller structure is explained in the paper.</span>

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6

Maeder, Urban, and Manfred Morari. "Offset-free reference tracking with model predictive control." Automatica 46, no. 9 (September 2010): 1469–76. http://dx.doi.org/10.1016/j.automatica.2010.05.023.

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7

Ooi, W. X., A. W. Hermansson, and C. H. Lim. "Model Predictive Control – Sliding Mode Control of a pH system." IOP Conference Series: Materials Science and Engineering 1257, no. 1 (October 1, 2022): 012036. http://dx.doi.org/10.1088/1757-899x/1257/1/012036.

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Abstract This paper studies the feasibility of using discrete sliding mode controller (SMC) to achieve offset-free control of nonlinear processes in the presence of disturbances. The performance of the SMC is compared to a multiple model predictive controller (MMPC) studying the ability of set-point tracking using the pH system as a case study. The results presented from the comparison show that SMC can perform offset-free control of a pH system, with the major drawback being slow response as well as oscillation at some pH values. Finally, a design of a combination between MMPC and SMC (MMPC-SMC) is proposed, with MMPC carrying out basic control response while the SMC fulfils the role of eliminating the offset. However, the inaccurate reading in the MATLAB simulation model does not generate the expected results on the pH control. Therefore, the modification on the MATLAB models is required to achieve the improved control system on the offset-free behaviour for the set-point tracking.

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8

Tatjewski, Piotr. "Offset-free nonlinear Model Predictive Control with state-space process models." Archives of Control Sciences 27, no. 4 (December 1, 2017): 595–615. http://dx.doi.org/10.1515/acsc-2017-0035.

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AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.

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9

Wallace, Matt, Prashant Mhaskar, John House, and Timothy I. Salsbury. "Offset-Free Model Predictive Control of a Heat Pump." Industrial & Engineering Chemistry Research 54, no. 3 (January 20, 2015): 994–1005. http://dx.doi.org/10.1021/ie5017915.

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10

Wallace, Matt, Steven Spielberg Pon Kumar, and Prashant Mhaskar. "Offset-Free Model Predictive Control with Explicit Performance Specification." Industrial & Engineering Chemistry Research 55, no. 4 (January 20, 2016): 995–1003. http://dx.doi.org/10.1021/acs.iecr.5b03772.

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11

Muske, Kenneth R., and Thomas A. Badgwell. "Disturbance modeling for offset-free linear model predictive control." Journal of Process Control 12, no. 5 (August 2002): 617–32. http://dx.doi.org/10.1016/s0959-1524(01)00051-8.

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12

Huusom, Jakob Kjøbsted, Niels Kjølstad Poulsen, Sten Bay Jørgensen, and John Bagterp Jørgensen. "Tuning SISO offset-free Model Predictive Control based on ARX models." Journal of Process Control 22, no. 10 (December 2012): 1997–2007. http://dx.doi.org/10.1016/j.jprocont.2012.08.007.

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13

Tian, Xuemin, Ping Wang, Dexian Huang, and Sheng Chen. "Offset-free multistep nonlinear model predictive control under plant-model mismatch." International Journal of Adaptive Control and Signal Processing 28, no. 3-5 (November 9, 2012): 444–63. http://dx.doi.org/10.1002/acs.2367.

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14

Zou, Tao. "Offset-Free Strategy by Double-Layered Linear Model Predictive Control." Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/808327.

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In the real applications, the model predictive control (MPC) technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. In the literature, conditions for offset-free linear model predictive control are given for combined estimator (for both the artificial disturbance and system state), steady-state target calculation, and dynamic controller. Usually, the offset-free property of the double-layered MPC is obtained under the assumption that the system is asymptotically stable. This paper considers the dynamic stability property of the double-layered MPC.

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15

Wang, Xiao Lan, and Li Sha Ye. "Offset-Free Model Predictive Control for Wind Power Generation Systems." Advanced Materials Research 724-725 (August 2013): 495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.495.

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Due to wind power generation system (WGS) is strongly nonlinear, multi-input multi-output and with high turbulent disturbance, we build a offset-free model considering system noise and measurement noise and its predictive controller for all operating area from cut-in speed to the cut-out wind speed. Finally we predict the optimum generator torque and pitch control signal. By add damping mode in drive train using torque control, the purpose of smoother power and less torsional torque pulsation on drive train are achieved. Simulation results shows that this predictive controller is able to reduce the impact of noise and interference on the system, ensuring constant power output and reducing the torque pulsation.

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16

Bonfitto, Angelo, Luis Miguel Castellanos Molina, Andrea Tonoli, and Nicola Amati. "Offset-Free Model Predictive Control for Active Magnetic Bearing Systems." Actuators 7, no. 3 (August 7, 2018): 46. http://dx.doi.org/10.3390/act7030046.

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This paper presents the study of linear Offset-Free Model Predictive Control (OF-MPC) for an Active Magnetic Bearing (AMB) application. The method exploits the advantages of classical MPC in terms of stability and control performance and, at the same time, overcomes the effects of the plant-model mismatch on reference tracking. The proposed approach is based on a disturbance observer with an augmented plant model including an input disturbance estimation. Besides the abovementioned advantages, this architecture allows a real-time estimation of low-frequency disturbance, such as slow load variations. This property can be of great interest for a variety of AMB systems, particularly where the knowledge of the external load is important to regulate the behavior of the controlled plant. To this end, the paper describes the modeling and design of the OF-MPC architecture and its experimental validation for a one degree of freedom AMB system. The effectiveness of the method is demonstrated in terms of the reference tracking performance, cancellation of plant-model mismatch effects, and low-frequency disturbance estimation.

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17

Askari, Masood, Mahmoud Moghavvemi, Haider A. F. Almurib, and K. M. Muttaqi. "Multivariable Offset-Free Model Predictive Control for Quadruple Tanks System." IEEE Transactions on Industry Applications 52, no. 2 (March 2016): 1882–90. http://dx.doi.org/10.1109/tia.2015.2501761.

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18

Wallace, Matt, Buddhadeva Das, Prashant Mhaskar, John House, and Tim Salsbury. "Offset-free model predictive control of a vapor compression cycle." Journal of Process Control 22, no. 7 (August 2012): 1374–86. http://dx.doi.org/10.1016/j.jprocont.2012.06.011.

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19

Dong, Zihang, and David Angeli. "Homothetic tube-based robust offset-free economic Model Predictive Control." Automatica 119 (September 2020): 109105. http://dx.doi.org/10.1016/j.automatica.2020.109105.

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20

Hou, Ligang, Ze Wu, Xin Jin, and Yue Wang. "Linear Offset-Free Model Predictive Control in the Dynamic PLS Framework." Information 10, no. 1 (December 24, 2018): 5. http://dx.doi.org/10.3390/info10010005.

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This work addresses the model predictive control (MPC) of the offset-free tracking problem in the dynamic partial least square (DyPLS) framework. Firstly, state space MPC based on the DyPLS is proposed. Then, two methods are proposed to solve the offset-free problem. One is to reform the state space model as a velocity form. Another is to augment the state space model with a disturbance model and estimate the mismatch between system output and model output with an estimator. Both methods use the system output as a feedback in the control scheme. Hence, the offset-free tracking is guaranteed, and unmeasured step disturbance can be rejected. The results of two simulations demonstrate the effectiveness of proposed methods.

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21

Hermansson, A. W., and S. Syafiie. "Offset-free control of a pH system using Multiple Model Predictive Control." IOP Conference Series: Materials Science and Engineering 778 (May 1, 2020): 012072. http://dx.doi.org/10.1088/1757-899x/778/1/012072.

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22

Wong, Wee Chin, and Jay H. Lee. "A Hidden Markov disturbance model for Offset-free linear model predictive control." IFAC Proceedings Volumes 41, no. 2 (2008): 1940–45. http://dx.doi.org/10.3182/20080706-5-kr-1001.00330.

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23

Son, Sang Hwan, Jong Woo Kim, Tae Hoon Oh, GiBaek Lee, and Jong Min Lee. "Improved offset-free model predictive control utilizing learned model-plant mismatch map." IFAC-PapersOnLine 55, no. 7 (2022): 792–97. http://dx.doi.org/10.1016/j.ifacol.2022.07.541.

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24

Faanes, Audun, and Sigurd Skogestad. "Offset-Free Tracking of Model Predictive Control with Model Mismatch: Experimental Results." Industrial & Engineering Chemistry Research 44, no. 11 (May 2005): 3966–72. http://dx.doi.org/10.1021/ie049422y.

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25

Ferramosca, Antonio, Alejandro H. González, and Daniel Limon. "Offset-free multi-model economic model predictive control for changing economic criterion." Journal of Process Control 54 (June 2017): 1–13. http://dx.doi.org/10.1016/j.jprocont.2017.02.014.

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26

Zhao, Qiang, Xin Jin, Huapeng Yu, and Shan Lu. "Nonlinear Offset-Free Model Predictive Control based on Dynamic PLS Framework." Processes 9, no. 10 (October 7, 2021): 1784. http://dx.doi.org/10.3390/pr9101784.

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A nonlinear offset-free model predictive control based on a dynamic partial least square (PLS) framework is proposed in this paper. A multi-output multi-input system is projected into latent variable space by a PLS outer model. For each latent variable model, the T–S fuzzy model is used to describe the nonlinear characteristics of the system; while the state-space model is used in T–S fuzzy model consequent parameters to describe the dynamic characteristics. A disturbance model is introduced in the state-space model. For model state variables, a state observer is used to compensate for the mismatch of the model. The case study results for the pH neutralization process show that the MPC controller based on this method can guarantee the tracking performance of the nonlinear system without static error.

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27

Horváth, Klaudia, Eduard Galvis, Manuel Gómez Valentín, and José Rodellar. "New offset-free method for model predictive control of open channels." Control Engineering Practice 41 (August 2015): 13–25. http://dx.doi.org/10.1016/j.conengprac.2015.04.002.

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28

Das, Buddhadeva, and Prashant Mhaskar. "Lyapunov-based offset-free model predictive control of nonlinear process systems." Canadian Journal of Chemical Engineering 93, no. 3 (January 29, 2015): 471–78. http://dx.doi.org/10.1002/cjce.22134.

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29

Errouissi, Rachid, Ahmed Al-Durra, S. M. Muyeen, Siyu Leng, and Frede Blaabjerg. "Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control." IEEE Transactions on Power Electronics 32, no. 3 (March 2017): 2265–77. http://dx.doi.org/10.1109/tpel.2016.2557964.

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30

Pannocchia, Gabriele, and Alberto Bemporad. "Combined Design of Disturbance Model and Observer for Offset-Free Model Predictive Control." IEEE Transactions on Automatic Control 52, no. 6 (June 2007): 1048–53. http://dx.doi.org/10.1109/tac.2007.899096.

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31

Li, Po, Rui Nan Liu, and Xiang Hui Ma. "Unknown Offset Free MPC for Buck Converter." Applied Mechanics and Materials 865 (June 2017): 175–80. http://dx.doi.org/10.4028/www.scientific.net/amm.865.175.

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Buck converters are commonly used as DC power supplies. To deal with the parameters uncertainty in R-L (resistance-inductance), an Unknown Offset Free Model Predictive Control (UOFMPC) method for buck converters have been proposed in this paper. Firstly, a continuous model for buck converters is established. Based on it, a discrete model with fixed sampling time is derived and the output of controller is set as the direct switch on/off signals. Secondly, one-step MPC method aimed at optimizing the output voltage with recursive least squares algorithm for parameters identification is given to satisfy the ability of adaptation in parameters. Finally, both the model and control scheme are validated by simulation in MATLAB/Simulink.

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32

Schmid, Patrick, and Peter Eberhard. "Offset-free Nonlinear Model Predictive Control by the Example of Maglev Vehicles." IFAC-PapersOnLine 54, no. 6 (2021): 83–90. http://dx.doi.org/10.1016/j.ifacol.2021.08.528.

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33

Huang, Rui, Lorenz T. Biegler, and Sachin C. Patwardhan. "Fast Offset-Free Nonlinear Model Predictive Control Based on Moving Horizon Estimation." Industrial & Engineering Chemistry Research 49, no. 17 (September 2010): 7882–90. http://dx.doi.org/10.1021/ie901945y.

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34

Siddiqui, Imran, Deepak Ingole, Dayaram Sonawane, and Sudhir Agashe. "Offset-free Nonlinear Model Predictive Control of A Drum-boiler Pilot Plant." IFAC-PapersOnLine 53, no. 1 (2020): 506–11. http://dx.doi.org/10.1016/j.ifacol.2020.06.085.

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35

Ntouskas, Sotiris, Haralambos Sarimveis, and Pantelis Sopasakis. "Model predictive control for offset-free reference tracking of fractional order systems." Control Engineering Practice 71 (February 2018): 26–33. http://dx.doi.org/10.1016/j.conengprac.2017.10.010.

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36

Wang, Xue, Baocang Ding, Xin Yang, and Zhaohong Ye. "Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method." Journal of Control Science and Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7279430.

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Model predictive control (MPC) with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents the necessary conditions for offset-free model predictive control based on the model. By applying disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the dynamic matrix control (DMC) algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.

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37

Bender, Frank A., Simon Goltz, Thomas Braunl, and Oliver Sawodny. "Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator." IEEE Transactions on Automation Science and Engineering 14, no. 4 (October 2017): 1682–94. http://dx.doi.org/10.1109/tase.2017.2700407.

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38

Vega Lara, Boris G., Luis M. Castellanos Molina, José P. Monteagudo Yanes, and Miguel A. Rodríguez Borroto. "Offset-free model predictive control for an energy efficient tropical island hotel." Energy and Buildings 119 (May 2016): 283–92. http://dx.doi.org/10.1016/j.enbuild.2016.03.040.

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39

Wang, Gaolin, Jiangbo Qi, Jin Xu, Xueguang Zhang, and Dianguo Xu. "Antirollback Control for Gearless Elevator Traction Machines Adopting Offset-Free Model Predictive Control Strategy." IEEE Transactions on Industrial Electronics 62, no. 10 (October 2015): 6194–203. http://dx.doi.org/10.1109/tie.2015.2431635.

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40

Caspari, Adrian, Hatim Djelassi, Adel Mhamdi, Lorenz T. Biegler, and Alexander Mitsos. "Semi-infinite programming yields optimal disturbance model for offset-free nonlinear model predictive control." Journal of Process Control 101 (May 2021): 35–51. http://dx.doi.org/10.1016/j.jprocont.2021.03.005.

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41

Cai, Hongbin, Ping Li, Chengli Su, and Jiangtao Cao. "Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection." Measurement and Control 51, no. 7-8 (July 6, 2018): 260–75. http://dx.doi.org/10.1177/0020294018785500.

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Анотація:

This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be considered to calculate steady-state target in steady-state target calculation layer. Moreover, the state feedback control law can be obtained in dynamic control layer. A simple proof for offset-free control is given in the proposed method. The simulation results show that the controlled variable can achieve the offset-free control. It can be seen that the proposed method has better disturbance rejection performance, strong robustness and practical value.

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42

Hammoud, Issa, Ke Xu, Sebastian Hentzelt, Thimo Oehlschlaegel, and Ralph Kennel. "On Offset-Free Continuous Model Predictive Current Control of Permanent Magnet Synchronous Motors." IFAC-PapersOnLine 53, no. 2 (2020): 6662–69. http://dx.doi.org/10.1016/j.ifacol.2020.12.088.

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43

Deenen, D. A., E. Maljaars, L. Sebeke, Bram de Jager, E. Heijman, H. Grüll, and W. P. M. H. Heemels. "Offset-free model predictive control for enhancing MR-HIFU hyperthermia in cancer treatment." IFAC-PapersOnLine 51, no. 20 (2018): 191–96. http://dx.doi.org/10.1016/j.ifacol.2018.11.012.

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44

Chen, Jie, Yu Dang, and Jianda Han. "Offset-free model predictive control of a soft manipulator using the Koopman operator." Mechatronics 86 (October 2022): 102871. http://dx.doi.org/10.1016/j.mechatronics.2022.102871.

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45

Castellanos Molina, Luis M., Angelo Bonfitto, and Renato Galluzzi. "Offset-Free Model Predictive Control for a cone-shaped active magnetic bearing system." Mechatronics 78 (October 2021): 102612. http://dx.doi.org/10.1016/j.mechatronics.2021.102612.

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46

Park, ByungJun, Jong Woo Kim, and Jong Min Lee. "Data-driven offset-free multilinear model predictive control using constrained differential dynamic programming." Journal of Process Control 107 (November 2021): 1–16. http://dx.doi.org/10.1016/j.jprocont.2021.09.010.

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47

Gao, Haiyan, Yuanli Cai, Zhiyong Chen, and Zhenhua Yu. "Offset-Free Output Feedback Robust Model Predictive Control for a Generic Hypersonic Vehicle." Journal of Aerospace Engineering 28, no. 6 (November 2015): 04014147. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000482.

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48

Vatankhah, Bahareh, and Mohammad Farrokhi. "Nonlinear Adaptive Model Predictive Control of Constrained Systems with Offset‐Free Tracking Behavior." Asian Journal of Control 21, no. 5 (September 28, 2017): 2232–44. http://dx.doi.org/10.1002/asjc.1655.

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Kim, Seok-Kyoon, Dae-Keun Choi, Kyo-Beum Lee, and Young Il Lee. "Offset-Free Model Predictive Control for the Power Control of Three-Phase AC/DC Converters." IEEE Transactions on Industrial Electronics 62, no. 11 (November 2015): 7114–26. http://dx.doi.org/10.1109/tie.2015.2436353.

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