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Journal articles on the topic 'Fuzzy model based multivariable predictive control'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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1

Nibiret, Getinet Asimare, and Abrham Tadesse Kassie. "Fuzzy Model Based Model Predictive Control for Biomass Boiler." International Journal of Engineering Research in Africa 71 (September 18, 2024): 93–108. http://dx.doi.org/10.4028/p-6uv4x4.

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In the realm of renewable energy, biomass plays a crucial role. A key component of power plants, the biomass boiler unit, is responsible for steam production. This unit operates as a nonlinear, highly coupled multivariable process. Traditional controllers used in the industry are ineffective for such systems. To address this, this paper presents a novel approach: a model predictive controller designed for biomass boiler plants. Fuzzy modelling, employed to approximate nonlinear functions to linear ones, is used for system identification. The methodology is implemented using MATLAB/Simulink and the Fuzzy modelling and identification (FMID) toolbox, utilizing input-output data from the Wenji-Shoa sugar factory for fuzzy model identification. The proposed controller demonstrates significant improvements, achieving settling times of 7.5, 13, and 7 seconds, with acceptable overshoots of 0.5%, 0.39%, and 0.46% for pressure, temperature, and level, respectively, for MISO systems. In contrast, the MPC shows improved performance in MIMO systems compared to MISO systems, with settling times of 5, 4, and 7 seconds, while the overshoot is reduced only for the pressure output, with 0.214%.

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2

Preglej, Aleksander, Jakob Rehrl, Daniel Schwingshackl, Igor Steiner, Martin Horn, and Igor Škrjanc. "Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system." Energy and Buildings 82 (October 2014): 520–33. http://dx.doi.org/10.1016/j.enbuild.2014.07.042.

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3

Huaguang, Zhang, Lilong Cai, and Zeungnam Bien. "A multivariable generalized predictive control approach based on T–S fuzzy model." Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 9, no. 3-4 (2000): 169–89. https://doi.org/10.3233/ifs-2000-125.

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4

Jeronymo, Daniel Cavalcanti, and Antonio Augusto Rodrigues Coelho. "Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models." PLOS ONE 11, no. 9 (2016): e0163116. http://dx.doi.org/10.1371/journal.pone.0163116.

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5

Bououden, S., M. Chadli, S. Filali, and A. El Hajjaji. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach." Renewable Energy 37, no. 1 (2012): 434–39. http://dx.doi.org/10.1016/j.renene.2011.06.025.

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6

Che, Yinping, Zhonggai Zhao, Zhiguo Wang, and Fei Liu. "Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model." Journal of Process Control 119 (November 2022): 1–12. http://dx.doi.org/10.1016/j.jprocont.2022.09.005.

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7

Vallejo LLamas, Pedro M., and Pastora Vega. "Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes." Complexity 2019 (January 3, 2019): 1–29. http://dx.doi.org/10.1155/2019/5720185.

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A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

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8

B., Babes, Hamouda N., Kahla S., Amar H., and S. M. Ghoneim S. "Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators." Electrical Engineering & Electromechanics, no. 3 (May 30, 2022): 51–62. https://doi.org/10.20998/2074-272X.2022.3.08.

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<strong><em>Introduction. </em></strong><em>A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. <strong>The novelty. </strong>The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose.</em> <em>Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement.</em> Methods. <em>In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.</em>

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9

Vallejo LLamas, Pedro M., and Pastora Vega. "Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes." Processes 9, no. 3 (2021): 531. http://dx.doi.org/10.3390/pr9030531.

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This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed.

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10

Babes, B., N. Hamouda, S. Kahla, H. Amar, and S. S. M. Ghoneim. "Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators." Electrical Engineering & Electromechanics, no. 3 (May 30, 2022): 51–62. http://dx.doi.org/10.20998/2074-272x.2022.3.08.

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Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

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11

Rosen, C., M. Larsson, U. Jeppson, and Z. Yuan. "A framework for extreme-event control in wastewater treatment." Water Science and Technology 45, no. 4-5 (2002): 299–308. http://dx.doi.org/10.2166/wst.2002.0610.

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In this paper an approach to extreme event control in wastewater treatment plant operation by use of automatic supervisory control is discussed. The framework presented is based on the fact that different operational conditions manifest themselves as clusters in a multivariate measurement space. These clusters are identified and linked to specific and corresponding events by use of principal component analysis and fuzzy c-means clustering. A reduced system model is assigned to each type of extreme event and used to calculate appropriate local controller set points. In earlier work we have shown that this approach is applicable to wastewater treatment control using look-up tables to determine current set points. In this work we focus on the automatic determination of appropriate set points by use of steady state and dynamic predictions. The performance of a relatively simple steady-state supervisory controller is compared with that of a model predictive supervisory controller. Also, a look-up table approach is included in the comparison, as it provides a simple and robust alternative to the steady-state and model predictive controllers. The methodology is illustrated in a simulation study.

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12

Dolgiy, Aleksandr, and Sergey Kovalev. "Automatic transportation process control systems with an extended data analytics circuit." Transport automation research 10, no. 4 (2024): 337–59. https://doi.org/10.20295/2412-9186-2024-10-04-337-359.

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A new hybrid approach has been proposed to automate the management of complex technological processes at railway stations of industrial transport using intelligent monitoring technologies. This approach is based on the concept of predictive modeling combined with methods of statistical analysis, including a modification of the principal components analysis method for multivariate statistical analysis and the identification of violations in technological processes using a combination of well-known methods such as contribution analysis and fuzzy dynamic analysis. The principal feature of the hybrid approach is mapping the initial space of numerical parameters of the technological process onto a new space formed by fuzzy rules of an evolving system model. Applying multivariate analysis to new system variables using the principal component method allows for the formation of a few intermediate variables with different degrees of granularity and interpretability, describing the behavior of the controlled process, which makes it possible to develop mathematical models and algorithms for solving various monitoring tasks An example of using this approach for post-processing monitoring data to identify performance discrepancies in a marshalling yard and anomalies in the controlled process is considered.

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13

Jin, Hua, Gang Meng, Yuanzhi Pan, Xing Zhang, and Changda Wang. "An Improved Intelligent Control System for Temperature and Humidity in a Pig House." Agriculture 12, no. 12 (2022): 1987. http://dx.doi.org/10.3390/agriculture12121987.

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The temperature and humidity control of a pig house is a complex multivariable control problem. How to keep the temperature and humidity in a pig house within a normal range is the problem to be solved in this paper. The traditional threshold-based environmental control system cannot meet this requirement. In this paper, an intelligent control system of temperature and humidity in a pig house based on machine learning and a fuzzy control algorithm is proposed. We use sensors to collect the temperature and humidity in the pig house and store these data in chronological order. Then, we use these time series data to train the GRU model and then use the GRU model to predict the temperature and humidity change curve in the pig house in the next 24 hours. Finally, the mathematical model of the pig house and related equipment is established, and the output power of the related equipment is calculated based on the prediction results of GRU so as to effectively regulate the indoor temperature and humidity. The experimental results show that compared with the threshold-based environmental control system, our system reduces the abnormal temperature and humidity by about 90%.

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14

Wu, Shuoming, Xiangbao Yang, Xie Qiu, and Yinpeng Pan. "Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer." Journal of Sensors 2022 (September 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/4420717.

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This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C -means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly ( P < 0.05 ). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher ( P < 0.05 ). The survival time of high-risk EC patients was much shorter than that of low-risk patients ( P < 0.05 ). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients ( P < 0.05 ). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.

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15

Núñez, Alfredo, Doris Sáez, Simon Oblak, and Igor Škrjanc. "Fuzzy-model-based hybrid predictive control." ISA Transactions 48, no. 1 (2009): 24–31. http://dx.doi.org/10.1016/j.isatra.2008.10.007.

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16

Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.

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17

Abonyi, János, Lajos Nagy, and Ferenc Szeifert. "Fuzzy model-based predictive control by instantaneous linearization." Fuzzy Sets and Systems 120, no. 1 (2001): 109–22. http://dx.doi.org/10.1016/s0165-0114(99)00118-9.

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18

Belarbi, Khaled, and FayÇal Megri. "A Stable Model-Based Fuzzy Predictive Control Based on Fuzzy Dynamic Programming." IEEE Transactions on Fuzzy Systems 15, no. 4 (2007): 746–54. http://dx.doi.org/10.1109/tfuzz.2006.890656.

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19

Moro, T. L., and N. Bonavita. "DCS Based Multivariable Model-Predictive Controller Algorithms." Measurement and Control 30, no. 6 (1997): 177–80. http://dx.doi.org/10.1177/002029409703000606.

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20

Adhemar de, B. Fontes, André Laurindo Maitelli, and Anderson Luiz de Oliveira Cavalcanti. "GENERALIZED PREDICTIVE CONTROL BASED IN MULTIVARIABLE BILINEAR MULTI-MODEL." IFAC Proceedings Volumes 40, no. 5 (2007): 89–94. http://dx.doi.org/10.3182/20070606-3-mx-2915.00133.

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21

Wen, Shuhuan, Jianhua Chen, Guiqian Qin, Qiguang Zhu, and Hongbin Wang. "An improved fuzzy model predictive control algorithm based on the force/position control structure of the five-degree of freedom redundant actuation parallel robot." International Journal of Advanced Robotic Systems 15, no. 5 (2018): 172988141880497. http://dx.doi.org/10.1177/1729881418804979.

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In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.

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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-linearity. Simulation results have also been illustrated. It shows that the proposed expert PID-like controller performed well than generally used PID.

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23

Masero, Eva, Mario Francisco, José M. Maestre, Silvana Revollar, and Pastora Vega. "Hierarchical distributed model predictive control based on fuzzy negotiation." Expert Systems with Applications 176 (August 2021): 114836. http://dx.doi.org/10.1016/j.eswa.2021.114836.

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24

Xu, Li, and Fei Liu. "Fuzzy Based Model Predictive Control for Uncertain Nonlinear System." Advanced Materials Research 383-390 (November 2011): 2404–10. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2404.

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In this paper, a model predictive control (MPC) scheme is investigated for uncertain nonlinear system with time delay and input constraint. First, the Takagi-Sugeno (T-S) fuzzy model is used to approximate the dynamics of nonlinear processes and the parallel distributed compensation (PDC) controllers which are parameter dependent and mirror the structure of the T-S plant model are proposed. Then a novel feedback PDC predictive controller obtained from the linear matrix inequality (LMI) solutions which can guarantee the stability of the closed-loop overall fuzzy system is put forward. Finally, a numerical example is provided to demonstrate the effectiveness and feasibility of the proposed method.

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25

Mahfouf, M., S. Kandiah, and D. A. Linkens. "Adaptive estimation for fuzzy TSK model-based predictive control." Transactions of the Institute of Measurement and Control 23, no. 1 (2001): 31–50. http://dx.doi.org/10.1177/014233120102300103.

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26

Fischer, Martin, Oliver Nelles, and Rolf Isermann. "Fuzzy Model-Based Predictive Control of a Heat Exchanger." IFAC Proceedings Volumes 30, no. 25 (1997): 409–14. http://dx.doi.org/10.1016/s1474-6670(17)41357-7.

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27

Nascimento Lima, Nádson M., Flavio Manenti, Rubens Maciel Filho, Marcelo Embiruçu, and Maria R. Wolf Maciel. "Fuzzy Model-Based Predictive Hybrid Control of Polymerization Processes." Industrial & Engineering Chemistry Research 48, no. 18 (2009): 8542–50. http://dx.doi.org/10.1021/ie900352d.

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28

Linkens, D. A., S. Kandiah, and M. Mahfouf. "Adaptive Estimation for Fuzzy TSK Model-Based Predictive Control." IFAC Proceedings Volumes 31, no. 22 (1998): 195–200. http://dx.doi.org/10.1016/s1474-6670(17)35942-6.

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29

Roubos, J. A., S. Mollov, R. Babuška, and H. B. Verbruggen. "Fuzzy model-based predictive control using Takagi–Sugeno models." International Journal of Approximate Reasoning 22, no. 1-2 (1999): 3–30. http://dx.doi.org/10.1016/s0888-613x(99)00020-1.

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Mahfouf, M., S. Kandiah, and D. A. Linkens. "Adaptive estimation for fuzzy TSK model-based predictive control." Transactions of the Institute of Measurement and Control 23, no. 1 (2001): 31–50. http://dx.doi.org/10.1191/014233101673920701.

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31

Zhao, Dong, Shuyan Sun, Ardashir Mohammadzadeh, and Amir Mosavi. "Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency." Sustainability 14, no. 18 (2022): 11772. http://dx.doi.org/10.3390/su141811772.

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In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results.

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32

Peng, Hui. "MULTIVARIABLE RBF-ARX MODEL-BASED PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS." IFAC Proceedings Volumes 40, no. 12 (2007): 882–87. http://dx.doi.org/10.3182/20070822-3-za-2920.00146.

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33

Huang, Chang Yuan, and Hai Peng Pan. "Practical Research on Predictive Fuzzy-PID Control in Reactor Temperature Control." Applied Mechanics and Materials 313-314 (March 2013): 355–58. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.355.

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Against the characteristics of the temperature in reactor such as time-delay, time-varying and difficulty to build a precise mathematical model in the chemical industry. Through the analysis of dynamic characteristics of the controlled object, the method of fuzzy-PID control was designed based on a predictive model. According to the detected temperature signal, the output deviation of the controller and the on-line identification of prediction model, this algorithm gains the predictive value which uses a generalized predictive model and the fuzzy-PID control. Then compare the predictive value with the reference trajectory to get the deviation. Finally use this deviation and the change of the deviation to optimize the PID control parameters and attain the appropriate amount of system control. The simulation results show that the fuzzy-PID control based on prediction model has strong adaptability, good robustness, control accuracy and higher practical value.

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34

Romanov, Anton A., Aleksey A. Filippov, and Nadezhda G. Yarushkina. "Adaptive Fuzzy Predictive Approach in Control." Mathematics 11, no. 4 (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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Wang, Shu, Xiaohui Xu, Tao Song, Xizhe Li, and Weilong Zhu. "Control strategy for power factor correction circuits based on model predictive control." Journal of Physics: Conference Series 2741, no. 1 (2024): 012064. http://dx.doi.org/10.1088/1742-6596/2741/1/012064.

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Abstract Power factor correction circuits are being used more extensively, and with that, the requirements and standards have become increasingly stringent. To improve the power factor of the circuit, thereby improving its operational efficiency and mitigating the load effects in boost power factor correction circuits, reducing Total Harmonics Distortion is crucial for improving the quality of the current. An improvement upon the conventional dual-loop PID control algorithm has been proposed. It involves integrating Model Predictive Control into the current loop design and incorporates a Luenberger observer. Model Predictive Control methods can be applied for the control of multivariable systems, especially in complex multivariable systems, as they exhibit strong robustness. Finally, by performing both simulation and experimental validations and analyzing the data results, it is shown that under full load conditions, the power factor λ is 0.968, the efficiency η is 91.11%, the voltage total harmonic distortion Uthd is 1.88%, and the current total harmonic distortion Ithd is 7.04%. These data results strongly confirm the performance of the control mechanism proposed in this paper.

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Yang, Weilin, Wentao Zhang, Dezhi Xu, and Wenxu Yan. "Fuzzy model predictive control for 2-DOF robotic arms." Assembly Automation 38, no. 5 (2018): 568–75. http://dx.doi.org/10.1108/aa-11-2017-162.

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Purpose Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However, it is usually nontrivial to tune the parameters in a PID controller. This paper aims to propose a model-based control strategy of robotic arms. Design/methodology/approach A Takagi–Sugeno (T-S) fuzzy model, which is capable of approximating nonlinear systems, is used to describe the dynamics of a robotic arm. Model predictive control (MPC) based on the T-S fuzzy model is considered, which optimizes system performance with respect to a user-defined cost function. Findings The control gains are optimized online according to the real-time system state. Furthermore, the proposed method takes into account the input constraints. Simulations demonstrate the effectiveness of the fuzzy MPC approach. It is shown that asymptotic stability is achieved for the closed-loop control system. Originality/value The T-S fuzzy model is discussed in the modeling of robotic arm dynamics. Fuzzy MPC is used for robotic arm control, which can optimize the transient performance with respect to a user-defined criteria.

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Yang, Libo, Mei Guo, Ardashir Mohammadzadeh, and Amir Mosavi. "Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots." Mathematics 10, no. 14 (2022): 2498. http://dx.doi.org/10.3390/math10142498.

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In this paper, a new hybrid method for controlling a wheeled robot is introduced. Model predictive control (MPC) is the main controller and a fuzzy controller is used as a compensator. The wheeled robot is a nonlinear, multi-input–multi-output system that requires new and combined methods for precise control. In order to stabilize the system the appropriate control input is set, and at the same time, attention is paid to the reference signal tracking. In the simulation section, several different scenarios are applied and parameter uncertainties and their effects on the controller’s performance are evaluated. The simulation results show the success and efficiency of the proposed method.

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Eliasi, H., H. Davilu, and M. B. Menhaj. "Adaptive fuzzy model based predictive control of nuclear steam generators." Nuclear Engineering and Design 237, no. 6 (2007): 668–76. http://dx.doi.org/10.1016/j.nucengdes.2006.08.007.

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Yahya, Olfa, Zeineb Lassoued, and Kamel Abderrahim. "Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model." International Journal of Automation and Computing 16, no. 5 (2018): 683–95. http://dx.doi.org/10.1007/s11633-018-1148-5.

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HE, M., W. CAI, and S. LI. "Multiple fuzzy model-based temperature predictive control for HVAC systems." Information Sciences 169, no. 1-2 (2005): 155–74. http://dx.doi.org/10.1016/j.ins.2004.02.016.

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Adjisetya, Muhammad, and Abdul Wahid. "Multivariable Model Predictive Control to Control Bio-H2 Production from Biomass." ChemEngineering 7, no. 1 (2023): 7. http://dx.doi.org/10.3390/chemengineering7010007.

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Two significant units in biomass-based hydrogen plants are the compressor and steam reformer. The compressor works to achieve high pressure for further operations, while the steam reformer produces H2 gas. For the units to operate well against disturbances that may occur (regulatory control) or changes in the set point (servo control), as well as the interactions between the relevant process variables, a Multivariable Model Predictive Control (MMPC) is considered as a controller. The determination of MMPC parameters, including the sampling time (T), prediction horizon (P), and control horizon (M), is crucial for achieving such objectives. Therefore, in this study, MMPC parameter adjustment was performed. The Integral of Absolute Error (IAE) and Integral of Square Error (ISE) were used as control performance indicators. For comparison, we considered the IAE and ISE from the Single-Input Single-Output (SISO)-based Model Predictive Control (MPC) from previous research. As a result, the optimum MMPC parameters were found to be T = 1, P = 341, and M = 121 for the compressor unit, and T = 1, P = 45, and M = 21 for the steam reformer unit. The average increases in control performance (IAE and ISE), compared to the MPC (SISO) used in previous research, were 85.84% for compressor unit 1, 61.39% for compressor unit 2, 94.57% for compressor unit 3, and 73.35% for compressor unit 4. Meanwhile, in the steam reformer unit, the increases in control performance were 63.34% for the heater and 80.16% for the combustor.

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Mao, Jin, Lei Yang, Yuanbo Hu, Kai Liu, and Jinfu Du. "Research on Vehicle Adaptive Cruise Control Method Based on Fuzzy Model Predictive Control." Machines 9, no. 8 (2021): 160. http://dx.doi.org/10.3390/machines9080160.

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Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback.

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Tran, Tuan Quang, and Minh Xuan Phan. "MODEL PREDICTIVE CONTROL BASED ON FUZZY SYSTEM, APPLICATION FOR A CONTINUOUS STIRRED TANK REACTOR (CSTR)." Science and Technology Development Journal 13, no. 1 (2010): 16–23. http://dx.doi.org/10.32508/stdj.v13i1.2064.

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The paper presents one method to design the Model Predictive Controller based on Fuzzy Model. The Plant is simulated by Takagi-Sugeno Fuzzy Model and the Optimisation Problem is solved by the Genetic Algorithms. By using the Fuzzy Model and Genetic Algorithm this MPC gives better quality than the other General Predictive Controllers. The case study of a continuous stirred tank reactor (CSTR) control is presented in this paper.

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Kumar Sunori, Sandeep, Pradeep Kumar Juneja, and Anamika Bhatia Jain. "Model Predictive Control System Design for Boiler Turbine Process." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 1054. http://dx.doi.org/10.11591/ijece.v5i5.pp1054-1061.

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<p>MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In the present work the control of boiler turbine process with three manipulated variables namely fuel flow valve position, steam control valve position and feed water flow valve position and three controlled variables namely drum pressure, output power and drum water level deviation [8] has been attempted using MPC technique. Boiler turbine process is very complex and nonlinear multivariable process. A linearized model obtained using Taylor series expansion around operating point has been used.</p>

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Ho, Y. K., F. S. Mjalli, and H. K. Yeoh. "Multivariable Adaptive Predictive Model Based Control of a Biodiesel Transesterification Reactor." Journal of Applied Sciences 10, no. 12 (2010): 1019–27. http://dx.doi.org/10.3923/jas.2010.1019.1027.

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Benlahrache, Mohamed Abdelmoula, Sami Othman, and Nida Sheibat-Othman. "Multivariable model predictive control of wind turbines based on Laguerre functions." Wind Engineering 41, no. 6 (2017): 409–20. http://dx.doi.org/10.1177/0309524x17721997.

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Multivariable model predictive control of wind turbine was considered in this work in full-load regime in order to maximize the power generation and reduce the loads on the equipment. The controller has as inputs the pitch angle and the generator torque and accounts for constraints on both the inputs and the outputs. In order to reduce the computation time and improve the conditioning of the constrained optimization problem, the inputs were parameterization using Laguerre functions. The optimization objective was thus transformed from the identification of the input sequence over the control horizon to the identification of the Laguerre function parameters allowing a good parameterization of the inputs over the control horizon. Multiple linearizing models were employed in order to cover different operating points corresponding to variable wind speeds in the full-load regime zone.

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Sutil, Mario Franciso, Yeray Mezquita, Silvana Revollar, Pastora Vega, and Paz Juan francisco de. "Multi-agent distributed model predictive control with fuzzy negotiation." Expert Systems with Applications 129 (September 1, 2019): 68–83. https://doi.org/10.1016/j.eswa.2019.03.056.

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In this work, a multi-agent distributed model predictive control (DMPC) including fuzzy negotiation has been developed. A novel fuzzy inference system is introduced as a negotiation technique between agents in a cooperative game algorithm, allowing for the consideration of economic criteria and process constraints within the negotiation process, providing an easier interpretation of the available knowledge. The fuzzy negotiation produces smoother control actions than where the negotiation is based only on costs evaluation, because both agents provide their best to generate the final control action. The results show good tracking and disturbance rejection in the case study proposed. The methodology has been implemented in a JAVA based platform with a friendly user interface to deploy the multi-agent system (MAS), and it has been validated in the water level control in a four coupled tanks system.

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Mahfouf, M., D. A. Linkens, and M. F. Abbod. "Adaptive Fuzzy TSK Model-Based Predictive Control Using a Carima Model Structure." Chemical Engineering Research and Design 78, no. 4 (2000): 590–96. http://dx.doi.org/10.1205/026387600527527.

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Ji, Runmin, Xianghua Huang, and Xiaochun Zhao. "Active Jet Noise Control of Turbofan Engine Based on Explicit Model Predictive Control." Applied Sciences 12, no. 10 (2022): 4874. http://dx.doi.org/10.3390/app12104874.

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The active jet noise control received significant attention due to the little influence it has on the engine performance. The active jet noise control is a multivariable problem because it needs to achieve the simultaneous closed-loop control of jet noise and engine performance. Model predictive control (MPC) has great application potentials in the field of multivariable control of aero-engines, but the real-time performance of MPC is intractable. This paper proposed an active jet noise controller of a turbofan engine, based on explicit model predictive control (EMPC). An integrated model of turbofan engine and jet noise, which calculates the engine parameters and jet noise in real time, was established. The online computational burden of MPC was transferred to offline computation using multi-parametric quadratic programming (MPQP). To improve the efficiency of the online positioning algorithm, the sequence search method was replaced by a binary search tree. Step simulations were performed to test the effectiveness of the proposed controller. The results show that the proposed EMPC controller not only achieves the simultaneous control of jet noise and the turbofan engine, but also improve the real-time performance greatly.

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Yin, Fang Chen, Geng Sheng Ma, Ya Feng Ji, Jia Xue Yu, De Hao Gu, and Dian Hua Zhang. "Fuzzy Adaptive Direct Generalized Predictive Control Algorithm in the Application of AWC Control." Advanced Materials Research 945-949 (June 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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