Dep. of Instrumentation and Control Engineering
Ústav přístrojové a řídicí techniky
Dep. of Instrumentation and Control Engineering

Ph.D. study program
MACHINE AND PROCESS CONTROL

Ph.D. program

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Description

Tutor: prof. Ing. Tomáš Vyhlídal, Ph.D.

Machine and Process Control is a doctoral study programme built with regard to the needs of future employers and their demand for highly qualified professionals, particularly in industries focusing on advanced control of machines, industrial systems and processes, energy systems, environmental biotechnology processes, as well as communications, data processing and management in industrial systems employing modern methods of artificial and computational intelligence.

The concept of the studies emphasizes scientific research and independent creative effort, taking into account strong interdisciplinary links in the particular field of modern mechanical engineering. Students are provided knowledge in progressive areas of mathematical modelling, management theory, project management, calculation and optimization procedures and artificial intelligence methods.   

The presented doctoral studies respond to the demand for professionals capable of understanding and developing increasingly complex systems for the monitoring, management and processing of data across industries (mechanical engineering, energetics, biotechnology, manufacturing, space technology). The complexity is increasing owing to extensive integration of supply chains, massively developing automation and robotization of processes, as well as their growing interconnections towards autonomous production systems employing advanced sensors to adaptively manage and optimize individual production processes on a real-time basis. Another important issue requiring response in the form of education is the introduction of numerous new manufacturing technology, such as additive technology or implementation of the Industry 4.0 concept, the mastering of which, along with the capacity for further development based on own research, is a prerequisite for consequent integration within intelligent manufacturing systems, modern informatics and cybernetics.

The doctoral study programme aims at educating professionals having such knowledge and being capable of creating new original outcomes in R&D to turn such into commercial practice

Graduate profile

Machine and Process Control graduates are capable of carrying out research & development to consequently transfer the interdisciplinary insights to the industry as members of research or managing staff in applications of automated control systems, artificial intelligence and industrial informatics. The topics particularly include designs of the control of mechanical, mechatronic, transport, energy or chemical units or indoor environment control systems. Further, programme graduates are prepared to address the challenges the society faces, such as environmental protection, be it in the form of developing new nature-friendly technologies or optimizing the existing ones to minimize their environmental burden. This is also associated with the graduates’ readiness to contribute to the development of driving systems in electric and (semi)autonomous vehicles. 

Doctoral graduates possess profound knowledge to complete the most challenging research tasks as well as experience with the latest experimental and diagnostic methods and world-class pilot and operating equipment. This allows them to tackle complex tasks in development of data management and processing systems on a theoretical level as well as to experimentally verify their conclusions by designing and realizing experimental equipment, conducting an evaluation and producing generalized results. Given the complexity of the latest technology that the education focuses on, emphasis is placed on strengthening the links among the particular areas of the doctoral study programme as well as on fostering interdisciplinary connections on the level of related fields. Relying on this knowledge, graduates are able to comprehensively solve, objectively evaluate and formulate the attained original results of scientific research assignments. Also, they are able to present or realize the same on an international scale, taking into account intellectual property issues. Programme graduates are suitable candidates for jobs in the field of industrial research and development, new technology design and implementation, and management of complex industrial systems. Other opportunities can be found in the academic sector and other institutions focused on science, R&D and innovations on the national and international level.

Admission requirements

  • The fundamental condition for admission is a completed master’s degree in a field related to the thesis topic.
  • Properly completed application form, submitted in due time and manner.
  • Original of a proof of completed university education (i.e. diploma or diploma amendment), or a certified copy of the same, presented to the Science and Research Department of the Faculty of Mechanical Engineering of CTU, Prague.
  • The entrance test is oral, mostly covering three subjects selected in a manner to allow the candidate to demonstrate knowledge of the theoretical basics of the doctoral study programme. The examination also includes oral verification of the candidate’s past professional activities, current level of language skills and orientation in the chosen thesis topic. Admission is based on a consensus of the examination board members as to whether the candidate has the capacity to successfully graduate and complete the thesis. Where multiple candidates apply for one announced topic, the board sets the order of successful applicants and recommends the first-ranking candidate for studies. As part of the admission examination and in accordance with the Study and Examination Rules, the tutor has the right to veto an admission decision in respect of a topic proposed by such tutor.

Framework topics

Department of Automatic Control (12137)

prof. Ing. Tomáš Vyhlídal, Ph.D.

  • Time delay algorithms for structurally optimized multi-dimensional vibration absorbers
  • Risk assessment and control of indoor climate in castles and museums
  • Control design for flexible mechanical systems and robotic structures
  • Controllers and compensators for time delay systems
  • Oscillation and vibration damping by time delay algorithms
  • Application of machine learning methods for control design of complex systems

doc. Ing. Jaromír Fišer, Ph.D.

  • Backstepping control and sliding mode control of nonlinear and delay systems
  • Similarity theory application to high-fidelity modeling of industrial process control and estimation

prof. Ing. Milan Hofreiter, CSc.

  • Predictive control of nonlinear systems
  • Relay identification of technological processes

Department of Electrical Engineering (12114)

doc. Ing. Martin Novák, Ph.D.

  • Advanced control methods of high-speed electrical machines
  • Advanced control of active magnetic bearings for high-speed electrical machines
  • Control of high-speed electrical machines for autonomous vehicles
  • 3D printing of electrical machines

doc. Ing. Jan Chyský, CSc.

  • New methods for modeling and controlling electrical drives
  • Control of food production technology with particular demands on precise pasteurisation temperature

prof. Ing. Jaroslav Novák, CSc.

  • Analysis of structures, dimensioning and control of zero-emission traction systems
  • Methods and means for reducing energy consumption in controlled electric drives

Department of Precision Mechanics and Optics (12136)

prof. Ing. Jan Hošek, Ph.D.

  • Automation of adjustment techniques of optical instruments
  • Machining conditions identification in micro electro-discharge machining
  • Control of foil optical systems

Additional Topics

doc. Ing. Ivo Bukovský, Ph.D. (12131)

  • Physics-informed machine learning in industrial applications
  • Explainable neural networks for multi-input multi-output systems

prof. Ing. Michael Valášek, DrSc. (12131)

  • Digital twin of engineering design
  • Sliding mode control of systems with multiple sliding surfaces
  • Optimal control of energy consumption by robots from battery
  • State derivative Kalman filter
  • Wave-based control of underactuated and distributed systems
  • Knowledge support of engineering design

prof. RNDr. Gejza Dohnal, CSc. (12101)

  • Optimal maintenance models based on Bayesian methods
  • Extreme value mixture models in change detection

prof. Ing. Zbyněk Šika, Ph.D. (12131)

  • Advanced control methods for active and semi-active reduction of machine tools vibrations
  • Advanced control methods for active and semi-active reduction of vehicle suspension vibrations
  • Predictive control of flexible robotic structures

doc. Ing. Petr Kadera, Ph.D. (CIIRC in cooperation with 12137)

  • Machine learning methods for quality control
  • Product and production system co-design
  • Methods for automation of distributed and reconfigurable industrial systems