Relevant bibliographies by topics / Modeling for control

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Modeling for control'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Modeling for control"

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van der Schaft, Arjan. "Port-Hamiltonian Modeling for Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 393–416. http://dx.doi.org/10.1146/annurev-control-081219-092250.

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This article provides a concise summary of the basic ideas and concepts in port-Hamiltonian systems theory and its use in analysis and control of complex multiphysics systems. It gives special attention to new and unexplored research directions and relations with other mathematical frameworks. Emergent control paradigms and open problems are indicated, including the relation with thermodynamics and the question of uniting the energy-processing view of control, as emphasized by port-Hamiltonian systems theory, with a complementary information-processing viewpoint.
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Lafortune, Stéphane. "Discrete Event Systems: Modeling, Observation, and Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 141–59. http://dx.doi.org/10.1146/annurev-control-053018-023659.

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This article begins with an introduction to the modeling of discrete event systems, a class of dynamical systems with discrete states and event-driven dynamics. It then focuses on logical discrete event models, primarily automata, and reviews observation and control problems and their solution methodologies. Specifically, it discusses diagnosability and opacity in the context of partially observed discrete event systems. It then discusses supervisory control for both fully and partially observed systems. The emphasis is on presenting fundamental results first, followed by a discussion of current research directions.
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Wanli Liu, Wanli Liu, Zhankui Wang Zhankui Wang, and Xiaoyang Li Xiaoyang Li. "Thermally Induced Errors Modeling and Control for PCMA." Chinese Optics Letters 12, s1 (2014): S11203–311207. http://dx.doi.org/10.3788/col201412.s11203.

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Wilkie, Richard M., John P. Wann, and Robert S. Allison. "Modeling locomotor control." ACM Transactions on Applied Perception 8, no. 2 (2011): 1–18. http://dx.doi.org/10.1145/1870076.1870077.

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Forbes, A. Dean. "Modeling and control." Journal of Clinical Monitoring 6, no. 3 (1990): 227–35. http://dx.doi.org/10.1007/bf02832152.

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Zare, A., T. T. Georgiou, and M. R. Jovanović. "Stochastic Dynamical Modeling of Turbulent Flows." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 195–219. http://dx.doi.org/10.1146/annurev-control-053018-023843.

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Advanced measurement techniques and high-performance computing have made large data sets available for a range of turbulent flows in engineering applications. Drawing on this abundance of data, dynamical models that reproduce structural and statistical features of turbulent flows enable effective model-based flow control strategies. This review describes a framework for completing second-order statistics of turbulent flows using models based on the Navier–Stokes equations linearized around the turbulent mean velocity. Dynamical couplings between states of the linearized model dictate structural constraints on the statistics of flow fluctuations. Colored-in-time stochastic forcing that drives the linearized model is then sought to account for and reconcile dynamics with available data (that is, partially known statistics). The number of dynamical degrees of freedom that are directly affected by stochastic excitation is minimized as a measure of model parsimony. The spectral content of the resulting colored-in-time stochastic contribution can alternatively arise from a low-rank structural perturbation of the linearized dynamical generator, pointing to suitable dynamical corrections that may account for the absence of the nonlinear interactions in the linearized model.
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Parsanejad, Mohammadreza, Ebrahim Teimoury, and Abozar Parsanejad. "Supply Chain Simulation and Modeling with Theory of Control." International Journal of Modeling and Optimization 4, no. 2 (2014): 167–70. http://dx.doi.org/10.7763/ijmo.2014.v4.367.

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C., Aguodoh Patrick, and Chiagunye Tochukwu T. "Modeling a Multivariable Process Control System using PID Optimization." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 433–39. http://dx.doi.org/10.31142/ijtsrd7016.

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Lin, Xinfan, Youngki Kim, Shankar Mohan, Jason B. Siegel, and Anna G. Stefanopoulou. "Modeling and Estimation for Advanced Battery Management." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 393–426. http://dx.doi.org/10.1146/annurev-control-053018-023643.

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The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.
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Batechko, N., M. Lut, S. Shostak, and O. Zinchenko. "Mathematical modeling of asynchronous electric drive with phase-impulse control." Energy and automation, no. 2(48) (April 29, 2020): 62–76. http://dx.doi.org/10.31548/energiya2020.02.062.

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Dissertations / Theses on the topic "Modeling for control"

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Barbosa, Fernando dos Santos. "4DOF Quadcopter: development, modeling and control." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-23102017-144556/.

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This text presents the development of a four-degree-of-freedom (4DOF) quadcopter prototype that allows the vehicle to rotate around the three axes (yaw, pitch and roll) and linear movement along z-axis (altitude). The goal is to obtain a prototype bench that uses a good amount of components used in commercial quadcopters (sensors and actuators) and use it to apply attitude and altitude controllers, using techniques such as PID, LQR and Sliding-Mode. Starting from the system modeling, its specifications are shown followed by listing the components used, finishing with the development of the controllers and their simulations and applications.<br>Este texto apresenta o desenvolvimento de um protótipo de quadricóptero com quatro graus de liberdade (4DOF), o qual possibilita a rotação do veículo em torno dos três eixos (yaw, pitch e roll) e o deslocamento ao longo do eixo z (altitude). O objetivo é obter um protótipo de bancada que use a maior quantidade de componentes de um quadricóptero comercial (sensores e atuadores) e usá-lo para a aplicação de controladores de atitude e altitude, utilizando técnicas PID, LQR e Sliding-Mode. Partindo da modelagem do sistema, mostra-se as especificações do mesmo, os componentes utilizados e finaliza-se com o desenvolvimento dos controladores, simulação e aplicação deles.
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Neves, Gabriel Pereira das. "Modeling, construction and control of a self-balancing unicycle." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-07112017-082249/.

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In this work, a unicycle system with reaction wheel is presented, considering the construction, the modeling, the design and test of the controllers. Firstly, a mechanical model considering a tridimensional computer aided design (3D CAD) is built in order to assist the construction and, after that, the modeling using the Lagrange method. In this work, linear controllers are designed and, therefore, the linearization of the system is done by the Jacobian, that is, assuming small variations around the equilibrium point. In this situation, there is no coupling between the pitch and the roll angles, thus resembling two inverted pendulums. The prototype is constructed by attaching the electronic components, the battery, the wheels and the motors to a body, to make it fully autonomous. The positioning of the parts has to balanced in order to maintain the position of the center of mass along the vertical and horizontal axis of symmetry. Then, a linear control project is done to stabilize the plant using two techniques that are validated considering simulations of the nonlinear coupled system. Then, the techniques were tested in the built prototype. The first one consists of the optimal LQR control that, although it worked, presented some problems due to parametric uncertainties. Therefore, the H2 control is used via LMI in such a way that the project becomes similar to the LQR, but in this way it is possible to insert parametric uncertainties and find a controller with some degree of robustness to them.<br>Neste trabalho, é apresentado um sistema de um monociclo com roda de reação, mostrando desde a construção, passando pela modelagem até o projeto e teste dos controladores. Primeiramente, é feito o projeto mecânico por meio de um desenho assistido por computador tridimensional (3D CAD), para auxiliar a construção e, em seguida, a modelagem por meio do método de Lagrange. Naturalmente, o sistema é não linear e os ângulos de arfagem e rolamento são acoplados. Neste trabalho, controladores lineares são projetados e, portanto, a linearização do sistema é feita pelo Jacobiano, ou seja, assumindo pequenas variações em torno do ponto de equilíbrio. Nesta situação, o modelo desacopla os ângulos de arfagem e rolamento. O protótipo é construído fixando os componentes eletrônicos, a bateria, as rodas e os motores a um corpo, de forma a ser totalmente autônomo. O posicionamento das peças precisa ser equilibrado, de forma a manter a posição do centro de massa ao longo dos eixos de simetria vertical e horizontal. Em seguida, é feito um projeto de controle linear para estabilização da planta usando duas técnicas que são validadas via simulações do sistema não linear acoplado. Depois, as técnicas são testadas no protótipo construído. A primeira consiste do controle ótimo LQR que, apesar de ter funcionado, apresentou alguns problemas devidos a incertezas paramétricas. Logo, é usado o controle H2 via LMI, de tal forma que o projeto equivalha ao LQR, mas desta forma é possível inserir incertezas paramétricas e achar um controlador com algum grau de robustez a elas.
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Harnischmacher, Gerrit. "Block structured modeling for control /." Düsseldorf : VDI-Verl, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016244726&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Claflin, Robert Alan. "Modeling control in computer simulations." Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/30927.

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This study outlines the design, implementation, and testing of the General Control Model as applied to the Future Theater-Level Model (FTLM) for the control of Joint and Allied Forces for all operational sides. The study develops a notion of battlefield control and describes the characteristics necessary to represent this notion of control in a computer simulation. Central to the implementation of the General Control Model is the robust capability for the user-analyst to describe any control relationship of research interest and to do so without having to alter the programming code. The user-analyst is provided the capability to determine the cause and effect relationship of different control representations in a simulation. A full description of the model is complimented by an explanation of the implementation to facilitate the use of the General Control Model. A discussion of the initial test results leads to a more rigorous test which confirms the intended behavior of the General Control Model in FTLM. Lastly, recommendations for future improvements to the General Control Model and FTLM are outlined to assist future research endeavors.
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Pais, Gabriel Dias. "Order book modeling and control." Instituto Tecnológico de Aeronáutica, 2012. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2209.

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On 6th May 2010, the stock market experienced in few minutes a large price decline and recovery collectively known as the Flash Crash. A serie of events on financial market before and during the flash crash setted up a high volume of transactions and unbalanced order flow collapsing into a lack of liquidity. The ultimate effect of this event was a challenge to the investor';s confidence in the markets. The financial regulators, responsibles to maintain the integrity of the financial makets, must be ahead of technological advances and be prepared to handle with unbalanced order flow and illiquidity scenarios. This work presents a preliminary and innovative solution for financial regulators to manage unbalanced order flow and to control an Order Book based on assumptions of Control Theory. Empirical simulations showed that in a high frequency world, algorithms could be used to control an Order Book and deal with Automatic Traders and Market Makers regulating the economy of supply and demand by adjusting execution fees. Under a stress scenario, when the Order Book become too unbalanced, the control system may change the fees attempting to induce the market makers to assume the role of counterpart of the Order book. The new orders may tend to balance the order flow and therefore prevent the imminent illiquidity scenario. Case studies show that an Order Book control can be an useful tool to manage unbalanced order flow and to promote market integrity.
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Pimentel, Guilherme Araujo. "Nonlinear Modeling, Identification and Control of Membrane Bioreactors." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS219/document.

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Cette thèse propose un modèle dynamique d'un bioréacteur à membrane submergée (sMBR) comprenant les comportements physiques et biologiques du processus. La filtration (aspect physique) est un modèle de résistances en série composé de la résistance réversible (liée au processus de formation d'un gâteau qui peut être enlevé par lavage de l'air) et de la résistance à colmatage irréversible. La fonction biologique est mise en œuvre par l'extension du modèle de chemostat simple avec un mécanisme de filtration.L'analyse du modèle comprend : l'analyse asymptotique, l'observabilité, la contrôlabilité et l'étude dynamique lente et rapide. Cette dernière, basée sur le théorème de Tikhonov, révèle la possibilité de simplifier la dynamique du modèle en découplant le processus en trois échelles de temps : l'évolution du colmatage à long terme (dynamique lente), la dégradation biologique ( dynamique rapide) et la formation du gâteau (dynamique ultrarapide). Comme les processus avec sMBRs sont relativement nouveaux, les données réelles de processus sont difficiles à obtenir. Ainsi, une installation pilote d'un système de recirculation de l'aquaculture avec une sMBR est conçue, construite et automatisée. Des mesures en ligne du processus, tels que la température, les matières en suspension (MES), l'ammoniac et les concentrations des effluents nitrates, la croisée de l'air et des débits d'effluents et la pression transmembranaire, sont réunis afin de valider le modèle proposé.Pour mettre en évidence le cadre général du modèle proposé, le même modèle est composé d'ensembles de données réelles obtenues à partir d'une installation de traitement des eaux usées à sMBR. Par conséquent, une identification de paramètre est organisée en trois étapes correspondant aux trois échelles de temps obtenues à partir de l'analyse analytique. L'identification de paramètre est implémentée en utilisant une fonction de coût aux moindres carrés pondérés et l'inverse de la Fisher Matrix Information (FIM), qui est utilisé pour obtenir les intervalles de confiance des paramètres calculées par une borne inférieure sur la matrice de covariance des estimations des paramètres. La capacité du modèle à prédire la pression transmembranaire et la dégradation biologique est prouvée par la validation du modèle et la validation croisée des résultats.Concernant le contrôle du processus, deux approches différentes sont utilisées : un contrôleur partielle linéaire basé sur la théorie de Lyapunov est conçu afin de stabiliser la production encrassement en actionnant dans la croisée de l'air et les flux d'effluents; une commande prédictive de modèle non linéaire (NMPC) est mise en œuvre afin d'optimiser le taux de production d'effluent et de maximiser la période entre deux opérations de lessivage chimique.Les résultats présentés dans cette thèse montrent l'importance des études analytiques sur des modèles afin de traiter la cognition et la simplification de modèle. Un autre point important est la structure du modèle dynamique simple avec une petite quantité de paramètres. Ce travail montre que cette structure est suffisante pour mettre en œuvre des stratégies de contrôle avancé sur les processus sMBR et même de prédire la dégradation biologique et la dynamique de croissance du colmatage<br>This thesis proposes a simple submerge membrane bioreactor (sMBR) dynamic model that comprises physical and biological process behaviors. The filtration, physical aspect, is a resistance-in-series model that is composed with reversible resistance, linked to sludge cake formation process that can be detached by air scouring, and the irreversible fouling resistance. The biological feature is implemented extending the simple chemostat model to the filtration mechanism. The model asymptotic analysis, observability, controllability and fast and slow dynamic study are carried out. The latter, based on the Tikhonov's theorem, reveals the possibility to simplify model dynamics by decoupling the process in three time scales, i.e. long-term fouling evolution (slow dynamic), biological degradation (fast dynamic) and fouling cake formation (ultrafast dynamic). As sMBR processes are relativity new, real process data are scarce. Thus, a recirculating aquaculture system pilot plant with an sMBR is design, build and automated. Process online measurements such as: temperature, total suspended solids (TSS), ammonia and nitrate effluent concentrations, air cross- and effluent flow rates and trans-membrane pressure are gathered in other to validate the proposed model. To evidence the model general framework the same model is confronted with real data sets obtained from an sMBR wastewater treatment plant. Therefore, a parameter identification is organized in three steps corresponding to the three time scales obtained from the analytical analysis. The parameter identification is implemented using a weighted least-squares cost function and the inverse of the Fisher Information Matrix (FIM), which is used to obtain the parameters confidence intervals, is computed by a lower bound on the covariance matrix of the parameter estimates. The model capacity to predict trans-membrane pressure and biological degradation is proved by model validation and cross-validation results, in which an accurate correlation coefficients (R^2) of approximately 0.83 are obtained. Concerning the process control, two different approach are used: a partial-linearizing feedback Lyapunov controller is designed in order to stabilize the fouling production by actuating in the air cross- and effluent flows; and a nonlinear model predictive control (NMPC) is implemented in other to optimize the effluent production rate and maximize the period between two chemical cleaning procedures. The results included in this thesis show the importance of analytical model studies in order to process cognition and model simplification. Another important point is the simple dynamic model structure, with a small quantity of the parameters, which is adequate to implement advanced control strategies on sMBR processes and, similarly, to predict biological degradation and fouling build-up dynamics
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Agi, Egemen. "Mathematical Modeling Of Gate Control Theory." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611468/index.pdf.

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The purpose of this thesis work is to model the gate control theory, which explains the modulation of pain signals, with a motivation of finding new possible targets for pain treatment and to find novel control algorithms that can be used in engineering practice. The difference of the current study from the previous modeling trials is that morphologies of neurons that constitute gate control system are also included in the model by which structure-function relationship can be observed. Model of an excitable neuron is constructed and the response of the model for different perturbations are investigated. The simulation results of the excitable cell model is obtained and when compared with the experimental findings obtained by using crayfish, it is found that they are in good agreement. Model encodes stimulation intensity information as firing frequency and also it can add sub-threshold inputs and fire action potentials as real neurons. Moreover, model is able to predict depolarization block. Absolute refractory period of the single cell model is found as 3.7 ms. The developed model, produces no action potentials when the sodium channels are blocked by tetrodotoxin. Also, frequency and amplitudes of generated action potentials increase when the reversal potential of Na is increased. In addition, propagation of signals along myelinated and unmyelinated fibers is simulated and input current intensity-frequency relationships for both type of fibers are constructed. Myelinated fiber starts to conduct when current input is about 400 pA whereas this minimum threshold value for unmyelinated fiber is around 1100 pA. Propagation velocity in the 1 cm long unmyelinated fiber is found as 0.43 m/s whereas velocity along myelinated fiber with the same length is found to be 64.35 m/s. Developed synapse model exhibits the summation and tetanization properties of real synapses while simulating the time dependency of neurotransmitter concentration in the synaptic cleft. Morphometric analysis of neurons that constitute gate control system are done in order to find electrophysiological properties according to dimensions of the neurons. All of the individual parts of the gate control system are connected and the whole system is simulated. For different connection configurations, results of the simulations predict the observed phenomena for the suppression of pain. If the myelinated fiber is dissected, the projection neuron generates action potentials that would convey to brain and elicit pain. However, if the unmyelinated fiber is dissected, projection neuron remains silent. In this study all of the simulations are preformed using Simulink.
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Moberg, Stig. "Modeling and Control of Flexible Manipulators." Doctoral thesis, Linköpings universitet, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60831.

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Industrial robot manipulators are general-purpose machines used for industrial automation in order to increase productivity, flexibility, and product quality. Other reasons for using industrial robots are cost saving, and elimination of hazardous and unpleasant work. Robot motion control is a key competence for robot manufacturers, and the current development is focused on increasing the robot performance, reducing the robot cost, improving safety, and introducing new functionalities.  Therefore, there is a need to continuously improve the mathematical models and control methods in order to fulfil conflicting requirements, such as increased performance of a weight-reduced robot, with lower mechanical stiffness and more complicated vibration modes. One reason for this development of the robot mechanical structure is of course cost-reduction, but other benefits are also obtained, such as lower environmental impact, lower power consumption, improved dexterity, and higher safety. This thesis deals with different aspects of modeling and control of flexible, i.e., elastic, manipulators. For an accurate description of a modern industrial manipulator, this thesis shows that the traditional flexible joint model, described in literature, is not sufficient. An improved model where the elasticity is described by a number of localized multidimensional spring-damper pairs is therefore proposed. This model is called the extended flexible joint model. The main contributions of this work are the design and analysis of identification methods, and of inverse dynamics control methods, for the extended flexible joint model. The proposed identification method is a frequency-domain non-linear gray-box method, which is evaluated by the identification of a modern six-axes robot manipulator. The identified model gives a good description of the global behavior of this robot. The inverse dynamics problem is discussed, and a solution methodology is proposed. This methodology is based on the solution of a differential algebraic equation (DAE). The inverse dynamics solution is then used for feedforward control of both a simulated manipulator and of a real robot manipulator. The last part of this work concerns feedback control. First, a model-based nonlinear feedback control (feedback linearization) is evaluated and compared to a model-based feedforward control algorithm. Finally, two benchmark problems for robust feedback control of a flexible manipulator are presented and some proposed solutions are analyzed.
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Haugen, Morten. "Modeling and Control of ROV Manipulator." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18614.

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The main objective of this thesis is to investigate and present the most relevant techniques and topics within the field of robot modeling and control. The studies will then be used to develop a working control system for the &apos;Raptor&apos; manipulator stationed on the ROV &apos;SubFighter 30K&apos;. Due to insufficient information, a simplified model is made to resemble the actual manipulator. This model forms the foundation of all subsequent actions, including the model based control design. The dynamic model is developed by the well known method of Euler-Lagrange. Since this is an energy based method, both the kinetic and the potential energy of the system must be calculated. Systematic procedures are given to clarify the process of these calculations. In this thesis, a sliding-mode controller is derived and proposed as a suitable controller for the given manipulator. The control objective is to force the manipulator to track a time dependent, desired path in the joint space. However, since it is inconvenient for the operator to specify joint space trajectories, several inverse kinematics algorithms are suggested. Due to the kinematic structure of the manipulator, no closed-form solutions can be obtained. The focus is thus directed towards numerical Jacobian based methods. A full-scale implementation requires a working interface between the developed control system and the manipulator system. For that reason, the main concepts of digital communication are presented. Although no communication data is logged from the Raptor, this presentation will pose an advantage if the work is continued. When no control forces are applied to the dynamic model, the manipulator model is expected to behave like a multi joint, three dimensional pendulum. The simulations corresponds to this assumptions, thus the model is assumed to be correct and valid. Simulations of the complete system shows that the sliding-mode controller works as intended. Two chosen IK algorithms are then implemented and compared through simulations. The DLS method proves to be superior to the simple inverse Jacobian method. Finally, the control system is implemented in LabVIEW and thus prepared for full-scale testing.
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Leufvén, Oskar. "Modeling for control of centrifugal compressors." Doctoral thesis, Linköpings universitet, Fordonssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91384.

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Downsizing and turbocharging of engines provide a way to meet increasing demands for efficiency and performance in the automotive industry. An engine design is a result of compromises, e.g. the selection of charging system, and the trend is to reduce these compromises by increasing system complexity. Models have come to play a central role to handle this rise in complexity, and are used for simulation, system optimization and control synthesis. The models should describe the entire operating range, be capable of extrapolation, be easily parameterizable, and wide cover a range of applications. A novel compressor model is developed which, in addition to the nominal operation, also covers surge, choke and operation at pressure ratios less than one. The model is based on data from more than 300 compressor maps, measurements from engine test stands, and a surge test stand. The general knowledge gained from the in-depth analysis is condensed in the model equations. The model can be automatically parametrized using a compressor map, is based on static functions for low computational cost, and is shown to extrapolate low speed compressor operation well. Furthermore, it is shown to be applicable to compressors of different size, ranging from small car applications to large heavy duty vehicles. Compressor restriction operation is modeled down to a standstill compressor, and shown to agree well with gas stand measurements. Further, the analysis contributes with new knowledge and models for choking pressure ratio and flow. A method to automatically determine a turbo map, when the turbo is installed on an engine in an engine test stand is developed. The method can be used to validate manufacturer maps or expand the region covered in a map. An analysis of the limits that an engine installation imposes on the reachable points in the compressor map is performed. The addition of a throttle before the compressor is suggested to increase the reachable map region, and an engine and test cell control structure that can be used to automate the measurements is proposed. Two methods that compensate for the deviation between measured and desired speeds, are proposed and investigated. A gas stand map is compared to the map generated in the engine test stand, and a generally good agreement results. An experimental analysis of the applicability of the commonly used correction factors, used for estimating compressor performance when the inlet conditions deviate from nominal, is performed. Correction factors are vital, to e.g. estimate turbocharger performance for driving at high altitude or to characterize second stage compressor performance, where the variations in inlet conditions are large. Measurements from an engine test stand and a gas stand show a small but clearly measurable trend, with decreasing compressor pressure ratio for decreasing compressor inlet pressure, for points with equal corrected shaft speed and corrected mass flow. A method that enables measurements to be analyzed with modified corrections is developed. As a result, an adjusted shaft speed correction quantity is proposed, incorporating also the inlet pressure in the shaft speed correction.
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Books on the topic "Modeling for control"

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Piegat, Andrzej. Fuzzy Modeling and Control. Physica-Verlag HD, 2001.

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Fuzzy modeling for control. Kluwer Academic Publishers, 1998.

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IIASA, Conference on "Discrete Event Systems" (1987 Sopron Hungary). Modeling and adaptive control. Springer-Verlag, 1988.

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Seth, Hutchinson, and Vidyasagar M. 1947-, eds. Robot modeling and control. John Wiley & Sons, 2005.

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Babuška, Robert. Fuzzy modeling for control. Springer, 1998.

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Spong, Mark W. Robot modeling and control. John Wiley & Sons, 2006.

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Aeroservoelasticity: Modeling and control. Birkhäuser, 2015.

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Eyman, Earl D. Modeling, simulation, and control. West Pub. Co., 1988.

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Isermann, Rolf. Engine Modeling and Control. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39934-3.

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Babuška, Robert. Fuzzy Modeling for Control. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4868-9.

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Book chapters on the topic "Modeling for control"

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Isermann, Rolf. "Vehicle Dynamics Modeling." In Automotive Control. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-39440-9_4.

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Ding, Wenjing. "Modeling and Control." In Self-Excited Vibration. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-69741-1_11.

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Yamaguchi, Gary Tad. "Control." In Dynamic Modeling of Musculoskeletal Motion. Springer US, 2001. http://dx.doi.org/10.1007/978-0-387-28750-8_7.

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Babuška, Robert. "Fuzzy Modeling." In Fuzzy Modeling for Control. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4868-9_2.

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Liu, Xinzhi, and Peter Stechlinski. "Switching Control Strategies." In Infectious Disease Modeling. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53208-0_5.

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Liu, Xinzhi, and Peter Stechlinski. "Pulse Control Strategies." In Infectious Disease Modeling. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53208-0_6.

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Piegat, Andrzej. "Fuzzy Control." In Fuzzy Modeling and Control. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1824-6_7.

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Janiszowski, Krzysztof, Józef Korbicz, Krzysztof Patan, and Marcin Witczak. "Process Modeling." In Modeling, Diagnostics and Process Control. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16653-2_3.

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Haefner, James W. "Hormonal Control in Mammals." In Modeling Biological Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-4119-6_12.

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Cai, Guowei, Ben M. Chen, and Tong Heng Lee. "Flight Dynamics Modeling." In Advances in Industrial Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-635-1_6.

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Conference papers on the topic "Modeling for control"

1

Elshazly, Osama, Ahmed Abo-Ismail, Hossam S. Abbas, and Zakarya Zyada. "Skid steering mobile robot modeling and control." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915116.

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Kumar, Manoj, A. K. Verma, A. Srividya, and P. P. Marathe. "Developments in dependability modeling of Networked Control Systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334650.

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Dong, Yue, Yang Liu, Fazhi Song, Li Li, and Kang Li. "Modeling and sliding-mode control of wafer stage in lithrography machines." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737578.

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Ng, Bing Feng, Henrik Hesse, Eric C. Kerrigan, Rafael Palacios, and J. Michael R. Graham. "Efficient aeroservoelastic modeling and control using trailing-edge flaps of wind turbines." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915106.

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Na, Tuopu, Qianfan Zhang, and Chaowei Zhou. "Modeling and design of quasi-Z-source inverter for PMSM drive system." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737524.

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Na, Tuopu, Qianfan Zhang, and Chaowei Zhou. "Modeling and design of quasi-Z-source inverter for PMSM drive system." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737527.

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Duarte, Franklyn, and Christian Bohn. "Modeling and centralized sliding mode control of a two-flexible-link robot." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737640.

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Shen, Yan-xia, Fan Li, Dinghui Wu, Ting-long Pan, and Xiang-xia Liu. "dSpace based direct-driven permanent magnet synchronous wind power system modeling and simulation." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334734.

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Bruja, Adrian, Luige Vladareanu, Hongnian Yu, Hongbo Wang, and Jingjing Liu. "The stability performances improvement through kinematic and dynamic modeling of the hopping robots." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915189.

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Schultze, Martin, Michael Kirsten, Sven Helmker, and Joachim Horn. "Modeling and simulation of a coupled double-loop-cooling system for PEM-fuel cell stack cooling." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334744.

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Reports on the topic "Modeling for control"

1

Morari, M. Modeling for process control. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5951697.

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Schwartz, C. Modeling transverse orbit feedback control. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/774052.

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Hauser, John E. Modeling and Control of Nonlinear Systems. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada308161.

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Bennett, W. H., G. L. Blankenship, and H. G. Kwatny. Modeling and Control of Flexible Structures. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177106.

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Leitner, Amiram. Thrust Vector Control, Heat Transfer Modeling. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada522372.

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Morari, M. Modeling for process control. Progress report. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/10116349.

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Scholl, Dennise, Patrick Mason, Lisa Sayegh, Roxanne Constable, and Amanda Tijerina. Crowd Control Modeling and Simulation Research Plan. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416783.

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Lev S. Tsimring. Modeling for Process Control: High-Dimensional Systems. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/937090.

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Beckey, Carl, Roy Hartfield, and Mark Carpenter. Compressor Modeling for Engine Control and Maintenance. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada546258.

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Atkeson, Christopher G. Nonparametric Modeling and Control of High Performance Maneuvers. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329721.

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