Full-time study

4 years

prof. Ing. Pavel Václavek, Ph.D.

Chairman :
prof. Ing. Pavel Václavek, Ph.D.
Councillor internal :
doc. Ing. Zdeněk Bradáč, Ph.D.
prof. Ing. Pavel Jura, CSc.
doc. Ing. Petr Beneš, Ph.D.
doc. RNDr. Zdeněk Šmarda, CSc.
Councillor external :
prof. Ing. Pavel Ripka, CSc.
Prof. Ing. Roman Prokop, CSc.
doc. Ing. Eduard Janeček, CSc.
prof. Dr. Ing. Alexandr Štefek, Dr.
prof. Ing. Tomáš Vyhlídal, Ph.D.

The doctor study programme "Cybernetics, Control and Measurements" is devoted to the preparation of the high quality scientific and research specialists in various branches of control technology, measurement techniques, automatic systems, robotics, artificial intelligence and computer vision.
The aim is to provide the doctor education in all these particular branches to students educated in university magister study, to make deeper their theoretical knowledge, to give them also requisite special knowledge and practical skills and to teach them methods of scientific work.
Through a systematic and comprehensive view of management and measurement, graduates of the study program successfully apply to key management and managerial positions and functions in which they use system view, knowledge of system analysis and optimal management.

Graduate of doctoral studies is profiled to independent creative work and critical thinking based on the systemic view of both technical and non-technical systems and the world as a whole. The graduate program is equipped with the necessary knowledge of mathematics, physics, electrical engineering, theory and practice of control and regulation, measuring techniques, robotics, artificial intelligence, image processing and other fields of applied electrical engineering and informatics. One of the characteristic features of graduates is the ability to integrate a broad spectrum of knowledge and to create functional technical as well as organizational and economic systems.
All graduates of the doctoral program Cybernetics, Automation and Measurement demonstrate during their studies:
• mathematical, physical and electrotechnical principles relevant to measurement and control;
• electronic measuring systems, embedded systems, communication systems, control theory, automatic control systems and artificial intelligence;
• design and operation of electrotechnical, electronic, measuring, control and communication systems.
The graduates are well versed in modern technologies (Industry 4.0, Artificial Intelligence, Signal Processing, Computer Vision, Advanced Management Methods, Industrial Measurement and Control Systems, Mobile and Stationary Robotics, Communication Systems, Functional and System Security).
The graduates are trained to find the work in technical practice, creative work, research and development, production, management and managerial positions in technical or business firms and companies at the highest qualification levels.

Graduates will apply in particular:
- in research, development and design teams,
- in the field of professional activity in production or business organizations,
- in the academic sphere and in other institutions involved in science, research, development and innovation,
- in all areas of the company where cybernetic systems or cybernetic principles are being applied
Our graduates are particularly experienced in the analysis, design, creation or management of complex measurement or control systems, as well as in the programming, integration, support, maintenance or sale of these systems.

Doctoral studies are carried out according to the individual study plan, which is prepared by the supervisor in the beginning of the study in cooperation with the doctoral student. The individual curriculum specifies all the duties determined in accordance with the BUT Study and Examination Rules, which the doctoral student must fulfill to successfully finish his studies. These responsibilities are time-bound throughout the study period, they are scored and fixed at fixed deadlines.
Students will write and perform tests of obligatory subjects (Selected Chapters of Control Engineering, Selected Chapters of Measurement Techniques and Exam in English before the state doctoral examination), at least two compulsory elective courses in view of the focus of his dissertation, at least two optional subjects (English for PhD students; Quoting in Scientific Practice; Resolving Innovation Assignments; Scientific Publishing from A to Z).
The student may enroll for the state doctoral exam only after all the tests prescribed by his / her individual study plan have been completed. Before the state doctoral exam, the student draws up a dissertation thesis describing in detail the aims of the thesis, a thorough evaluation of the state of knowledge in the area of ​​the dissertation solved, or the characteristics of the methods it intends to apply in the solution.
The defense of the controversy that is opposed is part of the state doctoral exam. In the next part of the exam, the student must demonstrate deep theoretical and practical knowledge in the field of electrical engineering, control technology, cybernetics and measuring techniques. The state doctoral examination is in oral form and, in addition to the discussion on the dissertation thesis, it also consists of thematic areas related to compulsory and compulsory elective subjects.
To defend the dissertation, the student reports after the state doctoral examination and after fulfilling conditions for termination, such as participation in teaching, scientific and professional activity (creative activity) and at least a monthly study or work placement at a foreign institution or participation in an international creative project. The studies are finished by successful defence of the dissertation thesis.

The doctoral studies of a student follow the Individual Study Plan (ISP), which is defined by the supervisor and the student at the beginning of the study period. The ISP is obligatory for the student, and specifies all duties being consistent with the Study and Examination Rules of BUT, which the student must successfully fulfill by the end of the study period. The duties are distributed throughout the whole study period, scored by credits/points and checked in defined dates. The current point evaluation of all activities of the student is summarized in the “Total point rating of doctoral student” document and is part of the ISP. At the beginning of the next study year the supervisor highlights eventual changes in ISP. By October, 15 of each study year the student submits the printed and signed ISP to Science Department of the faculty to check and archive.
Within the first four semesters the student passes the exams of compulsory, optional-specialized and/or optional-general courses to fulfill the score limit in Study area, and concurrently the student significantly deals with the study and analysis of the knowledge specific for the field defined by the dissertation thesis theme and also continuously deals with publishing these observations and own results. In the follow-up semesters the student focuses already more to the research and development that is linked to the dissertation thesis topic and to publishing the reached results and compilation of the dissertation thesis.
By the end of the second year of studies the student passes the Doctor State Exam, where the student proves the wide overview and deep knowledge in the field linked to the dissertation thesis topic. The student must apply for this exam by April, 30 in the second year of studies. Before the Doctor State Exam the student must successfully pass the exam from English language course.
In the third and fourth year of studies the student deals with the required research activities, publishes the reached results and compiles the dissertation thesis. As part of the study duties is also completing a study period at an abroad institution or participation on an international research project with results being published or presented in abroad or another form of direct participation of the student on an international cooperation activity, which must be proved by the date of submitting the dissertation thesis.
By the end of the winter term in the fourth year of study the students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The final dissertation thesis is expected to be submitted by the student by the end of the fourth year of studies.
In full-time study form, during the study period the student is obliged to pass a pedagogical practice, i.e. participate in the education process. The participation of the student in the pedagogical activities is part of his/her research preparations. By the pedagogical practice the student gains experience in passing the knowledge and improves the presentation skills. The pedagogical practice load (exercises, laboratories, project supervision etc.) of the student is specified by the head of the department based on the agreement with the student’s supervisor. The duty of pedagogical practice does not apply to students-payers and combined study program students. The involvement of the student in the education process within the pedagogical practice is confirmed by the supervisor in the Information System of the university.

1. Advanced methods for optimizing industrial production systems according to I4.0 principles

   Research in advanced optimization methods used in digital and smart factories with application in industrial control systems in line with the Industry 4.0 initiative. In the initial phase, the categorization of current approaches in the field of IT methods for the segment of operational technologies encapsulated by AAS (Asset Administration Shell) technologies is assumed. A significant part of the activities will be devoted to methods of distributed planning and anomaly detection in the system, while using formal mathematical tools (e.g. discrete event systems or transit systems for modeling and verification, temporal logic for requirements definition) and machine learning techniques with the use of AI. Practical verification of research results will take place in the form of simulations and deployment on a laboratory testbed using AAS. The aim of the research is to explore the possibilities of machine learning and AI techniques to increase the functional capabilities and flexibility of industrial systems.

   Tutor: Bradáč Zdeněk, doc. Ing., Ph.D.

2. Advanced sensor structures for measurement in ultrasonic range

   The topic is focused on research in the field of advanced sensor structures (MEMS, fibre-optical, arrays) and related methods for signal processing in the acoustic field usable for measurement of signals generated in solid materials for their non-disassembly diagnostics and also for measurement ultrasonic signals transmitted in free space. A limitation of the classic piezoelectric sensors, which are currently the most frequently used, is the complexity of the implementation of broadband sensitive elements and their dimensions. Research work will therefore focus on the design, simulation, optimization and characterization of such MEMS structures that provide sensing and analysis of ultrasonic signals in a wider frequency range and research methods for signal processing to optimize dimensions and energy consumption of the whole sensor. Possibility to define the necessary parameters of sensors in the design phase will ensure the subsequent high application potential in technical diagnostics and also in chemical and pharmaceutical industries. The research will be carried out in connection with already running and planned national and international projects.

   Tutor: Havránek Zdeněk, Ing., Ph.D.

3. Autonomous Operations of Unmanned Aerial Systems in Reconnaissance Robotics

   Research on the field of autonomous unmanned aerial reconnaissance focused on environmental data collection, creation of 3D maps and cooperation of multiple mobile platforms. Current approaches in trajectory planning, creation of various types of 3D maps of the machine surrounding, obstacle avoidance and other areas necessary for safe autonomous operation of air robotic reconnaissance vehicles in a complex outdoor environment will be studied. Based on the current state of knowledge of the issue, suitable algorithms and methods for solving this problem will be chosen. The proposed solution will then be tested in a simulated environment and compared with current best methods. The goal is also the implementation on real unmanned aircrafts, that are available at the ÚAMT.

   Tutor: Žalud Luděk, prof. Ing., Ph.D.

4. Data analysis methods for anomaly detection

   The research topic is focused on the area of automated analysis of the process data, particularly on the analysis of time series for the purpose of anomaly detection in technical systems or human-machine systems. Subject matter of the research should be creation and analysis of models in systems which afford a training data set. Application of modern methods for signal processing and analysis is expected, such as signal transforms, sparse signal representation, or machine learning. Purpose of the research is to enable development and optimisation of anomaly or fault detectors in applications with a relatively small dataset. Research will be done in connection with ongoing research projects and in cooperation with the industry.

   Tutor: Bradáč Zdeněk, doc. Ing., Ph.D.

5. Deep Learning in Mobile Robotics

   Machine learning methods, especially deep neural networks, find application in a wide range of research disciplines, including mobile robotics. The aim of the topic is to explore the current state of knowledge and potential applications of deep learning in areas of robotics such as environmental understanding, estimation of environment traversability, robust control, multimodal data fusion, and others. Attention must be paid to all major paradigms of machine learning, however, special emphasis should be placed on the area of reinforcement learning. This paradigm, based on the trial-and-error principle and to some extent resembling the way humans learn, proves to be very effective especially in solving complex kinematic tasks and brings a small revolution to mobile robotics. The goal is also to focus on methods that can be implemented on mobile robotic systems due to their computational complexity and which will enhance their capabilities especially compared to current, often analytical, approaches. After selecting a narrower research direction, implementation and testing of algorithms are planned both in simulated and real-world environments using robotic systems available at the ÚAMT workplace.

   Tutor: Žalud Luděk, prof. Ing., Ph.D.

6. Electric motor as a sensor

   In safety critical electric motor control applications like the fail-safe or fail operational ones, the redundancy is often required to be used. Some sensors which are not needed for the normal operation are added to diagnose the correct functionality of the overall drive. The utilization of multiphase motors or special inverter topologies are needed. In multiphase motor control applications, there seems to be an option to use the motor or its part as a redundant sensor. The deterioration of operational parameters is in this case acceptable. Only limited functionality for limited amount of time is needed. The project solution requires to become familiar with different motor types and to select suitable motor type with regard to its usability as a sensor. Later on, the design of motor control algorithms using motor feedback in case of fault will follow. The simulation results will be validated on a real motor with the help of rapid prototyping tools or using embedded platform commonly used for electric drive control.

   Tutor: Blaha Petr, doc. Ing., Ph.D.

7. Electrical drives diagnostics using artificial intelligence

   The idea of using artificial intelligence methods for the diagnostics of electric drives is not new. What is changing nowadays is the availability of computing resources that are able to calculate it at different levels and the availability of software tools for optimization. The problem with model-based diagnostics is the requirement for a relatively accurate model. Commonly used mathematical models often do not cover the entire working area well due to nonlinearities in the drive. The second problem is that they mostly aim to detect one type of fault. Algorithm combinations are difficult to solve and often suffer from misdiagnosed faults. The goal is to create algorithms using artificial intelligence (Machine Learning (ML), Artificial Neural Networks (ANN), Expert System (ES), ...) for complex diagnostics of electric drive faults, i.e., for the detection of several types of faults. Another goal is to even locate the fault and determine its severity. At the beginning, it is expected to generate data from simulation experiments in the MATLAB Simulink environment. Later, it will be possible to use available drives with the possibility of emulating various faults to generate real data. For time-consuming calculations (such as the ANN learning process) it will be possible to use the resources available in our laboratory, such as a multi-core server equipped with 3x RTX4090, but also the combination of nVIDIA DGX A100 and H100 systems. The proposed algorithms can be implemented in an embedded device (AURIX multi-core processor with a parallel data processing unit), but also on cloud resources using the aforementioned DGX platforms.

   Tutor: Blaha Petr, doc. Ing., Ph.D.

8. Immersive telepresence for unmanned vehicles

   The problematics of unmanned aerial vehicles (UAVs) and ground vehicles (UGVs) is currently a very important topic in various areas (military, emergency services, etc.). Although today’s leading research directions are mainly aimed at strengthening their autonomy, mediating perceptions from/to a remote environment (telepresence) will still be necessary, even in the case of a fully autonomous vehicle. The aim of the research is to increase the immersivity (the degree of similarity of the virtual perception with the real one) in remote sensing, which is currently still very limited. Cybernetic modelling and simulation of the human operator will be studied, including advanced dynamic and biomechanical models, on the basis of which appropriate telepresence hardware will be designed. The proposed solution will then be implemented in real UAVs and UGVs, which are available at the ÚAMT workplace and will be compared with the current best methods. Multidisciplinary cooperation with experts from non-technical areas (e.g. biomechanics, anatomy, physiology, etc.) is assumed.

   Tutor: Chromý Adam, Ing., Ph.D.

9. Methods for the measurement of mechanical shock

   The topic is aimed on measurement and generation of mechanical shocks - calibration of shock sensors and calibration of artificial sources of mechanical shocks. The aim of the thesis is to analyze the parasitic influences that affect the overall measurement uncertainties and to find new methods for their suppression. The SPEKTRA CS18 calibration system and the AVEX SM110 MP shock machine will be available for research.

   Tutor: Beneš Petr, doc. Ing., Ph.D.

10. Methods for vibrodiagnostics of non-rotating machines

    The topic is aimed to the research of the methods and algorithms for vibration diagnostic of non-rotating machines, especially for diagnostic method of bearings and gearboxes working under non-stationary conditions. Research will be focused on limiting factors in identification of actual methods for machines with a variable speed. Possibilities of modification or combination of these methods will be studied for achieving mechanical particles state estimation with better accuracy. In addition to the theoretical work, practical implementation and verification of these methods will be accomplished on mock-up.

    Tutor: Beneš Petr, doc. Ing., Ph.D.

11. Modelling of special safety function with respect on industrial Ethernet

    The topic is aimed on research of new special safety functions special safety functions models for machinery and the process safety. The objectives of the thesis consist of the a thorough analysis of the current safety function models, research a thorough analysis of the current models available safety functions, examining the impact of communication, particularly an industrial Ethernet. The student will be designed new models on the base on the analysis and will develop new algorithms for verification of the relevant safety logic functions and security elements for machinery and process safety. The topic will be solved in relation to national and international projects running in cooperation with industrial partners.

    Tutor: Štohl Radek, Ing., Ph.D.

12. Objective measurement and assessment in medicine through advanced cybernetic applications

    According to Evidence Based Medicine, the objective evidence is absolutely necessary for right diagnosis and proper selection of therapy. However, suitable methods providing a sufficiently objective index relevant to the symptoms are lacking, for example, in dermatology, diabetology, physiotherapy or oncology. The goal of this project is to find novel objective diagnostic methods using unconventional view of medical problems from the perspective of cybernetics. Research work will be devoted to the development of missing methods for identifying the status of living systems, which will mainly use objective quantification of symptoms (swelling, inflammation, atrophy, blocking, etc.), mostly by accurate multispectral 3D scanning of selected parameters (eg. 3D temperature distribution, accurate 3D volumetric measurement or topological alignment). The research will continue on the results of the H2020 ASTONISH project, which has already achieved the first positive results in this area. In identifying living systems and their failures, the research work will also follow the latest advanced methods of technical cybernetics, including the use of artificial intelligence. The result will be new accurate objective quantification methods that will bring more effective therapy, shorter recovery time, lower costs and higher quality of health care, not only in the above-mentioned medical fields.

    Tutor: Žalud Luděk, prof. Ing., Ph.D.

13. Predictive diagnostics

    Predictive diagnostics is a promising research area with rapid development of new data-driven methods. Unfortunately, there are still many areas where this approach fails and a classical knowledge-based approach has to be used. The aim of this research is to combine the methods of both approaches and try to apply them in areas that have been neglected so far, e.g. construction or testing.

    Tutor: Beneš Petr, doc. Ing., Ph.D.

14. Recursive nonlinear identification and filtering using approximate Bayesian inference

    The dissertation focuses on researching and developing algorithms for recursive estimation of nonlinear models, which transform the approximation of analytically unsolvable problems into an optimization task within the context of Bayesian inference. Emphasis will be placed on acquiring statistical knowledge about the parameters of the system model and its states considering the sequential data. acquisition. In this context, it will be necessary to find a solution that eliminates or at least satisfactorily reduces the accumulation of approximation errors to prevent significant degradation in the estimation process quality. The problematic accumulation arises when lossless estimation is replaced by a one-step approximation, motivated by the update of the available approximated posterior distribution conjugated to the treated parametric model. Addressing this issue may involve smoothing techniques, which retroactively operate with finite-dimensional statistics over an expanding observation horizon and do not require the often problematic application of the marginalization operator. Further, the smoothing technique should be suitably complemented by decision-making processes to determine the final parameter values of the posterior distribution. The evaluation of proposed solutions is anticipated in the field of electric drives for tasks involving state control and parameter-based fault detection and localization.

    Tutor: Dokoupil Jakub, Ing., Ph.D.

15. Synchronous signal processing for anomaly detection

    The topic is focused on research in the field of fault and anomaly detection methods with a focus on so-called partial discharges, including in the environment of rotating machines. Partial discharges are localized discharges where there is no full breakdown of the insulation and manifest as pulses that last less than 1 us. Under realistic operating conditions, traditional detection methods fail due to noise. Therefore, methods using synchronous multichannel measurement of quantities that allow to suppress the influence of noise will be the main object of research. The research in the environment of 3-phase systems and systems will be based on the 3PARD, 3PTRD and 3CFRD methods, which require synchronous sampling of the measured quantities. The research will focus on methods that are suitable for implementation in FPGAs, including methods to provide sufficiently accurate synchronization (better than 1 us) that is necessary to capture and localize the disturbance. The topic is prepared and will be implemented in cooperation with Modemtec company (expert guarantor: ing. Roman Mego, Ph.D.)

    Tutor: Fiedler Petr, doc. Ing., Ph.D.

16. Virtual Sensors for Sensorless Drive Control and Virtual Redundancy

    Modern control algorithms used for electric drives require information about a range of variables measured on the drive. A large number of sensors then means complications both in terms of mechanical configuration and the cost of the drive. The topic is focused on the research and development of virtual sensor algorithms that allow for sufficiently accurate estimation of selected quantities (especially mechanical) based on the measurement of other quantities on the drive. Although this approach is known, for example, in the context of so-called sensorless control of speed or position of the motor, in terms of real applications, existing algorithms still do not achieve sufficient accuracy, especially in the area of very low or conversely high speeds. The expected research will focus precisely on such situations. At the same time, the possibility of virtual sensor redundancy in connection with their diagnostics will be studied. Attention will be paid to the use of new sensors of electrical and magnetic quantities with a high dynamic range, which can provide more information for calculating the estimate of other quantities on the drive. The topic will be addressed in connection with the Horizon Europe Cynergy4MIE project.

    Tutor: Václavek Pavel, prof. Ing., Ph.D.

17. Visual Telepresence in Mobile Robotics

    Research in the field of modern progressive approaches to immersive visual telepresence for use in reconnaissance and service mobile robotics. The research will focus on achieving the best possible visual perception and will include the following sub-areas - stereovision, low latency of the sensing, transmission and imaging chain, high resolution, wireless transmission, head position sensing and head movement prediction and more. After selecting a suitable research direction, a custom solution with verification of parameters will be designed and technically implemented. Experimental deployment on one of the robotic devices available at Brno University of Technology is expected.

    Tutor: Žalud Luděk, prof. Ing., Ph.D.