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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Li, Zhaobin, Xiaohao Liu, and Xiaolei Yang. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes." Energies 15, no. 18 (September 7, 2022): 6533. http://dx.doi.org/10.3390/en15186533.
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Wind turbine parameterization models, which are often employed to avoid the computational cost of resolving the blade aerodynamics, are critical for the capability of large-eddy simulation in predicting wind turbine wakes. In this paper, we review the existing wind turbine parameterization models, i.e., the actuator disk model, the actuator line model, and the actuator surface model, by presenting the fundamental concepts, some advanced issues (i.e., the force distribution approaches, the method for velocity sampling, and the tip loss correction), and their applications to utility-scale wind farms. Emphasis is placed on the predictive capability of different parameterizations for different wake characteristics, such as the blade load, the tip vortices and hub vortex in the near wake, and the meandering of the far wake. The literature demonstrated the importance of taking into account the effects of nacelle and tower in wind turbine wake predictions. The predictive capability of the actuator disk model with different model complexities, which is preferred in wind farm simulations, is systematically reviewed for different inflows and different wind turbine designs. Applications to wind farms show good agreements between simulation results and measurements.
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Wang, Lin Lin, Hong Jian Wang, and Li Xin Pan. "Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control." Applied Mechanics and Materials 670-671 (October 2014): 1370–77. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1370.
Повний текст джерелаАнотація:
In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).
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Zhang, Jian, and Wan Juan Song. "The Framework Base on Bayesian Predictive Filtering Algorithm in VR/AR." Applied Mechanics and Materials 568-570 (June 2014): 1122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1122.
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Tracking system is a vital aspect of Virtual Reality and Augmented Reality, the efficiency of tracking system is determined by the implementation of framework and the predictive filtering algorithm. As a result of the better applicability of Bayesian predictive filtering algorithm in simulation of non-linear system model, this paper proposes a framework for Bayesian predictive filter, which includes predictive filtering layer and denotation layer, and according to every layer’s function, analyses the implementation of framework. The optimal simulation count is worked out by the experiment. The results show that in the simulation of non-linear system model, this framework for Bayesian predictive filter can implement the tracking of simple motion and the orientation prediction.
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Jeong, Yonghwan. "Stochastic Model-Predictive Control with Uncertainty Estimation for Autonomous Driving at Uncontrolled Intersections." Applied Sciences 11, no. 20 (October 10, 2021): 9397. http://dx.doi.org/10.3390/app11209397.
Повний текст джерелаАнотація:
This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covariance estimator has been utilized to estimate the prediction uncertainty for each approaching vehicle. The desired driving mode has been determined based on the uncontrolled intersection theory. The estimated prediction uncertainty has been used to define the probability distribution of the stochastic model-predictive controller to cope with time-varying uncertainty characteristics of the perception algorithm. The constrained stochastic model-predictive controller based on safety indexes has determined the desired longitudinal acceleration. The proposed robust intersection-passing algorithm has been evaluated via computer simulation based on Monte Carlo simulation with a sensor model. The simulation results showed that the proposed algorithm guarantees the minimum safety constraints and improves the ride comfort at uncontrolled intersections by estimating the uncertainty of sensors and prediction.
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AmirKhalkhali, Samad, and Sal AmirKhalkhali. "Predictive Efficiency Of Random Effects Approach: A Real Model Simulation Study." Journal of Business & Economics Research (JBER) 11, no. 11 (October 29, 2013): 497. http://dx.doi.org/10.19030/jber.v11i11.8196.
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This real model simulation study attempts to shed more light on the predictive performances of two of the most commonly used panel data regression methods - fixed effects and random effects. In particular, this paper attempts to address the question, How do these two alternative estimators perform in prediction when errors follow non-normal distributions? The simulation results support the random effects approach as the better choice.
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Nasejje, Justine B., Albert Whata, and Charles Chimedza. "Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival data." PLOS ONE 17, no. 12 (December 28, 2022): e0279435. http://dx.doi.org/10.1371/journal.pone.0279435.
Повний текст джерелаАнотація:
Research that seeks to compare two predictive models requires a thorough statistical approach to draw valid inferences about comparisons between the performance of the two models. Researchers present estimates of model performance with little evidence on whether they reflect true differences in model performance. In this study, we apply two statistical tests, that is, the 5 × 2-fold cv paired t-test, and the combined 5 × 2-fold cv F-test to provide statistical evidence on differences in predictive performance between the Fine-Gray (FG) and random survival forest (RSF) models for competing risks. These models are trained on different scenarios of low-dimensional simulated survival data to determine whether the differences in their predictive performance that exist are indeed significant. Each simulation was repeated one hundred times on ten different seeds. The results indicate that the RSF model is superior in predictive performance in the presence of complex relationships (quadratic and interactions) between the outcome and its predictors. The two statistical tests show that the differences in performance are significant in quadratic simulation but not significant in interaction simulations. The study has also revealed that the FG model is superior in predictive performance in linear simulations and its differences in predictive performance compared to the RSF model are significant. The combined 5 × 2-fold cv F-test has lower type I error rates compared to the 5 × 2-fold cv paired t-test.
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Avanzini, Giulio, Douglas Thomson, and Alberto Torasso. "Model Predictive Control Architecture for Rotorcraft Inverse Simulation." Journal of Guidance, Control, and Dynamics 36, no. 1 (January 2013): 207–17. http://dx.doi.org/10.2514/1.56563.
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Olajubu, E. A., Gbemisola Ajayi, Isaiah Oke, and Franklin Oladiipo Asahiah. "An ANN Model for Predicting the Quantity of Lead and Cadmium Ions in Industrial Wastewater." International Journal of Information Communication Technologies and Human Development 9, no. 4 (October 2017): 32–44. http://dx.doi.org/10.4018/ijicthd.2017100103.
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Rapid industrialization has contributed immensely to the discharge of heavy metals into receiving water bodies untreated. The quantity of heavy metals prediction in industrial wastewater is very essential before treatment so that the quantity is precisely removed. This article formulates, simulate and evaluate a predictive model that mimics electrochemical treatment of lead and cadmium ions present in paint industrial wastewater using artificial neural network. The predictive model was formulated using Fuzzy Logic toolbox in MATLAB and the simulation was done in the environment. The prediction of the model was evaluated by comparing the predicted quantity of lead ions and cadmium ions with the result of the experimental work in the laboratory. The article concludes that the developed prediction model demonstrated very high prediction accuracy in predicting the percentage of lead and cadmium ions present in paints wastewater.
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Kustowski, Bogdan, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G Kruse, and Ryan C. Nora. "Suppressing simulation bias in multi-modal data using transfer learning." Machine Learning: Science and Technology 3, no. 1 (March 1, 2022): 015035. http://dx.doi.org/10.1088/2632-2153/ac5e3e.
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Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.
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Yang, Da Peng, Ping Bo Sun, and Ke Qiang Hua. "Model Predictive Control of Flight Arrival Interval." Advanced Materials Research 503-504 (April 2012): 1375–80. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1375.
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In order to achieve the automation of Air Traffic Control (ATC), use system to identify the controlled model of flights arrival process which has been already built, using Model Predictive Control (MPC) of the dynamic matrix contro1 (DMC) to control the ATC process. According to DMC algorithm and the features of ATC, the design parameters of this system can be determined by a lot of simulations. It proves that the system design and parameters selection make the system has the required performance and the robustness even if the parameters be changed in a wide range. The experiment on the ATC Simulation System proves that the MPC method is available, conclusion of the study provides a new idea and method for the engineering implementation of the automation of flights arrival process control and some improvement of airspace utilization.
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de Oliveira, Marcelo Magaldi Ribeiro, Carlos Eduardo Ferrarez, Taise Mosso Ramos, Jose Augusto Malheiros, Arthur Nicolato, Carla Jorge Machado, Mauro Tostes Ferreira, et al. "Learning brain aneurysm microsurgical skills in a human placenta model: predictive validity." Journal of Neurosurgery 128, no. 3 (March 2018): 846–52. http://dx.doi.org/10.3171/2016.10.jns162083.
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OBJECTIVESurgery for brain aneurysms is technically demanding. In recent years, the process to learn the technical skills necessary for these challenging procedures has been affected by a decrease in the number of surgical cases available and progressive restrictions on resident training hours. To overcome these limitations, surgical simulators such as cadaver heads and human placenta models have been developed. However, the effectiveness of these models in improving technical skills is unknown. This study assessed concurrent and predictive validity of brain aneurysm surgery simulation in a human placenta model compared with a “live” human brain cadaveric model.METHODSTwo human cadaver heads and 30 human placentas were used. Twelve neurosurgeons participated in the concurrent validity part of this study, each operating on 1 human cadaver head aneurysm model and 1 human placenta model. Simulators were evaluated regarding their ability to simulate different surgical steps encountered during real surgery. The time to complete the entire aneurysm task in each simulator was analyzed. The predictive validity component of the study involved 9 neurosurgical residents divided into 3 groups to perform simulation exercises, each lasting 6 weeks. The training for the 3 groups consisted of educational video only (3 residents), human cadaver only (3 residents), and human placenta only (3 residents). All residents had equivalent microsurgical experience with superficial brain tumor surgery. After completing their practice training, residents in each of the 3 simulation groups performed surgery for an unruptured middle cerebral artery (MCA) aneurysm, and their performance was assessed by an experienced vascular neurosurgeon who watched the operative videos.RESULTSAll human cadaver heads and human placentas were suitable to simulate brain aneurysm surgery. In the concurrent validity portion of the experiment, the placenta model required a longer time (p < 0.001) than cadavers to complete the task. The placenta model was considered more effective than the cadaver model in simulating sylvian fissure splitting, bipolar coagulation of oozing microvessels, and aneurysm neck and dome dissection. Both models were equally effective in simulating neck aneurysm clipping, while the cadaver model was considered superior for simulation of intraoperative rupture and for reproduction of real anatomy during simulation. In the predictive validity portion of the experiment, residents were evaluated for 4 tasks: sylvian fissure dissection, microvessel bipolar coagulation, aneurysm dissection, and aneurysm clipping. Residents trained in the human placenta simulator consistently had the highest overall performance scores when compared with those who had trained in the cadaver model and those who had simply watched operative videos (p < 0.001).CONCLUSIONSThe human placenta biological simulator provides excellent simulation for some critical tasks of aneurysm surgery such as splitting of the sylvian fissure, dissection of the aneurysm neck and dome, and bipolar coagulation of surrounding microvessels. When performing surgery for an unruptured MCA aneurysm, residents who had trained in the human placenta model performed better than residents trained with other simulation scenarios/models. In this age of reduced exposure to aneurysm surgery and restrictions on resident working hours, the placenta model is a valid simulation for microneurosurgery with striking similarities with real surgery.
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Khelafa, Ilyas, Abdenaceur Baghdad, and Mohamed El Hachimi. "Improving model predictive control's optimization for urban traffic." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1367. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1367-1374.
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<p><span>When it comes to decreasing traffic congestion and enhancing mobility, traffic forecasting is critical. However, due to the complicated spatio-temporal dynamics of urban transportation networks, which are difficult to describe, this task is tough. Using a model predictive controller (MPC) provides the control of a traffic network's architecture as well as errors in its operations. Based on a real-time simulation, a novel, accurate prediction controller for urban traffic was presented in this study to estimate the number of cars at junctions and their waiting duration. Different optimization approaches were employed and evaluated to improve the MPC's performance. Simulation results demonstrated that the fmincon was very robust and could effectively reduce the number of vehicles in the link, in comparison with other algorithms This study also includes an in-depth analysis of the characteristics of various prediction horizon sets in an MPC. By increasing the prediction horizon, the amplitude of fluctuation became more important, but when Np=4, the fluctuations reduced.</span></p>
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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, Pablo de Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios Rovas, Simone Steiger, Christopher Mutschler, and Gunnar Gruen. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (December 2, 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 a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
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Guo, Hongqiang, Hongwen He, and Xuelian Xiao. "A Predictive Distribution Model for Cooperative Braking System of an Electric Vehicle." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/828269.
Повний текст джерелаАнотація:
A predictive distribution model for a series cooperative braking system of an electric vehicle is proposed, which can solve the real-time problem of the optimum braking force distribution. To get the predictive distribution model, firstly three disciplines of the maximum regenerative energy recovery capability, the maximum generating efficiency and the optimum braking stability are considered, then an off-line process optimization stream is designed, particularly the optimal Latin hypercube design (Opt LHD) method and radial basis function neural network (RBFNN) are utilized. In order to decouple the variables between different disciplines, a concurrent subspace design (CSD) algorithm is suggested. The established predictive distribution model is verified in a dynamic simulation. The off-line optimization results show that the proposed process optimization stream can improve the regenerative energy recovery efficiency, and optimize the braking stability simultaneously. Further simulation tests demonstrate that the predictive distribution model can achieve high prediction accuracy and is very beneficial for the cooperative braking system.
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Wang, Hong, Yong Hua Zhou, Yang Peng Wang, Wei Wang, and Xu Yang. "Traffic Signal Predictive Control Based on Cellular Automata Prediction Model and Non-Analytical Optimization." Advanced Materials Research 433-440 (January 2012): 2831–36. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2831.
Повний текст джерелаАнотація:
The effective control strategies of traffic signal can increase the passing-through traffic volume and decrease the total queue delay. Based on the prediction model of cellular automata, the corresponding non-analytical optimization procedure utilizing genetic algorithm is established for the traffic signal predictive control. The inductive predictive control for traffic signal is proposed which has certain practical importance. The simulation results demonstrate the advantages of proposed predictive control strategy over the conventional methods.
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Lin, Kuizhi, Wei Wang, Zhiqiang Wang, Xinmin Li, and Huang Zhang. "A Dynamic Load Simulation Algorithm Based on an Inertia Simulation Predictive Model." Applied Sciences 12, no. 14 (July 15, 2022): 7142. http://dx.doi.org/10.3390/app12147142.
Повний текст джерелаАнотація:
In this study, an electric dynamic load simulation system (EDLSS) algorithm was proposed based on an inertia simulation predictive model to mitigate strong-coupling torque disturbance and load torque fluctuation caused by the change in the motion state of a bearing system. First, the inertia simulation model was proposed by combining the dynamic equations of both the EDLSS and the target system. The aforementioned inertia simulation model converted the conventional realisation method of the inertia simulation into the tracking of the motion characteristics for the target system under the same working conditions. Next, based on the aforementioned inertia simulation model while considering the strong-coupling torque effect and motor braking state disturbance as two influential factors, an inertia simulation predictive model and a load simulation algorithm were proposed. The predicted speed calculated by the predictive model was consistent with the dynamic characteristics of the target system under the same working conditions and input into the control loop. Based on the analysis of the braking state and power model of the permanent magnet synchronous motor, an energy feedback control method was proposed to improve EDLSS stability caused by the braking state of the loading motor. Finally, the experimental data revealed that the maximum speed fluctuation range of the loading motor was approximately 7.5, which was 84% lower than the range before the application of the aforementioned algorithm, which was about 46.8. Furthermore, the maximum range of the torque ripple was close to 1.5, which was 75% lower than before, which was roughly 6. All experimental data were consistent with simulation data.
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Zhang, Li, Song Wang, and Guo Jun Su. "Intelligence Predictive Control Study on Lime Rotary Kiln Temperature." Applied Mechanics and Materials 385-386 (August 2013): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.848.
Повний текст джерелаАнотація:
For the non-linearity, large time lag characters of rotary kiln, we use intelligent predictive control method to control it. The prediction model, scrolling optimization and feedback adjustment are ultimate constituted the predictive control system each part. Gas flow measurement is used to realize rotary kiln`s temperature predictive control,and took NN-Model as prediction model to realize the intelligent forecast. The results of simulation show that this method has better stability and robustness than the traditional control method.
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Nasry, Hany, Wei Xu, Jian Wei Gong, and Hui Yan Chen. "Teleoperation Transparency Using Model Predictive Control." Applied Mechanics and Materials 446-447 (November 2013): 1151–55. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1151.
Повний текст джерелаАнотація:
Teleoperation means operating a vehicle or a system remotely over a distance.Teleoperation extends human capabilities to perform tasks remotely by providing the operator withsimilar conditions as those at the remote location. Generally, operator should be supported inreal-time with an accurate data about the teleoperation environment. This paper proposes atime-delay compensation algorithm for environment construction to modify the well-developed BITAGV to be teleoperated. This Algorithm includes predicting the vehicle position in the future timeusing Model Predictive control (MPC). Then, environment is constructed according to this positionusing the Laser scanner. Real and simulation experiments are presented to illustrate the performanceand effectiveness of the algorithm in compensating time-delay for Teleoperation.
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He, Huihui, Shengjun Huang, Yajie Liu, and Tao Zhang. "Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction." Discrete Dynamics in Nature and Society 2021 (October 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/2198846.
Повний текст джерелаАнотація:
With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.
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Duckett, Drew J., Tara A. Pelletier, and Bryan C. Carstens. "Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP." PeerJ 8 (January 10, 2020): e8271. http://dx.doi.org/10.7717/peerj.8271.
Повний текст джерелаАнотація:
Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.SNAPP. P2C2M.SNAPP is an R package that identifies model violations using posterior predictive simulation. P2C2M.SNAPP uses the posterior distribution of species trees output by the software package SNAPP to simulate posterior predictive datasets under the MSCM, and then uses summary statistics to compare either the empirical data or the posterior distribution to the posterior predictive distribution to identify model violations. In simulation testing, P2C2M.SNAPP correctly classified up to 83% of datasets (depending on the summary statistic used) as to whether or not they violated the MSCM model. P2C2M.SNAPP represents a user-friendly way for researchers to perform posterior predictive model checks when using the popular SNAPP phylogenetic estimation program. It is freely available as an R package, along with additional program details and tutorials.
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Haj Ahmad, Hanan. "Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications." Journal of Mathematics 2021 (July 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/1355990.
Повний текст джерелаАнотація:
This paper describes two prediction methods for predicting the non-observed (censored) units under progressive Type-II censored samples. The lifetimes under consideration are following a new two-parameter Pareto distribution. Furthermore, point and interval estimation of the unknown parameters of the new Pareto model is obtained. Maximum likelihood and Bayesian estimation methods are considered for that purpose. Since Bayes estimators cannot be expressed explicitly, Gibbs and the Markov Chain Monte Carlo techniques are utilized for Bayesian calculation. We use the posterior predictive density of the non-observed units to construct predictive intervals. A simulation study is performed to evaluate the performance of the estimators via mean square errors and biases and to obtain the best prediction method for the censored observation under progressive Type-II censoring scheme for different sample sizes and different censoring schemes.
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Pfeiffer, John R., Yuhan Zhang, Anu K. Antony, The SimBioSys Team, and John A. Cole. "The case for simul-omics: Identifying IO biomarkers of ganitumab-metformin pathological complete response (pCR)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e12551-e12551. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e12551.
Повний текст джерелаАнотація:
e12551 Background: During the I-SPY2 clinical trial, ganitumab (G) immunotherapy (IO) was co-administered with metformin (GM) to counteract G-induced hyperglycemia. The GM + standard of care (SOC) combination showed promising results but did not meet the statistical threshold for phase III. Predicting individual patient response to IO is a key limitation for clinical use and a reason why IO phase II to III oncology trial transitions fail. Thus, we used SimBioSys TumorScope (TS), a 4D spatiotemporal multiscale biophysical model, to identify predictive IO biomarkers of pCR in response to GM+SOC. Methods: Patients that received GM+SOC therapy (n=41) and SOC (n=41) matched controls from the I-SPY2 trial were included in analyses. Using SOC clinical data and DCE MRIs, we generated biophysical simulations of each individual patient’s response to SOC therapy. Derived from the TS simulation object of each patient, our “simul-omics” data represent tumor morphology, breast tissue proportions, drug response and delivery, and microvasculature-related feature sets. We also had access to the pre-treatment tumor biopsy-derived transcriptome microarray data. Predictive modeling was performed in a modular 3-step fashion: 1. clinical-only data to predict pCR, 2. clinical data plus candidate simul-omics data to predict pCR, and 3. available feature space for features that interact with treatment to provide the strongest predictive model of pCR. Results: The SOC data model (HR status, grade, treatment, age, race) predicted pCR with an accuracy (acc) of 0.67, sensitivity (sens)=0.71, specificity (spec)=0.66, and Cohen’s kappa=0.27. Simul-omic features median tumoral Kt at sim week 1 (acc=0.73), and tumoral pre-contrast small area emphasis (acc=0.77) improved model performance over SOC. When included in the same model, they increased predictive acc of the model to 0.79 (sens=0.64, spec=0.82, kappa=0.41). Simulation-derived features improved predictive power beyond SOC data. Exploratory search of the feature-space identified simul-omic features that would generate the most predictive models with a significant treatment interaction effect. Two simul-omics features added predictive strength to the model, with near-nominal interaction effects: phi_quant_90_regimen_wk22 (late timepoint mv density) (acc=0.84, kappa=0.58), and slopemap_tumor_glcm_clustershade (acc=0.86, kappa=0.60). We tested transcript expression as interaction terms in the model. The best performing model with an interaction effect was SHISA4 (acc=0.88, sens=0.86, spec=0.88, kappa=0.67). Conclusions: Our results show value in: 1. predicting pCR, the additive value of TS simulation data over just clinical/SOC data, 2. identification of potentially mechanistic drug targets facilitating GM response, and 3. potential to stratify patient populations into responder vs. non-responder categories prior to treatment.
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Pfeiffer, John R., Yuhan Zhang, Anu K. Antony, The SimBioSys Team, and John A. Cole. "The case for simul-omics: Identifying IO biomarkers of ganitumab-metformin pathological complete response (pCR)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e12551-e12551. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e12551.
Повний текст джерелаАнотація:
e12551 Background: During the I-SPY2 clinical trial, ganitumab (G) immunotherapy (IO) was co-administered with metformin (GM) to counteract G-induced hyperglycemia. The GM + standard of care (SOC) combination showed promising results but did not meet the statistical threshold for phase III. Predicting individual patient response to IO is a key limitation for clinical use and a reason why IO phase II to III oncology trial transitions fail. Thus, we used SimBioSys TumorScope (TS), a 4D spatiotemporal multiscale biophysical model, to identify predictive IO biomarkers of pCR in response to GM+SOC. Methods: Patients that received GM+SOC therapy (n=41) and SOC (n=41) matched controls from the I-SPY2 trial were included in analyses. Using SOC clinical data and DCE MRIs, we generated biophysical simulations of each individual patient’s response to SOC therapy. Derived from the TS simulation object of each patient, our “simul-omics” data represent tumor morphology, breast tissue proportions, drug response and delivery, and microvasculature-related feature sets. We also had access to the pre-treatment tumor biopsy-derived transcriptome microarray data. Predictive modeling was performed in a modular 3-step fashion: 1. clinical-only data to predict pCR, 2. clinical data plus candidate simul-omics data to predict pCR, and 3. available feature space for features that interact with treatment to provide the strongest predictive model of pCR. Results: The SOC data model (HR status, grade, treatment, age, race) predicted pCR with an accuracy (acc) of 0.67, sensitivity (sens)=0.71, specificity (spec)=0.66, and Cohen’s kappa=0.27. Simul-omic features median tumoral Kt at sim week 1 (acc=0.73), and tumoral pre-contrast small area emphasis (acc=0.77) improved model performance over SOC. When included in the same model, they increased predictive acc of the model to 0.79 (sens=0.64, spec=0.82, kappa=0.41). Simulation-derived features improved predictive power beyond SOC data. Exploratory search of the feature-space identified simul-omic features that would generate the most predictive models with a significant treatment interaction effect. Two simul-omics features added predictive strength to the model, with near-nominal interaction effects: phi_quant_90_regimen_wk22 (late timepoint mv density) (acc=0.84, kappa=0.58), and slopemap_tumor_glcm_clustershade (acc=0.86, kappa=0.60). We tested transcript expression as interaction terms in the model. The best performing model with an interaction effect was SHISA4 (acc=0.88, sens=0.86, spec=0.88, kappa=0.67). Conclusions: Our results show value in: 1. predicting pCR, the additive value of TS simulation data over just clinical/SOC data, 2. identification of potentially mechanistic drug targets facilitating GM response, and 3. potential to stratify patient populations into responder vs. non-responder categories prior to treatment.
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Wynants, L., Y. Vergouwe, S. Van Huffel, D. Timmerman, and B. Van Calster. "Does ignoring clustering in multicenter data influence the performance of prediction models? A simulation study." Statistical Methods in Medical Research 27, no. 6 (September 19, 2016): 1723–36. http://dx.doi.org/10.1177/0962280216668555.
Повний текст джерелаАнотація:
Clinical risk prediction models are increasingly being developed and validated on multicenter datasets. In this article, we present a comprehensive framework for the evaluation of the predictive performance of prediction models at the center level and the population level, considering population-averaged predictions, center-specific predictions, and predictions assuming an average random center effect. We demonstrated in a simulation study that calibration slopes do not only deviate from one because of over- or underfitting of patterns in the development dataset, but also as a result of the choice of the model (standard versus mixed effects logistic regression), the type of predictions (marginal versus conditional versus assuming an average random effect), and the level of model validation (center versus population). In particular, when data is heavily clustered (ICC 20%), center-specific predictions offer the best predictive performance at the population level and the center level. We recommend that models should reflect the data structure, while the level of model validation should reflect the research question.
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Carpenter, Chris. "Integrated Deep-Learning and Physics-Based Models Improve Production Prediction." Journal of Petroleum Technology 74, no. 11 (November 1, 2022): 78–80. http://dx.doi.org/10.2118/1122-0078-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204864, “Integrating Deep-Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs,” by Syamil M. Razak, SPE, Jodel Cornelio, SPE, and Atefeh Jahandideh, SPE, University of Southern California, et al. The paper has not been peer reviewed. _ The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs is not well understood. As a result, predicted production behavior using conventional simulation often does not agree with observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. In the complete paper, the authors discuss the development of a deep-learning model to investigate the errors in simulation-based performance prediction in unconventional reservoirs. Introduction One of the major challenges for petroleum engineers working with unconventional reservoirs is a lack of models that accurately represent a physical relationship between the formation, completion and fluid properties, and production responses. Statistical predictive models typically are used to extract sets of input parameters that better represent the properties of the field. However, a major drawback with statistical models is their inability to extrapolate from the training data set. The authors propose a deep-learning predictive model based on a combination of physics and data to account for the errors that may have come from undiscovered physics or imperfect description of unconventional reservoirs. The model leverages the power of deep learning to account for systematic prediction inaccuracies resulting from incomplete knowledge about the reservoir model and the underlying flow processes. Deep neural network models trained with observed production responses and other related data in the field are used to enhance the prediction performance of simulation models with limited knowledge of flow physics in complex, unconventional reservoirs. The data the authors use for their model consist of the following: - Formation, completion, and fluid properties for physics-based reservoir simulation (xsim) - Corresponding simulated production responses (dsim) - Formation, completion, and fluid properties collected from the field (xfield) - Related observed production response data (dfield) The authors define simulation errors (derr) as the difference between dsim and dfield. Each derr is paired with xerr that is the concatenation of the corresponding xsim and xfield. The predictive model integrates a 1D convolutional autoencoder (AE) that extracts temporal trends in the simulation errors as a set of latent variables. These components are used to train deep regression neural networks to represent the complex relationship between the simulation errors and these latent variables.
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Jianhong, Wang, and Ricardo A. Ramirez-Mendoza. "Application of Interval Predictor Model Into Model Predictive Control." WSEAS TRANSACTIONS ON SYSTEMS 20 (January 6, 2022): 331–43. http://dx.doi.org/10.37394/23202.2021.20.38.
Повний текст джерелаАнотація:
In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.
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Xu, Siqi, Yifeng Zhang, and Xiaodan Chen. "Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China." Discrete Dynamics in Nature and Society 2020 (October 26, 2020): 1–14. http://dx.doi.org/10.1155/2020/8827440.
Повний текст джерелаАнотація:
Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.
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Randvee, I. "A parametric optimization technique for model-predictive control simulation." Proceedings of the Estonian Academy of Sciences. Engineering 9, no. 1 (2003): 25. http://dx.doi.org/10.3176/eng.2003.1.02.
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Gupta, Arun, Sharad Bhartiya, and P. S. V. Nataraj. "Explicit-Model Predictive Control: A simulation based scalability study." IFAC Proceedings Volumes 45, no. 15 (2012): 204–9. http://dx.doi.org/10.3182/20120710-4-sg-2026.00072.
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Jiang, Bailun, Boyang Li, Weifeng Zhou, Li-Yu Lo, Chih-Keng Chen, and Chih-Yung Wen. "Neural Network Based Model Predictive Control for a Quadrotor UAV." Aerospace 9, no. 8 (August 20, 2022): 460. http://dx.doi.org/10.3390/aerospace9080460.
Повний текст джерелаАнотація:
A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.
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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 (September 1, 2019): 172988141988008. http://dx.doi.org/10.1177/1729881419880089.
Повний текст джерелаАнотація:
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 estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.
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Deist, Timo M., Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, and David Craft. "Simulation-assisted machine learning." Bioinformatics 35, no. 20 (March 23, 2019): 4072–80. http://dx.doi.org/10.1093/bioinformatics/btz199.
Повний текст джерелаАнотація:
Abstract Motivation In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems—three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. Availability and implementation The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern. Supplementary information Supplementary data are available at Bioinformatics online.
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Xu, Enyong, Fumin Wei, Changbo Lin, Yanmei Meng, Jihong Zhu, and Xin Liu. "Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles." AIP Advances 12, no. 7 (July 1, 2022): 075019. http://dx.doi.org/10.1063/5.0098223.
Повний текст джерелаАнотація:
The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.
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Backas, Joni, and Reza Ghabcheloo. "Nonlinear model predictive energy management of hydrostatic drive transmissions." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 3 (August 23, 2018): 335–47. http://dx.doi.org/10.1177/0959651818793454.
Повний текст джерелаАнотація:
In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.
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Yin, Derek, and Tony Z. Qiu. "Compatibility analysis of macroscopic and microscopic traffic simulation modeling." Canadian Journal of Civil Engineering 40, no. 7 (July 2013): 613–22. http://dx.doi.org/10.1139/cjce-2012-0104.
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To implement model predictive traffic control to reduce congestion, traffic state variables such as flow, speed, and density need to be accurately predicted with real-time measurements. To evaluate the accuracy of online prediction of a macroscopic traffic model, this paper compares the predicted flow, density, and speed from a macroscopic simulation model with those from a microscopic simulation model, using METANET and VISSIM respectively, on a section of urban freeway. Three levels of traffic demands and seven different time step lengths in macroscopic simulation were applied to evaluate the compatibility of the two models. It was concluded that in the macroscopic simulation model there exists an optimum time step length, under moderate to heavy traffic demands the predicted traffic states from the macroscopic simulation are consistent with the outputs from the microscopic simulation, and under stop-and-go traffic states significant difference exists between the two models.
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Yan, Gui Yun, and Zheng Zhang. "Predictive Control of a Cable-Stayed Bridge under Multiple-Support Excitations." Applied Mechanics and Materials 66-68 (July 2011): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.268.
Повний текст джерелаАнотація:
This paper presents a predictive control strategy for seismic protection of a benchmark cable-stayed bridge with consideration of multiple-support excitations. In this active control strategy, a multi-step predictive model is built to estimate the seismic dynamics of cable-stayed bridge and the effects of some complicated factors such as time-varying, model mismatching, disturbances and uncertainty of controlled system, are taken into account by the prediction error feedback in the multi-step predictive model. The prediction error is that the actual system output is compared to the model prediction at each time step. Numerical simulation is carried out for analyzing the seismic responses of the controlled cable-stayed bridge and the results show that the developed predictive control strategy can reduce the seismic response of benchmark cable-stayed bridge efficiently.
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Park, Kwang-Sung, Jin-Bae Park, Yoon-Ho Choi, Tae-Sung Yoon, and Guanrong Chen. "Generalized Predictive Control of Discrete-Time Chaotic Systems." International Journal of Bifurcation and Chaos 08, no. 07 (July 1998): 1591–97. http://dx.doi.org/10.1142/s0218127498001248.
Повний текст джерелаАнотація:
A generalized predictive control method based on an ARMAX model is suggested for chaos control in discrete-time systems. Both control performance and system sensitivity to initial conditions of this approach are compared with the conventional model-referenced adaptive control via numerical simulations. Simulation results show that this controller yields faster settling time, more accurate target tracking, and less initial sensitivity.
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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 (February 12, 2007): 527–33. http://dx.doi.org/10.1115/1.2745881.
Повний текст джерелаАнотація:
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 scheme in real time.
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Sawada, Masataka, Kazumoto Haba, and Muneo Hori. "Predictive Simulation for Surface Fault Occurrence Using High-Performance Computing." GeoHazards 3, no. 1 (February 24, 2022): 88–105. http://dx.doi.org/10.3390/geohazards3010005.
Повний текст джерелаАнотація:
Numerical simulations based on continuum mechanics are promising methods for the estimation of surface fault displacements. We developed a parallel finite element method program to perform such simulations and applied the program to reproduce the 2016 Kumamoto earthquake, where surface rupture was observed. We constructed an analysis model of the 5 × 5 × 1 km domain, including primary and secondary faults, and inputted the slip distribution of the primary fault, which was obtained through inversion analysis and the elastic theory of dislocation. The simulated slips on the surface were in good agreement with the observations. We then conducted a predictive simulation by inputting the slip distributions of the primary fault, which were determined using a strong ground motion prediction method for an earthquake with a specified source fault. In this simulation, no surface slip was induced in the sub-faults. A large surface slip area must be established near a sub-fault to induce the occurrence of a slip on the surface.
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Kim, Changkyum, Insik Chun, Byungcheol Oh, Hojin Lee, Jieun Choi, and Eunhye Mun. "Construction of Tidal Information Database on the Southern Coast of Korea and Prediction of the Tide." Korea Society of Coastal Disaster Prevention 9, no. 4 (October 30, 2022): 267–75. http://dx.doi.org/10.20481/kscdp.2022.9.4.267.
Повний текст джерелаАнотація:
The construction of the tidal information database study was conducted through numerical simulations for the southern coast of Korea. Because Korea has a complex coastline, the grid size of the numerical model was set to 0.1min(approx. 200m) to improve the accuracy of tidal prediction. The NAO.99jb, one of the ocean tide models, was employed in the study to increase the accuracy of the database. The numerical model applied to the database construction was calibrated and validated using observation data from the Korea hydrographic and oceanographic agency. To construct a high-resolution tidal information database, we conducted harmonic analysis for each computational grid point using numerical simulation results. In order to evaluate the tide and tidal current prediction performance of the tidal information database, numerical simulation results were used and compared. The results show that the predictive performance of the harmonic constant database is sufficient, so it can be used where rapid tidal prediction is required.