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Remote control of an embedded system via a mobile application using data transfer over Bluetooth
Dvořák, Richard ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
This work is focused on programming a mobile application that will be compatible with common mobile operating systems, and demonstrating two-way wireless communication between the application and the specified ESP32 module. Bluetooth wireless technology was chosen for communication between devices, and the mobile application was designed and programmed in the Qt Framework. To demonstrate the functionality of wireless communication, the ESP32 module is configured in the role of an SPP server, to which the mobile device connects in the role of a client.
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Bayesian approaches to adaptive system identification
Skalský, Ondřej ; ,, Straka Ondřej (referee) ; Dokoupil, Jakub (advisor)
The paper deals with Bayesian identification of time variant normal regression models and provides four key algorithms. The first two algorithms are designed for the online regularized identification of a single regression model. Absence of the time evolution model is solved in both algorithmizations by a data-informed forgetting technique. The choice of the forgetting factor in the first algorithm is performed using Variational Bayesian approximation. The second algorithm determines the value of the forgetting factor by statistical decision making. The second pair of algorithms treats the problem of time evolution of parameters as a sequence of switching normal regression models. In both of these offline algorithms which are based on the Variational Bayesian approximation, both the inference of the bank of model parameters and the activities of these models over the time of the experiment are iteratively performed. The actual number of models forming this bank is determined automatically. The difference between these algorithms lies mainly in the noise properties of the switched models. All four algorithms are tested on a real system and in simulations. The paper is complemented by a short introduction to the Bayesian world, which presents the essential statistical techniques that are used. For completeness and continuity with the aforementioned algorithms, the offline and online identification of a time invariant normal regression model is also described.
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Robust Student estimator
Hlavinka, Radek ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
This Master's thesis deals with Bayesian approach to robust parameter estimation for ARX models. Robustness is achieved by assuming the measurement noise to be generated by Student-t distribution. The asumption of Student-t noise renders the model's posterior intractable and requires utilization of approximation techniques. This thesis considers algorithms using Gibbs sampler and Variational approximation and compares them with Ordinary Least Squares. The algorithms are compared based on their Maximum Likelihood estimation. It is shown that approaches assuming the Student-t noise perform better in simulation. The results from data acquired from physical system are however similar for all algorithms considered.
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Adaptive controllers with principles of artificial intelligence and its comparison with classical identifications methods
Vaňková, Tereza ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
Master’s thesis is focused on the adaptive controllers. The first theoretic part mainly describes the parametric identification, which belongs to the most important part of the adaptive controller’s structure. Classical identification methods (the recursive least squares methods) are firstly mentioned and afterwards the identification methods based on the neural network (the Marquardt-Levenberg algorithm and the new identification algorithm NIA inspired by the neural networks) are described. At the conclusion of the theoretic part there are mentioned the algorithm of the adaptive controller’s tuning which uses the identification parameters (the modified Z-N method) and the tested types of adaptive controllers. Particular results, which were found out by verifying of the adaptive controllers on the simulation and real models, are contained in second, the practical, part of the thesis. Finally, achieved results are compared with the classical discrete PID controller and with the adaptive controller of the B&R company.
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Application of cable robot
Bulenínec, Martin ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
The thesis deals with the changes of a cable robot to a manipulator. The mechanical changes are mostly about adding an active part to a moving platform with the ability to transfer objects and the effort to exchange the silicon cables for metal ones. The main part of the thesis is the proposed design and implementation of the algorithm for detection of a possible collision of the cable robot with an object in its working space.
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Object positioning in 3D space using parallel cable-driven robot
Rajnoha, Andrej ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
At the beginning of this master’s thesis the definition of types of robots using parallel kinematics are presented, its possibilities of usage and current prototypes are described. The second chapter focuses on the proposal of robot construction and sizing electric and non-electric components of robot hardware. Derivation of direct and inverse transform mechanisms with creating flowcharts of their algorithms are stated in the two following chapters. The state machine controlled from user interface is then programmed based on these flowcharts. At the end of the work, cable-driven robot positioning accuracy is evaluated and platform workspace, together with motion and electric parameters, are measured.
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Mathematic model of steam turbine
Kroliczek, Filip ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
The goal of this thesis was to create a mathematical model of a steam turbine based on the data acquired by measurement, and to verify its behaviour. The first part contains research, which is supposed to introduce basic principles of the steam turbine and description of important construction parts and the possibilities of control. The second part describes the experimental identification method of least squares, used for the calculation of an ARX model of the steam turbine. Finally, the last part focuses on the program environment used for creation of the mathematical model and explanation of measured data analysis process. Furthermore this segment describes the created simulation program as well as a visualisation of the dynamic processes in the steam turbine, including the design of control.
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Optimal state estimation of a navigation model system
Papež, Milan ; Havlena, Vladimír (referee) ; Dokoupil, Jakub (advisor)
This thesis presents an investigation of the possibility of using the fixed-point arithmetic in the inertial navigation systems, which use the local level navigation frame mechanization equations. Two square root filtering methods, the Potter's square root Kalman filter and UD factorized Kalman filter, are compared with respect to the conventional Kalman filter and its Joseph's stabilized form. The effect of rounding errors to the Kalman filter optimality and the covariance matrix or its factors conditioning is evaluated for a various lengths of the fractional part of the fixed-point computational word. Main contribution of this research lies in an evaluation of the minimal fixed-point arithmetic word length for the Phi-angle error model with noise statistics which correspond to the tactical grade inertial measurements units.
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Adaptive controllers with principles of artificial intelligence and its comparison with classical identifications methods
Dokoupil, Jakub ; Malounek, Petr (referee) ; Pivoňka, Petr (advisor)
This piece of work deals with a philosophy of design adaptive controller, which is based on knowledge of mathematical model controlled plant. This master thesis is focused on closed-loop on-line parametric identification methods. An estimation of model´s parametres is solved by two main concepts: recursive leastsquare algorithms and neural estimators. In case of least-squares algorithm the strategy of preventing the typical problems are solved here. For instance numerical stability, accurecy and restricted forgetting. Back Propagation and Marquardt- Levenberg algorithm were choosen to represent artificial inteligence. There is still a little supermacy on the side of methods based on least-squares algorithm. To compare individual algorithms the grafical interface in MATLAB/Simulink was created.
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