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Добірка наукової літератури з теми "Predictive simulation model"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Predictive simulation model"

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El Hachimi, Mohamed, Abdelhakim Ballouk, and AbdNaceur Baghdad. "Fuzzy Model Predictive Controller for Artificial Pancreas." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 3 (September 1, 2018): 1178. http://dx.doi.org/10.11591/ijpeds.v9.i3.pp1178-1185.

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This work consists on new tuning of Model Predictive Controllers using Fuzzy Logic method. Tree relevant parameters are automatically adjusted the prediction horizon Np, the input weight R and the output weight Q. The proposed controller is implemented in an Artificial Pancreas and tested under realistic conditions in a commercial platform of simulation. The result of the simulations revealed the success of such a method to improve the controller’s performances compared to the previous ones.
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2

Li, Chao, Gang Huang, Zhongyang Hao, and Cheng Liu. "A Motion Control Simulation Based on Predictive Model." Journal of Physics: Conference Series 2333, no. 1 (August 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2333/1/012012.

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Abstract How to improve target tracking efficiency, how to track targets with different motion forms, and establish target motion models are the main research directions. This paper expounds a motion control algorithm based on the prediction model. By establishing the motion equation and prediction model of the target, setting the estimated parameters, optimizing the initial value setting, and predicting the motion state of the target, the simulation analysis based on MATLAB is completed. The simulation results show that the motion control algorithm based on the prediction model has good robustness and real-time performance, realizes the feedback control closed-loop of “prediction-error-correction-prediction”, and can effectively reduce the real-time calculation amount of target tracking. Good target tracking accuracy.
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3

Lee, Robin L., Brendon A. Bradley, Peter J. Stafford, Robert W. Graves, and Adrian Rodriguez-Marek. "Hybrid broadband ground motion simulation validation of small magnitude earthquakes in Canterbury, New Zealand." Earthquake Spectra 36, no. 2 (February 2, 2020): 673–99. http://dx.doi.org/10.1177/8755293019891718.

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Ground motion simulation validation is an important and necessary task toward establishing the efficacy of physics-based ground motion simulations for seismic hazard analysis and earthquake engineering applications. This article presents a comprehensive validation of the commonly used Graves and Pitarka hybrid broadband ground motion simulation methodology with a recently developed three-dimensional (3D) Canterbury Velocity Model. This is done through simulation of 148 small magnitude earthquake events in the Canterbury, New Zealand, region in order to supplement prior validation efforts directed at several larger magnitude events. Recent empirical ground motion models are also considered to benchmark the simulation predictive capability, which is examined by partitioning the prediction residuals into the various components of ground motion variability. Biases identified in source, path, and site components suggest that improvements to the predictive capabilities of the simulation methodology can be made by using a longer high-frequency path duration model, reducing empirical V s30-based low-frequency site amplification, and utilizing site-specific velocity models in the high-frequency simulations.
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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 (September 1, 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 are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.
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Giwa, Abdulwahab, Abel Adekanmi Adeyi, and Saidat Olanipekun Giwa. "Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB." International Journal of Engineering Research in Africa 30 (May 2017): 167–80. http://dx.doi.org/10.4028/www.scientific.net/jera.30.167.

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This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Thereafter, using the obtain optimum value of 5 and 15 for control horizon and prediction horizon respectively as well as a manipulated variable rate weight of 0.025 and an output variable rate weight of 1.10, various steps were applied to the setpoint of the controlled variable and the responses plotted. The results given by the simulations carried out by varying the model predictive control parameters (control horizon and prediction horizon) for the control of the system revealed that optimizing the control parameters is better than arbitrary choosing. Also, the simulation of the developed model predictive control system of the process showed that its performance was better than those used to control the same process using a proportional-integral-derivative (PID) controller tuned with Cohen-Coon and Ziegler-Nichols techniques. It has, thus, been discovered that the Model Predictive Control Toolbox of MATLAB can be applied successfully to control a reactive distillation process in order to obtain better performance than that obtained from a PID controller tuned with Cohen-Coon and Ziegler-Nichols methods.
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Kour, Ravdeep, Adithya Thaduri, and Ramin Karim. "Predictive model for multistage cyber-attack simulation." International Journal of System Assurance Engineering and Management 11, no. 3 (February 3, 2020): 600–613. http://dx.doi.org/10.1007/s13198-020-00952-5.

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Stenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (February 13, 2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.

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The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, “predictive fit,” based on the model’s ability to predict new data is advocated. The authors define two prediction tasks: “missing responses prediction”—where the goal is to predict an in-sample person’s response to an in-sample item—and “missing persons prediction”—where the goal is to predict an out-of-sample person’s string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a “model’s predictions on average?”). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike’s information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.
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Olofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (May 28, 2014): 71. http://dx.doi.org/10.12688/f1000research.2-71.v2.

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Анотація:
Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.
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Olofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (July 27, 2015): 71. http://dx.doi.org/10.12688/f1000research.2-71.v3.

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Анотація:
Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed-effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.
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10

Becker, Dennis, Vincent Bremer, Burkhardt Funk, Mark Hoogendoorn, Artur Rocha, and Heleen Riper. "Evaluation of a temporal causal model for predicting the mood of clients in an online therapy." Evidence Based Mental Health 23, no. 1 (February 2020): 27–33. http://dx.doi.org/10.1136/ebmental-2019-300135.

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Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity.Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients’ future mood levels.Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes.Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.
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Більше джерел

Дисертації з теми "Predictive simulation model"

1

Shah, Nirali. "Simulation of Model Predictive Control using Dynamic Matrix Control algorithm." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604872.

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Model Predictive Control has emerged as a very powerful technology in the area of process control for three decades. The objective of this work was to develop Dynamic Matrix Control Algorithm, one of the most widely used Model Predictive Control Algorithms using MATLAB and simulate it for a real world Single Input Single Output system. This thesis focuses on the impacts and importance of the tuning parameters of Dynamic Matrix Control along with an overview of the general Model Predictive Control strategy. The tuning of the Dynamic Matrix Controller was done by trial and error based on the knowledge of the simulated system under consideration and the control strategy. The Control Signal computed was then implemented on the system to study its effect on the system output using a discrete transfer function model. The results of the tuned controller were observed to be similar to the other tuning methods discussed in the literature.

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2

Wei, Zhouping, University of Western Sydney, and of Mechatronic Computer and Electrical Engineering School. "Model predictive control of a robot using neural networks." THESIS_XXX_MCEE_Wei_Z.xml, 1999. http://handle.uws.edu.au:8081/1959.7/323.

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The aim of the thesis is to develop a model-based control strategy, namely, the Model Predictive Control (MPC) method, for robot position control using artificial neural networks. MPC is primarily developed for process control. Therefore its application in robot control has been less reported. In addition, conventional MPC uses linear model of the system for prediction which leads to inaccuracy for highly non-linear systems, such as robot. In this thesis a simulation model of a modified PUMA robot is constructed. This model is built using both MATLAB/SIMULINK and FORTRAN languages. In this model, the full robot dynamics is used together with the realistic factors, such as the actuator effects and the gear backlash, to represent the real system accurately. All simulations throughout this thesis are carried out on this model. A model predictive control strategy for robot trajectory tracking is also introduced in this thesis. The feasibility of the proposed MPC control method is studied based on a perfect prediction model, a model with uncertainties, and when the frequency band of the MPC controller is limited. Furthermore, a new method of using neural networks for robot dynamics modelling is introduced. This method is developed on the basis of a numerical differential technique that eliminates the explicit requirement of robot joint accelerations. Therefore, this method can be easily implemented on physical systems. As the measurements of the robot joint positions, velocities, and torques collected from operating the robot can be used to train the neural network, a more accurate dynamic model can be obtained. Finally, the MPC control method and the neural network model are combined together to form a neural network based MPC controller. The validity of this method is verified by using simulation on the simulated robot system
Master of Engineering (Hons)
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3

Riley, Matthew E. "Quantification of Model-Form, Predictive, and Parametric Uncertainties in Simulation-Based Design." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1314895435.

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4

Silva, Marco Jorge Tome da. "Simulation of human motion data using short-horizon model-predictive control." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43041.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 52-56).
Many data-driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model-predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low-gain proportional-derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.
by Marco da Silva.
S.M.
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5

Toschi, Alessandro. "Integration of Model Predictive Control for autonomous racing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Анотація:
Autonomous driving is one of the technologies that could impact significantly society in the next decades. While various advanced driver assistance systems (ADAS) have already been introduced in commercial passenger vehicles, the technology for fully self- driving cars is not yet ready. The Indy Autonomous Challenge is a competition between universities and research centers, born to advance the technology in this field by com- peting in autonomous racecar events. The IAC seeks to increase public awareness of the transformational impact that automation can have on society and solve edge-case scenarios unlikely to happen in an urban scenario but with the need of be addressed to ensure safety. The focus of this thesis is on the integration of the controller, a model predictive control (MPC), used in two of these challenges. This class of control, based on a constrained op- timal control scheme, is usually used to cope with challenging situations and was suitable for handling an autonomous car at high speeds.
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6

Abdul-Jalal, Rifqi I. "Engine thermal management with model predictive control." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/24274.

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The global greenhouse gas CO2 emission from the transportation sector is very significant. To reduce this gas emission, EU has set an average target of not more than 95 CO2/km for new passenger cars by the year 2020. A great reduction is still required to achieve the CO2 emission target in 2020, and many different approaches are being considered. This thesis focuses on the thermal management of the engine as an area that promise significant improvement of fuel efficiency with relatively small changes. The review of the literature shows that thermal management can improve engine efficiency through the friction reduction, improved air-fuel mixing, reduced heat loss, increased engine volumetric efficiency, suppressed knock, reduce radiator fan speed and reduction of other toxic emissions such as CO, HC and NOx. Like heat loss and friction, most emissions can be reduced in high temperature condition, but this may lead to poor volumetric efficiency and make the engine more prone to knock. The temperature trade-off study is conducted in simulation using a GT-SUITE engine model coupled with the FE in-cylinder wall structure and cooling system. The result is a map of the best operating temperature over engine speed and load. To quantify the benefit of this map, eight driving styles from the legislative and research test cycles are being compared using an immediate application of the optimal temperature, and significant improvements are found for urban style driving, while operation at higher load (motorway style driving) shows only small efficiency gains. The fuel consumption saving predicted in the urban style of driving is more than 4%. This assess the chance of following the temperature set point over a cycle, the temperature reference is analysed for all eight types of drive cycles using autocorrelation, lag plot and power spectral density. The analysis consistently shows that the highest volatility is recorded in the Artemis Urban Drive Cycle: the autocorrelation disappears after only 5.4 seconds, while the power spectral density shows a drop off around 0.09Hz. This means fast control action is required to implement the optimal temperature before it changes again. Model Predictive Control (MPC) is an optimal controller with a receding horizon, and it is well known for its ability to handle multivariable control problems for linear systems with input and state limits. The MPC controller can anticipate future events and can take control actions accordingly, especially if disturbances are known in advance. The main difficulty when applying MPC to thermal management is the non-linearity caused by changes in flow rate. Manipulating both the water pump and valve improves the control authority, but it also amplifies the nonlinearity of the system. Common linearization approaches like Jacobian Linearization around one or several operating points are tested, by found to be only moderately successful. Instead, a novel approach is pursued using feedback linearization of the plant model. This uses an algebraic transformation of the plant inputs to turn the nonlinear systems dynamics into a fully or predominantly linear system. The MPC controller can work with the linear model, while the actual control inputs are found using an inverse transformation. The Feedback Linearization MPC of the cooling system model is implemented and testing using MathWork Simulink®. The process includes the model transformation approach, model fitting, the transformation of the constraints and the tuning of the MPC controller. The simulation shows good temperature tracking performance, and this demonstrates that a MPC controller with feedback linearization is a suitable approach to thermal management. The controller strategy is then validated in a test rig replicating an actual engine cooling system. The new MPC controller is again evaluated over the eight driving cycles. The average water pump speed is reduced by 9.1% compared to the conventional cooling system, while maintaining good temperature tracking. The controller performance further improves with future disturbance anticipation by 20.5% for the temperature tracking (calculated by RMSE), 6.8% reduction of the average water pump speed, 47.3% reduction of the average valve movement and 34.0% reduction of the average radiator fan speed.
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7

Vara-Cadillo, Gabriel. "Autonomous Car Overtake Using Model Predictive Control." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293818.

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Анотація:
Autonomous vehicles have in recent years grownin popularity. An autonomous car has the potential to safelymaneuver in an efficient manner. This in combination with thefocus on increased road safety has put higher emphasis onimplementing an overtaking controller. Model Predictive Control(MPC) is very useful because it can handle linear constraintsand works for autonomous driving. I implemented the controlsystem in Python and did tests on its overtake capability usingdifferent velocities, car distances and initial speeds. Constraintswere implemented so that the autonomous vehicle did not collidewith another vehicle or drive outside the road when overtaking.The results show that a safe overtake could be performed undercertain conditions. The MPC algorithm is proven useful butdifficult to optimize.
Autonoma fordon har lyckats locka till sig mer populäritet under de senaste åren. En autonom bil har möjligheten att manövrera på ett säkert och effektivt sätt. Detta i kombination med ett fokus att öka vägsäkerheten har lagt större press på att implementera reglersystem för omkörningar. Modell prediktiv reglering (MPC) är användbar för den kan hantera linjära bivillkor och fungerar till autonomon körning. Ett reglersystem är implementerat i Python och testades på sin omkörningförmåga med olika hastigheter, avstånd och begynnelse hastigheter. Implementationen utformades med bivillkor som att det autonoma fordonet inte ska krocka med ett annat fordon eller köra utanför vägen i en omkörning. Resultaten visar att det gick att köra om på ett säkert sätt med vissa förutsättningar. MPC algoritmen har visat sig användbar men svår att optimera.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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8

Sheth, Katha Janak. "Model predictive control for adaptive digital human modeling." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/884.

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Анотація:
We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.
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9

Horii, M. Michael. "A Predictive Model for Multi-Band Optical Tracking System (MBOTS) Performance." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579658.

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Анотація:
ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV
In the wake of sequestration, Test and Evaluation (T&E) groups across the U.S. are quickly learning to make do with less. For Department of Defense ranges and test facility bases in particular, the timing of sequestration could not be worse. Aging optical tracking systems are in dire need of replacement. What's more, the increasingly challenging missions of today require advanced technology, flexibility, and agility to support an ever-widening spectrum of scenarios, including short-range (0 − 5 km) imaging of launch events, long-range (50 km+) imaging of debris fields, directed energy testing, high-speed tracking, and look-down coverage of ground test scenarios, to name just a few. There is a pressing need for optical tracking systems that can be operated on a limited budget with minimal resources, staff, and maintenance, while simultaneously increasing throughput and data quality. Here we present a mathematical error model to predict system performance. We compare model predictions to site-acceptance test results collected from a pair of multi-band optical tracking systems (MBOTS) fielded at White Sands Missile Range. A radar serves as a point of reference to gauge system results. The calibration data and the triangulation solutions obtained during testing provide a characterization of system performance. The results suggest that the optical tracking system error model adequately predicts system performance, thereby supporting pre-mission analysis and conserving scarce resources for innovation and development of robust solutions. Along the way, we illustrate some methods of time-space-position information (TSPI) data analysis, define metrics for assessing system accuracy, and enumerate error sources impacting measurements. We conclude by describing technical challenges ahead and identifying a path forward.
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10

Zsolt, Pap Levente. "Model Predictive Control of Electric Drives -Design, Simulation and Implementation of PMSM Torque Control." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240365.

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Анотація:
The thesis deals with the design of a permanent magnet synchronous machine controller that isimplemented on an embedded platform to replace the off-the-shelf controller currently being used in theelectric race car of the KTH Formula Student team. Software implementation of the control algorithmwas tested in laboratory environment on the hardware prototype of a 2-level three-phase voltage sourceinverter.Field oriented control and finite control set model predictive control algorithms were implemented insimulation environment. The latter performed better in terms of reducing switching activity and torqueripple, but needs vastly more computational resources due to its nature of being an online optimizationproblem. Trade-off curve of phase current harmonic distortion and switching activity showed that themodel prediction control algorithm performs better in the low frequency range (1-20 kHz). Obtainedsimulation results were used for power electronics component selection.Field oriented control was implemented on a TMS320F28335 DSP. SPI communication was employedto configure gate driver circuits and perform error handling. The DSP program follows interrupt basedorganization and the main control loop runs on the variable frequency of the pulse width modulation.Low voltage test results on three-phase inductive-resistive load showed that the controller outputssinusoidal current. Efficiency measurement, high voltage and motor testing were hindered by interferencefrom the Silicon-Carbide MOSFETs that prohibited correct operation of hardware.
Den här uppsatsen handlar om designen och implementeringen av en motorstyrning för en permanen- magnetiserad synkronmotor, med syfte att ersätta standardmotorstyrningsenheten i KTH Formula Students tävlingsbil. Implementationen av styralgoritmen testades experimentellt tillsammans med en prototyptillverkad frekvensomriktare i labbmiljö. Regleralgoritmer för field oriented control och finite control set model predictive control implementerades och testades i simuleringsmiljö. Den senare algoritmen visade sig prestera bättre i form av lägre vridmomentsoscillationer trots lägre switch-frekvens men den kräver samtidigt mer beräkningskraft. Övertonsinnehållet (THD) i fasströmmarna som funktion av switchfrekvensen undersöktes för de båda regleralgoritmerna, algoritmen för model predictive control gav lägre THD vid lägre frekvenser (1-20 kHz). Simuleringsresultaten användes för att motivera valet av komponenter till frekvensomriktaren. Regleralgoritmen för field oriented control implementerades och testades experimentellt med hjälp av ett utvecklingskort (TMS320F28335) från Texas Instruments. SPI-kommunikation användes för att konfigurera drivkretsana samt för att utläsa felkoder. Experimentalla tester som utfördes på låg spänningsnivå visade att strömmen till lasten var sinusformad. Mätning av verkningsgrad och provning tillsammans med motorn på en högre spänningsnivå gick inte att geno av att de snabba switchförloppen i kiselkarbidtransistorerna störde ut motorstyrningen.
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Книги з теми "Predictive simulation model"

1

1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Camacho, E. F. Model predictive control. 2nd ed. New York: Springer, 2004.

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4

Richards, Thomas. A predictive model of Aboriginal archaeological site distribution in the Otway Range. [Melbourne]: Aboriginal Affairs Victoria, 1998.

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5

Bousfield, Wayne E. Application of predictive model to forecast Douglas-fir tussock moth defoliation. Missoula, Mont: USDA Forest Service, Northern Region, 1986.

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6

McMillan, Gregory K. Models unleashed: Virtual plant and model predictive control applications : a pocket guide. Research Triangle Park, NC: ISA, 2004.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Bureau of Land Management, Utah State Office, 1986.

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8

Black, Kevin D. The Castle Valley archaeological project: An inventory and predictive model of selected tracts. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Black, Kevin D. The Castle Valley archaeological project: An inventory and predictive model of selected tracts. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Частини книг з теми "Predictive simulation model"

1

Takács, Gergely, and Boris Rohal’-Ilkiv. "Simulation Study of Model Predictive Vibration Control." In Model Predictive Vibration Control, 391–425. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0_11.

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2

Palmieri, Giovanni, Osvaldo Barbarisi, Stefano Scala, and Luigi Glielmo. "An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results." In Automotive Model Predictive Control, 231–55. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_15.

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3

Wüthrich, Mario V., and Michael Merz. "Predictive Modeling and Forecast Evaluation." In Springer Actuarial, 75–110. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_4.

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AbstractThis chapter is the core theoretical chapter on predictive modeling, forecast evaluation and model selection. The main problem in actuarial modeling is to forecast and price future claims. For this, we build predictive models, and this chapter deals with assessing and ranking these predictive models. We therefore introduce the mean squared error of prediction (MSEP) and, more generally, the expected generalization loss (GL) to assess predictive models. This chapter is complemented by a more decision-theoretic approach to forecast evaluation, it discusses deviance losses, proper scoring, elicitability, forecast dominance, cross-validation, Akaike’s information criterion (AIC) and we give an introduction to the bootstrap simulation method.
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4

Karamouzas, Ioannis, Peter Heil, Pascal van Beek, and Mark H. Overmars. "A Predictive Collision Avoidance Model for Pedestrian Simulation." In Motion in Games, 41–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10347-6_4.

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5

Phan, Minh Q., and Seyed Mahdi B. Azad. "Model Predictive Q-Learning (MPQ-L) for Bilinear Systems." In Modeling, Simulation and Optimization of Complex Processes HPSC 2018, 97–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55240-4_5.

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Tymchuk, Sergii, Sergii Shendryk, Vira Shendryk, Ivan Abramenko, and Anastasiia Kazlauskaite. "The Methodology of Obtaining Power Consumption Fuzzy Predictive Model for Enterprises." In Advances in Design, Simulation and Manufacturing III, 210–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50794-7_21.

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Brown, Arthur A., Bonnie R. Antoun, Michael L. Chiesa, Stephen B. Margolis, Devin O’Connor, Jason M. Simmons, Douglas J. Bammann, Chris San Marchi, and Nancy Y. C. Yang. "Predictive Simulation of a Validation Forging Using a Recrystallization Model." In Time Dependent Constitutive Behavior and Fracture/Failure Processes, Volume 3, 51–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9794-4_9.

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8

Ginhoux, Romuald, Jacques A. Gangloff, Michel F. de Mathelin, Luc Soler, Jöel Leroy, and Jacques Marescaux. "Model Predictive Control for Cancellation of Repetitive Organ Motions in Robotized Laparoscopic Surgery." In Surgery Simulation and Soft Tissue Modeling, 353–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7_34.

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Luo, Zhixiang, Shanbi Wei, Yi Chai, Yanxing Liu, and Xiuling Sun. "Simulation of Wind Farm Scheduling Algorithm Based on Predictive Model Control." In Lecture Notes in Electrical Engineering, 581–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_56.

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10

Bernardeschi, Cinzia, Pierpaolo Dini, Andrea Domenici, Ayoub Mouhagir, Maurizio Palmieri, Sergio Saponara, Tanguy Sassolas, and Lilia Zaourar. "Co-simulation of a Model Predictive Control System for Automotive Applications." In Lecture Notes in Computer Science, 204–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12429-7_15.

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Тези доповідей конференцій з теми "Predictive simulation model"

1

HERZOG, Simon, Dennis ATABAY, Johannes JUNGWIRTH, and Vesna MIKULOVIC. "Self-adapting Building Models For Model Predictive Control." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.2298.

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Honc, Daniel, and Frantisek Dusek. "State-Space Constrained Model Predictive Control." In 27th Conference on Modelling and Simulation. ECMS, 2013. http://dx.doi.org/10.7148/2013-0441.

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Erfani, Arash, Xingji Yu, Tuule Mall Kull, Peder Bacher, and Tohid Jafarinejad. "Analysis of the impact of predictive models on the quality of the Model Predictive Control for an experimental building." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30566.

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Lai, C. Y., C. Xiang, and T. H. Lee. "Identification and Control of Piecewise Affine Systems using Multiple Models and Model Predictive Control." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.697-009.

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Samek, David. "Elman Neural Networks In Model Predictive Control." In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0577-0581.

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Goussous, Faisal, Rajankumar Bhatt, Soura Dasgupta, and Karim Abdel-Malek. "Model Predictive Control for Human Motion Simulation." In Digital Human Modeling for Design and Engineering Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2009. http://dx.doi.org/10.4271/2009-01-2306.

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Rusar, Lukas, Adam Krhovjak, Stanislav Talas, and Vladimir Bobal. "State-Space Predictive Control Of Inverted Pendulum Model." In 31st Conference on Modelling and Simulation. ECMS, 2017. http://dx.doi.org/10.7148/2017-0384.

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Nagpal, Himashu, Florin Capitanescu, and Per Heiselberg. "Model predictive control framework for operation of smart sustainable buildings." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.31111.

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9

Mattar, Ebrahim A., and Khaled H. Al Mutib. "Synthesizing Fuzzy Based Model Predictive Controller." In 2011 Third International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2011. http://dx.doi.org/10.1109/cimsim.2011.29.

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Yang, Tao, Fisayo Caleb Sangogboye, Krzysztof Arendt, Konstantin Filonenko, and Jonathan Dallaire. "The impact of occupancy prediction accuracy on the performance of model predictive control (MPC) in buildings." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30571.

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Звіти організацій з теми "Predictive simulation model"

1

Bays, Samuel E., and David L. Chichester. TREAT HODOSCOPE – A Predictive Simulation Model. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1408774.

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2

Oberkampf, William Louis, Timothy Guy Trucano, and Martin M. Pilch. Predictive Capability Maturity Model for computational modeling and simulation. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/976951.

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3

Nishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, January 2020. http://dx.doi.org/10.4271/2019-32-0572.

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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force. We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run. The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.
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4

Nichols, Will, Stephanie Tomusiak, and Ryan Nell. Predictive Flow Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1668408.

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Tomusiak, Stephanie, Hai Pham, and Trevor Budge. Predictive Flow Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1855946.

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Kohler, Christian. Simulation of complex glazing products; from optical data measurements to model based predictive controls. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1171808.

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Tomusiak, S., and William Nichols. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1706735.

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Tomusiak, Stephanie, Hai Pham, and Trevor Budge. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Inventory Sensitivity Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856027.

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Pham, Hai, Stephanie Tomusiak, and Trevor Budge. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Recharge Sensitivity Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856046.

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10

Nichols, Will, and S. Tomusiak. Predictive Contaminant Transport Simulation with P2R Model for the Composite Analysis Limited Source Sensitivity Case. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1862348.

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