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Journal articles on the topic 'Offset free control'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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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

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

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3

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

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4

Heath, W. P., M. Polignano, and G. Pannocchia. "Observer-based offset-free internal model control." IFAC-PapersOnLine 50, no. 1 (2017): 898–903. http://dx.doi.org/10.1016/j.ifacol.2017.08.078.

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5

ELBER, GERSHON, and ELAINE COHEN. "ERROR BOUNDED VARIABLE DISTANCE OFFSET OPERATOR FOR FREE FORM CURVES AND SURFACES." International Journal of Computational Geometry & Applications 01, no. 01 (1991): 67–78. http://dx.doi.org/10.1142/s0218195991000062.

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Most offset approximation algorithms for freeform curves and surfaces may be classified into two main groups. The first approximates the curve using simple primitives such as piecewise arcs and lines and then calculates the (exact) offset operator to this approximation. The second offsets the control polygon/mesh and then attempts to estimate the error of the approximated offset over a region. Most of the current offset algorithms estimate the error using a finite set of samples taken from the region and therefore can not guarantee the offset approximation is within a given tolerance over the whole curve or surface. This paper presents new methods to globally bound the error of the approximated offset of freeform curves and surfaces and then automatically derive new approximations with improved accuracy. These tools can also be used to develop a global error bound for a variable distance offset operation and to detect and trim out loops in the offset.
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6

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

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7

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

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<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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8

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

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9

Salvador, Jose R., Daniel Rodriguez Ramirez, Teodoro Alamo, and David Muñoz de la Peña. "Offset free data driven control: application to a process control trainer." IET Control Theory & Applications 13, no. 18 (2019): 3096–106. http://dx.doi.org/10.1049/iet-cta.2019.0376.

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10

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 (2015): 994–1005. http://dx.doi.org/10.1021/ie5017915.

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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 (2002): 617–32. http://dx.doi.org/10.1016/s0959-1524(01)00051-8.

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12

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 (2016): 995–1003. http://dx.doi.org/10.1021/acs.iecr.5b03772.

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13

Dang, D. Q., Y. Wang, and W. Cai. "Offset-Free Predictive Control for Variable Speed Wind Turbines." IEEE Transactions on Sustainable Energy 4, no. 1 (2013): 2–10. http://dx.doi.org/10.1109/tste.2012.2195731.

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14

Pannocchia, Gabriele, and Eric C. Kerrigan. "Offset-free receding horizon control of constrained linear systems." AIChE Journal 51, no. 12 (2005): 3134–46. http://dx.doi.org/10.1002/aic.10626.

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15

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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16

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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17

Hou, Ligang, Ze Wu, Xin Jin, and Yue Wang. "Linear Offset-Free Model Predictive Control in the Dynamic PLS Framework." Information 10, no. 1 (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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18

Pannocchia, Gabriele, and William P. Heath. "Offset-free IMC with generalized disturbance models." Automatica 122 (December 2020): 109270. http://dx.doi.org/10.1016/j.automatica.2020.109270.

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19

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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20

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 (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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21

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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22

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 (2012): 1374–86. http://dx.doi.org/10.1016/j.jprocont.2012.06.011.

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23

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 (2016): 1882–90. http://dx.doi.org/10.1109/tia.2015.2501761.

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24

Ławryńczuk, Maciej, and Piotr Tatjewski. "Offset-free state-space nonlinear predictive control for Wiener systems." Information Sciences 511 (February 2020): 127–51. http://dx.doi.org/10.1016/j.ins.2019.09.042.

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25

Jo, Yeon-Pyeong, Mohammed Saad Faizan Bangi, Sang-Hwan Son, Joseph Sang-Il Kwon, and Sung-Won Hwang. "Dynamic modeling and offset-free predictive control of LNG tank." Fuel 285 (February 2021): 119074. http://dx.doi.org/10.1016/j.fuel.2020.119074.

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26

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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27

Pannocchia, Gabriele. "An economic MPC formulation with offset-free asymptotic performance." IFAC-PapersOnLine 51, no. 18 (2018): 393–98. http://dx.doi.org/10.1016/j.ifacol.2018.09.332.

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28

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 (2017): 2265–77. http://dx.doi.org/10.1109/tpel.2016.2557964.

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29

Patrikalakis, N. M., and P. V. Prakash. "Free-Form Plate Modeling Using Offset Surfaces." Journal of Offshore Mechanics and Arctic Engineering 110, no. 3 (1988): 287–94. http://dx.doi.org/10.1115/1.3257064.

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This paper addresses the representation of plates within the framework of the Boundary Representation method in a Solid Modeling environment. Plates are defined as the volume bounded by a progenitor surface, its offset surface and other, possibly ruled surfaces for the sides. Offset surfaces of polynomial parametric surfaces cannot be represented exactly within the same class of functions describing the progenitor surface. Therefore, if the offset surface is to be represented in the same form as the progenitor surface, approximation is required. A method of approximation relevant to non-uniform rational parametric B-spline surfaces is described. The method employs the properties of the control polyhedron and a recently developed subdivision algorithm to satisfy a certain accuracy criterion. Representative examples are given which illustrate the efficiency and robustness of the proposed method.
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30

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 (2012): 1997–2007. http://dx.doi.org/10.1016/j.jprocont.2012.08.007.

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31

Colonnese, Stefania, Stefano Rinauro, Gianpiero Panci, and Gaetano Scarano. "Gain-Control-Free Blind Carrier Frequency Offset Acquisition for QAM Constellations." IEEE Transactions on Signal Processing 58, no. 1 (2010): 349–61. http://dx.doi.org/10.1109/tsp.2009.2028971.

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32

Tatjewski, Piotr. "Offset-free nonlinear Model Predictive Control with state-space process models." Archives of Control Sciences 27, no. 4 (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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33

Ding, Baocang, Jun Wang, and Xiaoming Tang. "MPC-Based Offset-Free Tracking Control for Intermittent Transonic Wind Tunnel." IEEE Access 8 (2020): 46909–16. http://dx.doi.org/10.1109/access.2020.2977047.

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34

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 (2012): 444–63. http://dx.doi.org/10.1002/acs.2367.

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35

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

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36

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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37

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 (2015): 6194–203. http://dx.doi.org/10.1109/tie.2015.2431635.

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38

OHHIRA, Takashi, and Akira SHIMADA. "Offset-free tracking movement control based on model predictive control with disturbance suppression using disturbance observer." Transactions of the JSME (in Japanese) 83, no. 856 (2017): 17–00276. http://dx.doi.org/10.1299/transjsme.17-00276.

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39

Bianchi, M., A. van der Maas, E. Maljaars, and W. P. M. H. Heemels. "Offset-Free MPC for Resource Sharing on a Nonlinear SCARA Robot." IFAC-PapersOnLine 51, no. 20 (2018): 265–72. http://dx.doi.org/10.1016/j.ifacol.2018.11.024.

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40

Vaccari, Marco, Federico Pelagagge, Dominique Bonvin, and Gabriele Pannocchia. "Estimation technique for offset-free economic MPC based on modifier adaptation." IFAC-PapersOnLine 53, no. 2 (2020): 11251–56. http://dx.doi.org/10.1016/j.ifacol.2020.12.357.

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41

Choi, Kyunghwan, Dong Soo Kim, and Seok-Kyoon Kim. "Disturbance Observer-Based Offset-Free Global Tracking Control for Input-Constrained LTI Systems with DC/DC Buck Converter Applications." Energies 13, no. 16 (2020): 4079. http://dx.doi.org/10.3390/en13164079.

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This paper presents an offset-free global tracking control algorithm for the input-constrained plants modeled as controllable and open-loop strictly stable linear time invariant (LTI) systems. The contribution of this study is two-fold: First, a global tracking control law is devised in such a way that it not only leads to offset-free reference tracking but also handles the input constraints using the invariance property of a projection operator embedded in the proposed disturbance observer (DOB). Second, the offset-free tracking property is guaranteed against uncertainties caused by plant-model mismatch using the DOB’s integral action for the state estimation error. Simulation results are given in order to demonstrate the effectiveness of the proposed method by applying it to a DC/DC buck converter.
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42

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 (2010): 7882–90. http://dx.doi.org/10.1021/ie901945y.

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43

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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44

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

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45

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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46

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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47

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 (2017): 1682–94. http://dx.doi.org/10.1109/tase.2017.2700407.

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48

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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49

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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50

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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