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Journal articles on the topic 'Predictive control – Computer simulation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Tian, Zi De. "Algorithm and Implementation of Smith Predictive Control." Applied Mechanics and Materials 687-691 (November 2014): 60–63. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.60.

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With invariable time delay theoretically,Smith predictor is deeply studied,the fact that the Smith predictor depends on the math model of the system is found ,and so it is very difficult to control the time-varying delay system well. Therefore,it is necessary to take an effective method--using PID controller with the digital Smith predictor,and studying its control algorithm,and doing the simulation in the lab of micro-computer control. Simulation results have proved the efficiency of the algorithm and the validity.

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2

Tong, Ming Yu, Di Jian Xu, and Jin Liang Shi. "Furnace Temperature Control and Simulation." Applied Mechanics and Materials 556-562 (May 2014): 2492–95. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2492.

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with the rapid development of science and technology, in all fields of temperature control system of the precision, stability and other requirements of increasingly high, the control system is the myriads of changes. Computer measurement and control technology, the traditional electronic measuring changed dramatically in the principle, function, accuracy and automation degree, degree of automation of the scientific experiment and application engineering to improve. Temperature control key lies in the two aspects of temperature measurement and control. Temperature measurement is based on temper

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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, et al. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (2018): 3376. http://dx.doi.org/10.3390/en11123376.

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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with

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Brüdigam, Tim, Johannes Teutsch, Dirk Wollherr, Marion Leibold, and Martin Buss. "Probabilistic model predictive control for extended prediction horizons." at - Automatisierungstechnik 69, no. 9 (2021): 759–70. http://dx.doi.org/10.1515/auto-2021-0025.

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Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods

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Lu, Jialiang, Guanrong Chen, and Hao Ying. "Predictive fuzzy PID control: theory, design and simulation." Information Sciences 137, no. 1-4 (2001): 157–87. http://dx.doi.org/10.1016/s0020-0255(01)00119-0.

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Tang, Zhi Jie, Qing Bo He, Shu Ai Wang, and Jia Li Shen. "An Improved Generalized Predictive Control for AUV Yaw." Advanced Materials Research 490-495 (March 2012): 1709–13. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1709.

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In this paper, a new design for underwater vehicle yaw control is presented for nonlinear, large delay, time-varying systems. The horizontal stability controller combines generalized predictive control with FuzzyPID control method. The predictive function is introduced into the tradition FuzzyPID control through optimizing the performance index function of GPC. Computer simulation is provided for verification, and the results show this new controller has a better performance

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7

Yu, Kaijiang, Xiaozhuo Xu, Qing Liang, et al. "Model Predictive Control for Connected Hybrid Electric Vehicles." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/318025.

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This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV forPbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electri

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Li, Hongwei, Kaide Ren, Shuaibing Li, and Haiying Dong. "Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System." Energies 13, no. 6 (2020): 1329. http://dx.doi.org/10.3390/en13061329.

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To deal with the randomness and uncertainty of the wind power generation process, this paper proposes the use of the clustering method to complement the multi-model predictive control algorithm for active power control. Firstly, the fuzzy clustering algorithm is adopted to classify actual measured data; then, the forgetting factor recursive least square method is used to establish the multi-model of the system as the prediction model. Secondly, the model predictive controller is designed to use the measured wind speed as disturbance, the pitch angle as the control variable, and the active powe

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Lin, Fen, Yuke Chen, Youqun Zhao, and Shaobo Wang. "Path tracking of autonomous vehicle based on adaptive model predictive control." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141988008. http://dx.doi.org/10.1177/1729881419880089.

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimat

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Lee, Leng-Feng, and Brian R. Umberger. "Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB." PeerJ 4 (January 26, 2016): e1638. http://dx.doi.org/10.7717/peerj.1638.

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Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tool

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Sun, Zhe, Cheng Zhang, Jiawei Chen, Pingbo Tang, and Alper Yilmaz. "Predictive nuclear power plant outage control through computer vision and data-driven simulation." Progress in Nuclear Energy 127 (September 2020): 103448. http://dx.doi.org/10.1016/j.pnucene.2020.103448.

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Zhao, Xinxin, and Zhijun Li. "Data-Driven Predictive Control Applied to Gear Shifting for Heavy-Duty Vehicles." Energies 11, no. 8 (2018): 2139. http://dx.doi.org/10.3390/en11082139.

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In this paper, the data-driven predictive control method is applied to the clutch speed tracking control for the inertial phase of the shift process. While the clutch speed difference changes according to the predetermined trajectory, the purpose of improving the shift quality is achieved. The data-driven predictive control is implemented by combining the subspace identification with the model predictive control. Firstly, the predictive factors are constructed from the input and output data of the shift process via subspace identification, and then the factors are applied to a prediction equat

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Fnadi, Mohamed, and Julien Alexandre dit Sandretto. "Experimental Validation of a Guaranteed Nonlinear Model Predictive Control." Algorithms 14, no. 8 (2021): 248. http://dx.doi.org/10.3390/a14080248.

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This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several const

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14

Zeng, Xiaohong. "Optimization and Simulation of Generalized Predictive Control Based on Wavelet Transform." Information Technology Journal 12, no. 16 (2013): 3542–47. http://dx.doi.org/10.3923/itj.2013.3542.3547.

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15

Zhang, Xiao Yun. "MATLAB-Based Comparative Furnace Temperature Control Algorithm and Simulation." Applied Mechanics and Materials 494-495 (February 2014): 1335–38. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1335.

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Temperature control is critical temperature and the temperature of the two aspects. Temperature measurement is based on temperature control. However, due to the control object becomes more and more complex, temperature control, there are still many problems. In this paper, based on the use PID, Smith predictive control, Dalin algorithm comparative study of three algorithms for industrial resistance furnace temperature control system computer simulation design, and the use of simulation software MATLAB / SIMULINK simulation of the control algorithms, but also on advanced control algorithm is st

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Anuchin, Alecksey, Galina L. Demidova, Chen Hao, Alexandr Zharkov, Andrei Bogdanov, and Václav Šmídl. "Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables." Energies 13, no. 13 (2020): 3317. http://dx.doi.org/10.3390/en13133317.

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A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torq

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Liu, Fang, Haotian Li, Ling Liu, Runmin Zou, and Kangzhi Liu. "A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control." Mathematics 9, no. 7 (2021): 760. http://dx.doi.org/10.3390/math9070760.

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In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of t

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18

Khelafa, Ilyas, Abdelhakim Ballouk, and Abdenaceur Baghdad. "Control algorithm for the urban traffic using a realtime simulation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 3934. http://dx.doi.org/10.11591/ijece.v11i5.pp3934-3942.

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Many types of research have been interesting by real-time control of urban networks. This paper, basing on a simplified urban traffic model, proposes a novel control approach based on model predictive control concept to reduce congestion and improve the safety of cars on the roads. The contributions of this paper are: First, we consider vehicle heterogeneity, represented by a mathematical model called “S Model” and integrate it with a realtime simulator to evaluate the performance of controllers on real traffic conditions. Second, in order to assess each controller's success under particular c

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Zhang, Jianwei, Margarita Norambuena, Li Li, David Dorrell, and Jose Rodriguez. "Sequential Model Predictive Control of Three-Phase Direct Matrix Converter." Energies 12, no. 2 (2019): 214. http://dx.doi.org/10.3390/en12020214.

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The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting facto

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20

Balchen, Jens G., Dag Ljungqvist, and Stig Strand. "State-space predictive control." Modeling, Identification and Control: A Norwegian Research Bulletin 13, no. 2 (1992): 77–112. http://dx.doi.org/10.4173/mic.1992.2.2.

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21

Bao, GuangQing, WuGang Qi, and Ting He. "Direct Torque Control of PMSM with Modified Finite Set Model Predictive Control." Energies 13, no. 1 (2020): 234. http://dx.doi.org/10.3390/en13010234.

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A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weigh

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22

Gawronski, W. "Predictive Controller and Estimator for NASA Deep Space Network Antennas." Journal of Dynamic Systems, Measurement, and Control 116, no. 2 (1994): 241–48. http://dx.doi.org/10.1115/1.2899216.

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This paper presents a modified output prediction procedure, and a new controller design based on the predictive control law. Also, a predictive estimator is developed for implementing the controller. The predictive controller was designed and simulated for tracking control of the NASA Deep Space Network 70-m antenna. Simulation results show significant improvement in tracking performance compared to the linear quadratic controller and estimator presently in use.

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23

Lorini, Gabriele, Alessandro Miotti, and Riccardo Scattolini. "Modelling, simulation and predictive control of a spark ignition engine." International Journal of Modelling, Identification and Control 3, no. 3 (2008): 258. http://dx.doi.org/10.1504/ijmic.2008.020124.

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24

Felisiak, Piotr A., Krzysztof S. Sibilski, Kaiyu Qin, Gun Li, and Wiesław A. Wróblewski. "Nonlinear model predictive control of spacecraft relative motion." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 11 (2018): 3906–19. http://dx.doi.org/10.1177/0954410018807613.

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This investigation deals with the problem of spacecraft relative motion control, which is typically associated with the spacecraft rendezvous and proximity maneuvers. Relative position and linear velocity are considered. A distinguishing attribute of the presented approach is consideration of definitely larger relative distance between the satellites than it is commonly addressed in the literature. The presented control method is applicable in the case where the chief satellite moves in a known, highly elliptical orbit. A quasi-optimal control is found by a model predictive control algorithm,

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Ben Aicha, Faten, Faouzi Bouani, and Mekki Ksouri. "A multivariable multiobjective predictive controller." International Journal of Applied Mathematics and Computer Science 23, no. 1 (2013): 35–45. http://dx.doi.org/10.2478/amcs-2013-0004.

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Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MI

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Colin, G., Y. Chamaillard, G. Bloch, and A. Charlet. "Exact and Linearized Neural Predictive Control: A Turbocharged SI Engine Example." Journal of Dynamic Systems, Measurement, and Control 129, no. 4 (2007): 527–33. http://dx.doi.org/10.1115/1.2745881.

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This paper describes a real-time control method for non-linear systems based on model predictive control. The model used for the prediction is a neural network because of its ability to represent non-linear systems, its ability to be differentiated, and its simplicity of use. The feasibility and the performance of the method, based on on-line linearization, are demonstrated on a turbocharged spark-ignited engine application, where the simulation models used are very accurate and complex. The results, first in simulation and then on a test bench, show the implementation of the proposed control

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Liu, Zhilin, Linhe Zheng, and Guosheng Li. "Longitudinal motion control for high-speed trimaran based on computational fluid dynamics and predictive control." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092528. http://dx.doi.org/10.1177/1729881420925280.

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This article investigates a model predictive control (MPC) with disturbance observer (DOB) for a trimaran longitudinal motion control. Firstly, to design the trimaran longitudinal motion stability controller, the mathematical model of the trimaran requires to be obtained. The hydrodynamic coefficients in the mathematical model are obtained by the computational fluid dynamics simulation. Secondly, a T-foil with the fixed attack angle is selected as an antipitching appendage. It is verified that the T-foil is effective in restraining the longitudinal motion of the trimaran through numerical simu

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Chen, Juan, Yuxuan Yu, and Qi Guo. "Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control." Algorithms 12, no. 10 (2019): 220. http://dx.doi.org/10.3390/a12100220.

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This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissi

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Kacur, Jan, Patrik Flegner, Milan Durdan, and Marek Laciak. "Model Predictive Control of UCG: An Experiment and Simulation Study." Information Technology and Control 48, no. 4 (2019): 557–78. http://dx.doi.org/10.5755/j01.itc.48.4.23303.

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Underground coal gasification (UCG) is a potential technology that enables to mine coal without traditional mining equipment. The coal is gasified deep in underground and produced syngas is processed on the surface. The most important technical problem in UCG is unstable quality of syngas and control. This paper proposes advanced control based on an adaptive predictive controller. The maintaining of desired calorific value depends on flow rates of gasification agents injected to the underground geo-reactor and controlled exhaust. The paper proposes a physical model of UCG technology and applie

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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

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Li, Qiang, Cheng Zhi Yang, Wen Bo Zhang, and Yang Yu. "The Modeling and Control of Leaching Rate in Nickel Stir Leaching Process of Sulfuric Acid." Advanced Materials Research 485 (February 2012): 165–68. http://dx.doi.org/10.4028/www.scientific.net/amr.485.165.

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Leaching rate is one of the key parameters in the nickel stir leaching process of sulfuric acid and it is hard to online measure directly due to a lot of uncertain facts. In this paper, the prediction model of nickel leaching rate is established by least squares identification method. A controller combining predictive control(PFC) and PID control is designed to control nickel leaching rate in stir leaching process of sulfuric acid and better results of leaching rate control is proved by computer simulation.

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Khan, Hussain, Muhammad Aamir, Muhammad Ali, Asad Waqar, Syed Ali, and Junaid Imtiaz. "Finite Control Set Model Predictive Control for Parallel Connected Online UPS System under Unbalanced and Nonlinear Loads." Energies 12, no. 4 (2019): 581. http://dx.doi.org/10.3390/en12040581.

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In this paper, the finite control set model predictive control (FCS–MPC) technique-based controller is proposed for the inverter of the uninterrupted power supply (UPS) system. The proposed controller uses the mathematical model of the system to forecast the response of voltage for each possible switching state for every sampling instant. Following this, the cost function was used to determine the switching state, applied to the next sampling instant. First, the proposed control strategy was implemented for the single inverter of the UPS system. Finally, the droop control strategy was implemen

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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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Zhang, Tao, Qiang Hao, Zheng Zheng, and Chuang Lu. "An Electric Spring Control Strategy Based on Finite Control Set-Model Predictive Control." Journal Européen des Systèmes Automatisés 53, no. 4 (2020): 461–68. http://dx.doi.org/10.18280/jesa.530403.

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As a novel voltage control device, electric spring (ES) can effectively suppress the voltage fluctuations across critical loads (CLs), and solve the various problems with electrical quality induced by the grid access of renewable energy resources (RES). However, the traditional controllers for the ES system can no longer meet the control requirements, as the environment is complicated by the growing number of load-side nonlinear loads and uncertain disturbances. To solve the problem, this paper proposes a control system based on finite control set-model predictive control (FCS-MPC), and applie

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M. Dzhambov, Angel, Donka D. Dimitrova, and Tanya H. Turnovska. "Improving Traffic Noise Simulations Using Space Syntax: Preliminary Results from Two Roadway Systems." Archives of Industrial Hygiene and Toxicology 65, no. 3 (2014): 259–72. http://dx.doi.org/10.2478/10004-1254-65-2014-2469.

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AbstractNoise pollution is one of the four major pollutions in the world. In order to implement adequate strategies for noise control, assessment of traffic-generated noise is essential in city planning and management. The aim of this study was to determine whether space syntax could improve the predictive power of noise simulation. This paper reports a record linkage study which combined a documentary method with space syntax analysis. It analyses data about traffic flow as well as field-measured and computer-simulated traffic noise in two Bulgarian agglomerations. Our findings suggest that s

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Liu and Zhao. "Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch." Energies 12, no. 19 (2019): 3711. http://dx.doi.org/10.3390/en12193711.

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In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper

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Rodrigues, Reurison, Andre Murilo, Renato Vilela Lopes, and Luiz Carlos Gadelha De Souza. "Hardware in the Loop Simulation for Model Predictive Control Applied to Satellite Attitude Control." IEEE Access 7 (2019): 157401–16. http://dx.doi.org/10.1109/access.2019.2949731.

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Wang, Wei, and Rolf Henriksen. "Direct adaptive generalized predictive control." Modeling, Identification and Control: A Norwegian Research Bulletin 14, no. 4 (1993): 181–91. http://dx.doi.org/10.4173/mic.1993.4.1.

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Bulatov, Yuri, and Andrey Kryukov. "Study of cyber security of predictive control algorithms for distributed generation plants." Analysis and data processing systems, no. 2 (June 18, 2021): 19–34. http://dx.doi.org/10.17212/2782-2001-2021-2-19-34.

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The power industry is currently actively developing the field related to the use of distributed generation plants located near the power receiving devices of consumers. At the same time, the introduction of distributed generation plants causes a lot of engineering problems which need solutions. One of them is the optimization of the settings of automatic voltage regulators (AVR) and speed regulators (ASR) of synchronous generators in all possible operating modes. This requires the use of complex models of power supply systems, distributed generation plants and their regulators, as well as labo

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Ahmed, N. U., and Hui Song. "Real-time feedback control of computer networks based on predicted state estimation." Mathematical Problems in Engineering 2005, no. 1 (2005): 7–32. http://dx.doi.org/10.1155/mpe.2005.7.

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We present a real-time feedback control strategy to optimize the dynamic performance of a computer communication network. In previous studies closely related to this topic, feedback delay, arising from communication delay, was shown to degrade system performance. Considering this negative impact of delay, we propose a new control law which predicts the traffic in advance and exercises control based on the predicted traffic. We demonstrate through simulation experiments that the predictive feedback control law substantially improves the system performance.

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Hansen, Anders H., Magnus F. Asmussen, and Michael M. Bech. "Hardware-in-the-Loop Validation of Model Predictive Control of a Discrete Fluid Power Power Take-Off System for Wave Energy Converters." Energies 12, no. 19 (2019): 3668. http://dx.doi.org/10.3390/en12193668.

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Model predictive control based wave power extraction algorithms have been developed and found promising for wave energy converters. Although mostly proven by simulation studies, model predictive control based algorithms have shown to outperform classical wave power extraction algorithms such as linear damping and reactive control. Prediction models and objective functions have, however, often been simplified a lot by for example, excluding power take-off system losses. Furthermore, discrete fluid power forces systems has never been validated experimentally in published research. In this paper

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Chu, Zhenzhong, Da Wang, and Fei Meng. "An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles." Machines 9, no. 5 (2021): 105. http://dx.doi.org/10.3390/machines9050105.

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An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction outp

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Shi, Jianjun, Atul G. Kelkar, and Don Soloway. "Stable Reconfigurable Generalized Predictive Control With Application to Flight Control." Journal of Dynamic Systems, Measurement, and Control 128, no. 2 (2005): 371–78. http://dx.doi.org/10.1115/1.2194076.

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This paper presents the development of a multiinput multioutput generalized predictive control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. The stability of the GPC control law without reconfiguration is first established using an end-point state weighting. Based on the constrained nonlinear optimization, an end-point state weighting matrix synthesis method is derived. A novel reconfiguration strategy is developed for systems that have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable contro

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Gonçalves, Pedro, Sérgio Cruz, and André Mendes. "Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview." Energies 12, no. 24 (2019): 4693. http://dx.doi.org/10.3390/en12244693.

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Recently, the control of multiphase electric drives has been a hot research topic due to the advantages of multiphase machines, namely the reduced phase ratings, improved fault tolerance and lesser torque harmonics. Finite control set model predictive control (FCS-MPC) is one of the most promising high performance control strategies due to its good dynamic behaviour and flexibility in the definition of control objectives. Although several FCS-MPC strategies have already been proposed for multiphase drives, a comparative study that assembles all these strategies in a single reference is still m

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Tian, Zhongda, Shujiang Li, and Yanhong Wang. "Coke Oven Flue Temperature Control Based on Improved Implicit Generalized Predictive Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 2 (2018): 203–13. http://dx.doi.org/10.20965/jaciii.2018.p0203.

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The flue temperature of coke oven is an important factor that guarantees the coke yield, the coke quality and the energy consumption of coking production. The heating process of coke oven is an object with multi control variables, nonlinear and large lag. The traditional PID control algorithm cannot further improve the control performance of the coke oven system. An improved implicit generalized predictive control algorithm with better control performance is proposed in this paper. Through inputting control increment value constrained by soft coefficient matrix, the calculation of matrix inver

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Peng, Daogang, Yue Xu, and Huirong Zhao. "Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards." Energies 12, no. 21 (2019): 4073. http://dx.doi.org/10.3390/en12214073.

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In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of the

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Yin, Guodong, Jianghu Li, Xianjian Jin, Chentong Bian, and Nan Chen. "INTEGRATION OF MOTION PLANNING AND MODEL-PREDICTIVE-CONTROL-BASED CONTROL SYSTEM FOR AUTONOMOUS ELECTRIC VEHICLES." TRANSPORT 30, no. 3 (2015): 353–60. http://dx.doi.org/10.3846/16484142.2015.1089322.

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This paper introduces the development of an autonomous driving system in autonomous electric vehicles, which consists of a simplified motion-planning program and a Model-Predictive-Control-Based (MPC-based) control system. The motion-planning system is based on polynomial parameterization, which computes a path toward the expected longitudinal and lateral positions within required time interval in real scenarios. Then the MPC-based control system cooperates the front steering and individual wheel torques to track the planned trajectories, while fulfilling the physical constraints of actuators.

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Grosswindhager, S., M. Kozek, Andreas Voigt, and Lukas Haffner. "Fuzzy predictive control of district heating network." International Journal of Modelling, Identification and Control 19, no. 2 (2013): 161. http://dx.doi.org/10.1504/ijmic.2013.054320.

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Ye, Hao, Haobin Jiang, Shidian Ma, Bin Tang, and Lukuman Wahab. "Linear model predictive control of automatic parking path tracking with soft constraints." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141985220. http://dx.doi.org/10.1177/1729881419852201.

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This study examines how to improve the accuracy of auto parking path tracking control; therefore, a linear model predictive control with softening constraints path tracking control strategy is proposed. Firstly, a linear time-varying predictive model of vehicle is established, and the future state of the vehicle can be predicted. The designed objective function fully considers the deviation between the predictor variable and the reference variable. Also, the relaxation factors are added to the optimization process, and the control increment of each cycle is calculated by the quadratic programm

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Roasto, Indrek, Oleksandr Husev, Mahdiyyeh Najafzadeh, Tanel Jalakas, and Jose Rodriguez. "Voltage Source Operation of the Energy-Router Based on Model Predictive Control." Energies 12, no. 10 (2019): 1892. http://dx.doi.org/10.3390/en12101892.

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The energy router (ER) is regarded as a key component of microgrids. It is a converter that interfaces the microgrid(s) with the utility grid. The energy router has a multiport structure and bidirectional energy flow control. The energy router concept can be implemented in nearly zero energy buildings (NZEB) to provide flexible energy management. We propose a concept where ER is working as a single grid-forming converter with a predefined voltage reference. The biggest challenge is to maintain regulated voltage and frequency inside the NZEB in the idle operation mode, where traditional regulat

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