Digitální knihovnaUPCE
 

Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics

Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847

Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce

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  • Článekpeer-reviewedpostprintOmezený přístup
    Generalized first-principle model of magnetic levitation
    (2023) Dušek, František; Tuček, Jiří; Novotný, Aleš; Honc, Daniel
    Since its first demonstration more than a half century ago, magnetic levitation (MagLev) has gained eminent scientific attention from both the fundamental and applied points of view. In essence, MagLev shows highly nonlinear dynamics, described with nonlinear differential equations. Thus, in order to exploit the MagLev phenomenon, both mathematical models and control algorithms must be constructed. Frequently authors use simplifications of the model, and in doing so, limit the application of the MagLev model around a nominal operating point. In these simplified cases, the MagLev models may contain parameters that are not represented by proper physical quantities. Thus, in this work, we revised the issue of MagLev modelling from the first-principle approach. More specifically, we theoretically derived expressions for the interaction between the magnetic fields of the solenoid and a small magnetic object. The behaviour of the inductance on a distance from the solenoid was then described. The suggested MagLev modelling concept was verified experimentally, confirming the validity and correctness of the proposed MagLev mathematical model. The results presented here could thus be regarded as highly beneficial for formulating more complex MagLev designs exploitable in the field of model predictive control of the position of a levitating object.
  • Konferenční objektpeer-reviewedpostprintOmezený přístup
    Application of Model Predictive Controller to Magnetic Levitation
    (IEEE (Institute of Electrical and Electronics Engineers), 2023) Novotný, Aleš; Honc, Daniel
    Model Predictive Control (MPC) is an advanced process control method that is widely used for controlling both linear and under some modifications for non-linear systems. The aim of this work is to show a way how to apply MPC to a non-linear Magnetic Levitation System (MLS) and its capability of stabilization and closed-loop performance. This work is a continuation of the previous article where the laboratory plant CE 152 MLS was identified, and a non-linear model was designed. This paper proposes a control circuit consisting of linearized discretized non-linear MLS model, Extended Kalman Filter (EKF) algorithm for state estimation and linear MPC. The results are verified in simulation and real-world experiment.
  • Konferenční objektpeer-reviewedpostprintOmezený přístup
    RCDue - experimental identification of continuous- and discrete-time models
    (IEEE (Institute of Electrical and Electronics Engineers), 2023) Dušek, František; Honc, Daniel; Novotný, Aleš
    The paper is devoted to the education and teaching of process control and automation. Various laboratory equipment is used to explain and better understand the theory and to gain practical experience. The authors have designed and developed a simple electrical dynamical system RCDue (dynamic model with passive RC components and Arduino Due as measurement and communication unit) that allows students to perform various laboratory experiments – e.g. static and dynamic characteristics measurements, modeling, experimental identification, control design and application of from the simplest strategies to advanced methods. Specifically, in this paper, the authors focus on experimental identification.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Otevřený přístup
    Grasping Point Detection Using Monocular Camera Image Processing and Knowledge of Center of Gravity
    (Springer Nature Switzerland AG, 2022) Štursa, Dominik; Doležel, Petr; Honc, Daniel
    The ability to grasp objects is one of the basic functions of modern industrial robots. In this article, the focus is placed on a system for processing the image provided by a robot visual perception system leading to the detection of objects grasping points. The proposed processing system is based on a multi-step method using convolutional neural networks (CNN). The first step is to use the first CNN to transform the input image into a schematic image with labeled objects centers of gravity, which then serves as a supporting input to the second CNN. In this second CNN, original input and supporting input images are used to obtain a schematic image containing the grasping points of the objects. This solution is further compared with a network providing grasping points directly from the input image. As a result, the proposed method provided a 0.7% improvement in the average intersection over union for all of the models.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Otevřený přístup
    Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study
    (SciTePress - Science and Technology Publications, 2022) Doležel, Petr; Roleček, Jiří; Honc, Daniel; Štursa, Dominik; Baruque Zanon, Bruno
    Microplastics, i.e. synthetic polymers that have particle size smaller than 5 mm, are emerging pollutants that are widespread in the environment. In order to monitor environmental pollution by microplastics, it is necessary to have available rapid screening techniques, which provide the accurate information about the quality (type of polymer) and quantity (amount). Spectroscopy is an indispensable method, if precise classification of individual polymers in microplastics is required. In order to contribute to the topic of autonomous spectra matching when using spectroscopy, we decided to demonstrate the quality and efficiency of neural networks. We adopted three neural network architectures, and we tested them for application to spectra matching. In order to keep our study transparent, we use publicly available dataset of FTIR spectra. Furthermore, we performed a deep statistical analysis of all the architectures performance and efficiency to show the suitability of neural networks for spectra matching. The results presented at the end of this article indicated the overall suitability of the selected neural network architectures for spectra matching in microplastics classification.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Explanation of the predictive controller and the effect of its tuning on the control quality
    (Springer Nature Switzerland AG, 2022) Honc, Daniel; Novotný, Aleš; Kupka, Libor
    The Model Predictive Control (MPC) concept and its realization is explained together with some real-world laboratory application examples. Usually, future control errors and control action changes are penalized in the cost function (objective) by predictive controllers. The cost function can be seen as a function of future control actions utilizing the process model in a form of the predictor. A derivation of the predictors for the transfer function (external) and state-space (internal) model is indicated in the paper. An analytical solution to the given optimization problem is possible in an unconstrained case. A quadratic programming strategy must be used in case of the occurrence of the process constraints that should be respected by the controller. The authors apply both algorithms to two types of dynamical systems – to proportional (stable) system and to integrating (unstable) system and they demonstrate the influence of the penalization parameters (weights) in the cost function on the control quality. The authors aim to high-light the MPC strategy and its potential, and on the other hand, mention some bottlenecks and risks associated with model-optimisation-based methods.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Process Control Laboratory
    (Springer Nature Switzerland AG, 2022) Honc, Daniel; Novotný, Aleš; Havlíček, Libor
    The authors are presenting Process Control Laboratory at the Faculty of Electrical Engineering and Informatics, University of Pardubice. The laboratory is equipped with six GUNT training systems covering common technological variables such as level, flow, pressure, temperature, speed, and position. The authors created SW support for MATLAB and Simulink environment. An internal part of the GUNT training systems is the LabJack U12 data acquisition card. LabJack Dynamic Link Library (DLL) is used to operate training systems from MATLAB. Authors created the M-S function to allow experimenting from Simulink. The possibilities of the proposed solution are demonstrated in several control applications.
  • Článekpeer-reviewedpublishedOtevřený přístup
    Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
    (MDPI, 2022) Mrázek, Michal; Honc, Daniel; Sanseverino, Eleonora Riva; Zizzo, Gaetano
    Featured Application The potential application of the proposed model is a computationally inexpensive semi- or fully automated system for the optimization of operation in residential buildings in terms of energy consumption. Some analyses state that buildings contribute to overall energy consumption by 20-40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REPowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house's real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Identification of Magnetic Levitation System
    (Springer Science and Business Media, 2021) Novotný, Aleš; Honc, Daniel; Dušek, František
    Magnetic Levitation Systems (MLS) are usually highly non-linear systems with a great sensitivity to the size of the control input. Therefore, special emphasis should be placed on the correct identification of all unknown MLS parameters. This paper describes the principle and procedure for identifying laboratory plant CE 152 MLS with an emphasis on automatically processing identification data. Moving-Average Filter (MAF) and Fast Fourier Transform Filter (FFTF) methods are compared to filtering input data noise. Key parameters are then estimated using the Least Squares Method (LSM). The results are verified in simulation and real-world experiment using a simple PID controller.
  • Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Non-square Multivariable System Control Case Study – Static Optimal Compensator Design and Application
    (Springer Science and Business Media, 2021) Varga, Dominik; Honc, Daniel; Dušek, František
    By multivariable decentralized control, changing one set-point in result acts as a disturbance to other control loops. This can be solved by using multivariable controller or compensator. In this paper, a novelty approach to control non-square eighth-order system with four inputs and three outputs is demonstrated using a static compensator that guarantees autonomy in the steady state (changing one input, affects one output) and also optimal solution for non-square overdetermined systems (systems with more manipulated variables than controlled variables). To evaluate the control quality of this method, the system is also controlled without static compensator for comparison.