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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Konferenční objektpeer-reviewedpostprint Omezený přístup Application of Model Predictive Controller to Magnetic Levitation(IEEE (Institute of Electrical and Electronics Engineers), 2023) Novotný, Aleš; Honc, DanielModel 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-reviewedpostprint Omezený přístup RCDue - experimental identification of continuous- and discrete-time models(IEEE (Institute of Electrical and Electronics Engineers), 2023) Dušek, František; Honc, Daniel; Novotný, AlešThe 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, DanielThe 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, BrunoMicroplastics, 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, LiborThe 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, LiborThe 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.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Identification of Magnetic Levitation System(Springer Science and Business Media, 2021) Novotný, Aleš; Honc, Daniel; Dušek, FrantišekMagnetic 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, FrantišekBy 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.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup RCDue - Laboratory System for Teaching Automation and Control - Concept of the system(IEEE (Institute of Electrical and Electronics Engineers), 2021) Dušek, František; Honc, Daniel; Mrázek, MichalPaper describes an implementation of a low cost but nontrivial laboratory system for automation and control theory teaching purposes. The system called RCDue consists of two parts - a dynamic system module and a control unit providing measurement, control and communication with MATLAB through USB serial port. Wiring of the dynamic system module can be modified to get a system of the desired behavior. Application example named as R5C4 with four RC circuits is described together with its mathematical model and calculated step responses. The system can be used in laboratory for practical education of C or MATLAB programming, modelling and identification or control theory subjects.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Multiple Objects Localization Using Image Segmentation with U-Net(IEEE (Institute of Electrical and Electronics Engineers), 2021) Štursa, Dominik; Doležel, Petr; Honc, DanielPrecise object localization in an industrial environment is a significant task affecting follow-up processes for a pick and place application. One of the solutions to effectively ensure the success of this task is to use modern methods of machine vision. Machine vision is still a highly evolving topic, in which the use of approaches based on convolutional neural networks is rising. And so in this contribution, an innovative engineering approach based on convolutional neural networks is proposed for an object localization task. The approach is based on an atypical image segmentation, where the individual objects are represented by two colored gradient circles. These circles represent significant parts of the object like its center or ending. Each object type (class) is determined by a specific color. By use of a local maxima finder, all circles in an image are transformed to points. With knowledge of these points the coordinates and rotations are calculated. The proposed approach was tested on a legitimate localization problem with 100% precision, more than 99.52% recall on the positioning task and with an average of 6 minutes angle variance per object.