This dissertation thesis presents, in both its structure and proposed solution of defined objectives, a comprehensive concept for the design of a methodology for modeling, analysis, simulation and control of walking robots with hybrid dynamics. The first part of the thesis deals with the design and implementation of a methodology which allows to obtain mathematical models of nonlinear robotic systems with hybrid dynamics with the particular focus on bipedal walking robots using modern methods and suitable hybrid system representations. The aim of the proposed modeling methodology is to enable the algorithmic generation of mathematical models for underactuated walking robotic systems with hybrid dynamics based on classical mechanics and hybrid systems theory. Limit cycle analysis and Poincaré maps are subsequently used to verify the accuracy of resulting simulation models of nonlinear robotic systems. The second part of the thesis focuses on the implementation of the results of modeling methodology for robotic systems with hybrid dynamics into the design of a methodology for hybrid walking robot control using classical and intelligent methods in suitable control structures. The designed methodology for the control of hybrid walking robots in selected configurations aims to point out the possibility of algorithmically solving the task of tracking reference trajectories in a feed-forward control structure (FFC) using trajectory planning methods based on partial feedback linearization of underactuated hybrid walking models and the boundary value problem solution. The proposed methodology for modeling and analysis of walking robots, shown in the first part of the thesis, as combined with the proposed control methodology in the second part of the thesis, which focuses on solving the task of trajectory tracking in walking robots, is demonstrated through several case studies based on different configurations of underactuated walking robots, and implemented as a software application called „WalMod“, which allows to verify the accuracy of proposed procedures, specified in the methodologies. The concept of hybrid systems is also used in the thesis to solve one of the research tasks defined in the „ALICE Experiment at the LHC in CERN“, project, which consisted of the design of software modules for the control and monitoring application of the ITS detector electronics. A test facility was built by the research team to enable testing of the proposed applications, developed as part of the ALICE CERN project. The software output of the thesis is implemented in the MATLAB / Simulink / Stateflow environment and in the WinCC OA environment by Siemens. The research objectives of the thesis together with the achieved results are expected to provide a platform for further research in modeling, analysis and control of walking robots with hybrid dynamics in more complex configurations, as well as for teaching subjects dealing with modeling, control and simulation of nonlinear hybrid systems

The presented dissertation thesis deals with the modeling and identification of nonlinear dynamical systems of various dynamics. The thesis presents a theoretical analysis of methods, algorithms, and principles for modeling and identification of nonlinear dynamical systems using classical and intelligent methods. The primary goal of the dissertation thesis is the proposal of a complex methodology for modeling and identification of physical systems of various dynamics using classical methods and methodology based on artificial intelligence methods. The proposed methodology based on classical methods combines analytical and experimental identification methods and is verified within two case studies of modeling and identification of the aerodynamic levitation plant and the helicopter educational model. The proposed methodology of experimental identification using artificial intelligence methods is verified within the third case study on the helicopter educational model. Obtained gray-box models and black-box models of physical systems, which represent digital twins of real physical systems, are used for the design and verification of stabilizing control algorithms using MATLAB / Simulink software tools including application Toolboxes. The thesis illustrates the implementation of models of physical systems forming a research and development platform into the DCS network control system in CMCT&II at the DCAI FEEI TU of Košice. In the thesis, an analysis of the distributed control system of the ALICE experiment at CERN and the modification of DCS software modules developed by the CMCT&II is presented. Software modules were developed by CMCT&II as part of the research project The ALICE experiment on LHC at CERN.

The main aim of this thesis is to design a methodology for modeling, analysis and control of hybrid systems in the context of cyber-physical systems and their subsequent implementation into the distributed control system. The thesis deals with selected methods and algorithms for modeling and control of hybrid systems, which were modified to the conditions of the Center of Modern Control Techniques and Industrial Informatics at KKUI FEEI TU and to the tasks within the experiment at ALICE CERN. These methods and algorithms are then validated both on simulation and laboratory model applications. The thesis contains a comprehensive methodology for modeling of hybrid systems, both for discrete-state systems with defined continuous dynamics in each state and for systems with discrete states without defined continuous dynamics. Subsequently, in the analysis of hybrid systems, analysis in open-loop as well as phase portraits are used. After the analysis of hybrid systems, control algorithms for selected hybrid systems are designed, whether in the form of optimal control with the application of metaheuristic algorithms or explicit model predictive control. The next part of the thesis deals with the design of adaptive supervisory control using RBF neural networks for the under-actuated inverted pendulum system with a linear synchronous motor. The methodology and its subsequent verification are elaborated within the thesis in four case studies and two research tasks of the experiment at ALICE CERN. The software output of the thesis is implemented in MATLAB/Simulink using application toolboxes, in C++ language and in WinCC OA.

The presented dissertation deals with the modeling and implementation of distributed control systems in the context of cyber-physical systems. The dissertation is devoted to selected methods for modeling systems with discrete events, specifically to models of hybrid systems in the form of finite-state automata and Petri nets. The main goal of the dissertation is the proposal of a methodology for the modeling and analysis of distributed control systems in terms of throughput and response of communication interfaces and computing processes. The proposed methodology describes the procedure for performing an analysis of existing hardware and software resources within the framework of a distributed control system. Subsequently, it presents the procedure for creating a model of a distributed system composed of a combination of models of finite-state automata and Petri nets. In the last submodule, the methodology presents the possibility of using the created model to determine the throughput and response of the system, which can be used in the design and actual implementation of the distributed control system. The verification of the proposed methodology is demonstrated in three case studies that use the proposed methodology to create a complex subsystem model of the control system of the ALICE experiment at CERN and the application of mobile robotics within the distributed control system in CMCT&II at the DCAI FEEI TU workplace in Košice. Last but not least, the dissertation deals with the implementation of the software modules forming the software output of this dissertation, which are used within the detector control system of the ALICE experiment at CERN.