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Journal articles on the topic 'Model predictive control tuning'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

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 (2020): 1224–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201597.

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2

Alhajeri, Mohammed, and Masoud Soroush. "Tuning Guidelines for Model-Predictive Control." Industrial & Engineering Chemistry Research 59, no. 10 (2020): 4177–91. http://dx.doi.org/10.1021/acs.iecr.9b05931.

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3

Garriga, Jorge L., and Masoud Soroush. "Model Predictive Control Tuning Methods: A Review." Industrial & Engineering Chemistry Research 49, no. 8 (2010): 3505–15. http://dx.doi.org/10.1021/ie900323c.

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4

Trierweiler, J. O., and L. A. Farina. "RPN tuning strategy for model predictive control." Journal of Process Control 13, no. 7 (2003): 591–98. http://dx.doi.org/10.1016/s0959-1524(02)00093-8.

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5

Trierweiler, J. O., L. A. Farina, and R. G. Duraiski. "RPN tuning strategy for model predictive control." IFAC Proceedings Volumes 34, no. 25 (2001): 245–50. http://dx.doi.org/10.1016/s1474-6670(17)33831-4.

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6

Di Cairano, S., and A. Bemporad. "Model Predictive Control Tuning by Controller Matching." IEEE Transactions on Automatic Control 55, no. 1 (2010): 185–90. http://dx.doi.org/10.1109/tac.2009.2033838.

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7

Yamashita, André Shigueo, Paulo Martin Alexandre, Antonio Carlos Zanin, and Darci Odloak. "Reference trajectory tuning of model predictive control." Control Engineering Practice 50 (May 2016): 1–11. http://dx.doi.org/10.1016/j.conengprac.2016.02.003.

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8

Nebeluk, Robert, and Maciej Ławryńczuk. "Tuning of Multivariable Model Predictive Control for Industrial Tasks." Algorithms 14, no. 1 (2021): 10. http://dx.doi.org/10.3390/a14010010.

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This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of

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9

Belda, Květoslav. "On-line Parameter Tuning of Model Predictive Control." IFAC Proceedings Volumes 44, no. 1 (2011): 5489–94. http://dx.doi.org/10.3182/20110828-6-it-1002.00167.

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10

Hu, Haimin, Konstantinos Gatsis, Manfred Morari, and George J. Pappas. "Tuning Communication Latency for Distributed Model Predictive Control." IFAC-PapersOnLine 52, no. 20 (2019): 279–84. http://dx.doi.org/10.1016/j.ifacol.2019.12.194.

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11

Miller, Anna. "Ship Model Identification with Genetic Algorithm Tuning." Applied Sciences 11, no. 12 (2021): 5504. http://dx.doi.org/10.3390/app11125504.

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Modeling is the most important component in predictive controller design. It should predict outputs precisely and fast. Thus, it must be adequate for the ship dynamics while having as simple a structure as possible. In a good ship model the standard deviation of a particular coefficient should not exceed 10% of its value. Fitting the validation data to 80% for short-term prediction and 65% for long-term prediction is treated as a declared benchmark for model usage in ship course predictive controller. Regularization was proposed to ensure better state-space models to fit the real ship dynamics

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12

Bobál, Vladimír, Petr Chalupa, Marek Kubalčík, and Petr Dostál. "Self-Tuning Predictive Control of Nonlinear Servo-Motor." Journal of Electrical Engineering 61, no. 6 (2010): 365–72. http://dx.doi.org/10.2478/v10187-010-0056-x.

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Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization wa

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13

Yamashita, A. S., A. C. Zanin, and D. Odloak. "TUNING OF MODEL PREDICTIVE CONTROL WITH MULTI-OBJECTIVE OPTIMIZATION." Brazilian Journal of Chemical Engineering 33, no. 2 (2016): 333–46. http://dx.doi.org/10.1590/0104-6632.20160332s20140212.

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14

Huzmezan, M., and J. M. Maciejowski. "Automatic Tuning for Model Based Predictive Control During Reconfiguration." IFAC Proceedings Volumes 31, no. 21 (1998): 237–42. http://dx.doi.org/10.1016/s1474-6670(17)41083-4.

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15

Fontes, Raony M., Márcio A. F. Martins, and Darci Odloak. "An Automatic Tuning Method for Model Predictive Control Strategies." Industrial & Engineering Chemistry Research 58, no. 47 (2019): 21602–13. http://dx.doi.org/10.1021/acs.iecr.9b03502.

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16

He, Ning, Dawei Shi, Jiadong Wang, Michael Forbes, Johan Backström, and Tongwen Chen. "Automated Two-Degree-of-Freedom Model Predictive Control Tuning." Industrial & Engineering Chemistry Research 54, no. 43 (2015): 10811–24. http://dx.doi.org/10.1021/acs.iecr.5b02569.

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17

Shridhar, Rahul, and Douglas J. Cooper. "A Tuning Strategy for Unconstrained SISO Model Predictive Control." Industrial & Engineering Chemistry Research 36, no. 3 (1997): 729–46. http://dx.doi.org/10.1021/ie9604280.

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18

Shridhar, Rahul, and Douglas J. Cooper. "A Tuning Strategy for Unconstrained Multivariable Model Predictive Control." Industrial & Engineering Chemistry Research 37, no. 10 (1998): 4003–16. http://dx.doi.org/10.1021/ie980202s.

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19

Shridhar, Rahul, and Douglas J. Cooper. "A novel tuning strategy for multivariable model predictive control." ISA Transactions 36, no. 4 (1997): 273–80. http://dx.doi.org/10.1016/s0019-0578(97)00036-0.

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20

Giraldo, Sergio A. C., Príamo A. Melo, and Argimiro R. Secchi. "Tuning of Model Predictive Control Based on Hybrid Optimization." IFAC-PapersOnLine 52, no. 1 (2019): 136–41. http://dx.doi.org/10.1016/j.ifacol.2019.06.050.

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21

Müller, Matthias A., David Angeli, and Frank Allgöwer. "Economic model predictive control with self-tuning terminal cost." European Journal of Control 19, no. 5 (2013): 408–16. http://dx.doi.org/10.1016/j.ejcon.2013.05.019.

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22

Qian, Zheng Zai, Gong Cai Xin, and Jin Niu Tao. "Predictive Control Based on Fuzzy Expert PID Tuning Control." Advanced Materials Research 466-467 (February 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1207.

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In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-l

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23

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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24

Tran, Quang N., Joni Scholten, Leyla Ozkan, and A. C. P. M. Backx. "A model-free approach for auto-tuning of model predictive control." IFAC Proceedings Volumes 47, no. 3 (2014): 2189–94. http://dx.doi.org/10.3182/20140824-6-za-1003.01494.

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25

SOTNIKOVA, MARGARITA. "PLASMA STABILIZATION BASED ON MODEL PREDICTIVE CONTROL." International Journal of Modern Physics A 24, no. 05 (2009): 999–1008. http://dx.doi.org/10.1142/s0217751x09044450.

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The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demo

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26

TANGE, Yoshio, and Chikashi NAKAZAWA. "Optimal Tuning for Disturbance Suppression Mechanism for Model Predictive Control." Transactions of the Society of Instrument and Control Engineers 47, no. 9 (2011): 380–87. http://dx.doi.org/10.9746/sicetr.47.380.

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27

Bachtiar, Vincent, Chris Manzie, and Eric C. Kerrigan. "Nonlinear Model-Predictive Integrated Missile Control and Its Multiobjective Tuning." Journal of Guidance, Control, and Dynamics 40, no. 11 (2017): 2961–70. http://dx.doi.org/10.2514/1.g002279.

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28

Yan, Jun, Eranda Harinath, and Guy Dumont. "Tuning and Identification for Model Predictive Control: An Iterative Approach." IFAC Proceedings Volumes 42, no. 10 (2009): 1062–67. http://dx.doi.org/10.3182/20090706-3-fr-2004.00176.

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29

Maekawa, Sari, Taiga Suzuki, and Ryona Koashi. "Model Predictive Sensorless Control for PMSM Without Estimator Gain Tuning." IEEJ Transactions on Industry Applications 140, no. 12 (2020): 929–38. http://dx.doi.org/10.1541/ieejias.140.929.

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30

Geyer, Tobias. "Algebraic Tuning Guidelines for Model Predictive Torque and Flux Control." IEEE Transactions on Industry Applications 54, no. 5 (2018): 4464–75. http://dx.doi.org/10.1109/tia.2018.2835375.

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31

Yamashita, André Shigueo, Antonio Carlos Zanin, and Darci Odloak. "Tuning the Model Predictive Control of a Crude Distillation Unit." ISA Transactions 60 (January 2016): 178–90. http://dx.doi.org/10.1016/j.isatra.2015.10.017.

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32

Nascimento, Tiago P., Luis F. S. Costa, André G. S. Conceição, and António Paulo Moreira. "Nonlinear Model Predictive Formation Control: An Iterative Weighted Tuning Approach." Journal of Intelligent & Robotic Systems 80, no. 3-4 (2015): 441–54. http://dx.doi.org/10.1007/s10846-015-0183-5.

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33

Yu, Xiao Li. "Simulation and Research for Generalized Predictive Control." Advanced Materials Research 694-697 (May 2013): 2205–10. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2205.

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This paper presents analysis and experiments for Generalized Predictive Control (GPC) algorithm based on software simulation. First, we illustrate the time invariant GPC algorithm in detail. Then, we describe the principle for the control parameter selection of GPC based on empirical results. The Recursive Least Square (RLS) algorithm will be used to identify model parameters in the self-tuning GPC. The performance of GPC algorithm is validated by simulation results, which show that the algorithm has rapid and accurate dynamic responses for input signals, such as step signal and square wave. W

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34

Odgaaard, Peter Fogh, and Tobias Gybel Hovgaard. "On Practical tuning of Model Uncertainty in Wind Turbine Model Predictive Control." IFAC-PapersOnLine 48, no. 30 (2015): 327–32. http://dx.doi.org/10.1016/j.ifacol.2015.12.399.

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35

He, Bin, Da Peng Jiang, Guo Cheng Zhang, and Ying Hao Zhang. "Research on Typical Methods of S Surface Controller Parameter Self-Tuning for Underwater Vehicles." Applied Mechanics and Materials 365-366 (August 2013): 897–904. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.897.

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S surface control is a simple and operative motion control algorithm for underwater vehicles, but it has two parameters requiring to be adjusted manually. In order to enhance the adaptability of S surface controller, the research of S surface controller parameter self-tuning methods based on rules and models is carried out. Firstly, combined with fuzzy control, parameter self-tuning method based on fuzzy rules is presented. Then by means of predictive control theory, model-based parameter self-tuning method is proposed. By introducing the nonlinear autoregressive moving average model, the pred

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36

Ahmad, A., and A. Wahid. "APPLICATION OF MODEL PREDICTIVE CONTROL (MPC) TUNING STRATEGY IN MULTIVARIABLE CONTROL OF DISTILLATION COLUMN." Reaktor 11, no. 2 (2007): 66. http://dx.doi.org/10.14710/reaktor.11.2.66-70.

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A model predictive control strategy is proposed for multivariable nonlinear control problem in a distillation column. The aim is to provide a solution to nonlinear control problem that is favorable in terms of industrial implementation. The scheme utilizes multiple linear models to cover wider range of operating conditions. Depending on the operating conditions, suitable model is used in control computations. Servo and regulatory controls of the system are examined. Comparisons are made to conventional controllers. The results confirmed the potentials of the proposed strategy.

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37

Bouzoualegh, Samir, El-Hadi Guechi, and Ridha Kelaiaia. "Model Predictive Control of a Differential-Drive Mobile Robot." Acta Universitatis Sapientiae Electrical and Mechanical Engineering 10, no. 1 (2018): 20–41. http://dx.doi.org/10.2478/auseme-2018-0002.

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Abstract This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided

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38

Henmi, Tomohiro. "Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing." Journal of Robotics and Mechatronics 28, no. 5 (2016): 695–701. http://dx.doi.org/10.20965/jrm.2016.p0695.

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[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptiv

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39

Cho, Hancheol, Giorgio Bacelli, and Ryan G. Coe. "Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter." Energies 12, no. 21 (2019): 4158. http://dx.doi.org/10.3390/en12214158.

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This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimizat

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40

Abrashov, Sergey, Tudor Bogdan Airimitoaie, Patrick Lanusse, et al. "Model Predictive Control Tuning: Methods and Issues. Application to steering wheel position control." IFAC-PapersOnLine 50, no. 1 (2017): 11331–36. http://dx.doi.org/10.1016/j.ifacol.2017.08.1668.

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41

Maass, Alejandro I., Chris Manzie, Iman Shames, et al. "Tuning of model predictive engine controllers over transient drive cycles." IFAC-PapersOnLine 53, no. 2 (2020): 14022–27. http://dx.doi.org/10.1016/j.ifacol.2020.12.923.

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42

Ali, Emad, and Evanghelos Zafiriou. "Optimization-based tuning of nonlinear model predictive control with state estimation." Journal of Process Control 3, no. 2 (1993): 97–107. http://dx.doi.org/10.1016/0959-1524(93)80005-v.

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43

Mahramian, Mehran, Hassan Taheri, and Mohammad Haeri. "On tuning and complexity of an adaptive model predictive control scheduler." Control Engineering Practice 15, no. 9 (2007): 1169–78. http://dx.doi.org/10.1016/j.conengprac.2007.02.002.

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44

Bagheri, Peyman. "Pole‐zero assignment in model predictive control, using analytical tuning approach." Optimal Control Applications and Methods 42, no. 5 (2021): 1253–68. http://dx.doi.org/10.1002/oca.2724.

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45

Al-Ghazzawi, Ashraf, Emad Ali, Adnan Nouh, and Evanghelos Zafiriou. "On-line tuning strategy for model predictive controllers." Journal of Process Control 11, no. 3 (2001): 265–84. http://dx.doi.org/10.1016/s0959-1524(00)00033-0.

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46

Dettori, Stefano, Alessandro Maddaloni, Filippo Galli, et al. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control." Energies 14, no. 13 (2021): 3998. http://dx.doi.org/10.3390/en14133998.

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The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computat

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47

Luzi, M., M. Vaccarini, and M. Lemma. "A tuning methodology of Model Predictive Control design for energy efficient building thermal control." Journal of Building Engineering 21 (January 2019): 28–36. http://dx.doi.org/10.1016/j.jobe.2018.09.022.

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48

Wahid, Abdul, and Richi Adi. "MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM." SINERGI 20, no. 1 (2016): 14. http://dx.doi.org/10.22441/sinergi.2016.1.003.

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Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation

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49

Li, Feng Ling, Meng Xu, and Hui Liu. "Model Predictive Control of Filament Tension in Textile Winding Process." Advanced Materials Research 308-310 (August 2011): 2454–57. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.2454.

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Abstract. Yarn tension controlling at heart is tuning servo-motor speed on a restricted range. In this paper, we proposed an advanced model predictive control strategy to solving filament winding system time-delay problem. The application of controlled auto-regressive integrated moving-average (CARIMA) model was described the dynamic time-varying character. The parameters of CARIMA model were solved by recursive least squares parameter estimation method through input-output data. And using the receding optimization method acquire controlling amount in the future. The result of simulation shows

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50

Giwa, Saidat Olanipekun, Abel Adekanmi Adeyi, and Abdulwahab Giwa. "Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process." International Journal of Engineering Research in Africa 27 (December 2016): 95–110. http://dx.doi.org/10.4028/www.scientific.net/jera.27.95.

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Reactive distillation is a process that combines chemical reaction and separation in a single piece of equipment (distillation column). The process has a lot of benefits especially for those reactions occurring at conditions suitable for the distillation of the process components, and these result in significant economic advantages. However, owing to the complexities resulting from the integration of reaction and separation, its control is still a challenge to process engineers because it requires a control method that is robust enough to handle its complexities. Therefore, in this work, model

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