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A substantial part of Distributed Control Systems are SCADA systems that require connection to low level electronics through standard industrial interfaces and protocols. When implementing Distributed Control Systems for physics experiments, it is often necessary to use custom made electronics that do not have the ability to communicate using standard protocols, but instead use custom communication protocols. This paper describes the new Front End Device (FRED) framework, which provides the possibility of connecting custom electronics to standard SCADA systems, thus filling the gap in the implementation of Distributed Control Systems that deploy custom electronics. The FRED framework also serves as a translation layer, which provides translation of raw values acquired from electronics to real physical quantities and vice versa. At the same time, it is easy to use, since there is no need for additional programming when used in the simple mode, and its entire functionality can be configured in several configuration files. In case of the need to perform more complex operations over electronics, it is possible to use the provided API for the implementation of additional program functionalities. Tests of the FRED framework have shown that it is fast and scalable enough for use within the Distributed Control Systems of large physics experiments. Based on experience with the implementation of the FRED framework in real-world systems of physics experiments, it can be stated that it meets all requirements for data processing throughput.

The purpose of this paper is to present the Single inverted pendulum laboratory model with LSM as a cyber-physical system (CPS). The implementation of the model application into the Distributed Control System infrastructure at the DCAI, FEEI, TU is first described. Pre-prepared program modules from the custom Simulink library Inverted Pendula Modeling and Control, developed by the authors of this paper, are used in subsequent modeling and control design. The contribution to the simulation model level of the DCS infrastructure is described in detail - the nonlinear mathematical model obtained from the library is modified to include the velocity loop, and the necessary parameters are obtained via experimental identification. Finally, a control strategy based on a hybrid control structure is designed and swing-up/stabilizing control algorithms are verified on the laboratory model.

This paper presents design of the predictive control algorithms, which are verified using the simulation and laboratory model of the Intelligent Positionig Plate. The results of the predictive control algorithm verification are presented also in this article. The created tool called the IPPtools is based on the designed predictive control algorithms and it is shortly presented. A part of the paper is dedicated to the concept of the diagnosis system, which is designed and implemented into the 5-level Distributed Control System of the Department of the Cybernetics and Artificial Intelligence. Possibility how to modify the predictive control algorithms using diagnosis system is also stated in the last section of this paper.

One of the research activities of the Center of Modern Control Techniques and Industrial Informatics (CMCT&II) is the Center for Nondestructive Diagnostics of Technological Processes (CNDTP) implemented as part of the TECHNICOM project at the Technical University of Kosice in accordance with the project's intention to improve conditions for transferring research results into practice. The focus of the Center's research is on nondestructive, contactless diagnostics of technological processes relying on image recognition systems where images are scanned by means of contact-free characteristics scanning through grayscale, color or thermovision cameras. This equipment is integrated into the control systems of technological processes and interconnected with the mechatronic parts of technological devices or production lines such as servo systems, mobile and manipulator robots. Our project therefore involves a wide range of technical, programming and networking resources which allow the development, experimental verification and adjustment of solutions related to monitoring, diagnostics and control of technological processes.

The aim of this paper is to design of control algorithm for the Magnetic levitation system using an exact input — output feedback linearization method. In this paper is given nonlinear simulation model of the Magnetic levitation based on mathematical model of the Magnetic levitation system. Designed control algorithm together with simulation model of the Magnetic levitation is implemented into control structure with purpose of control on steady state defined by a reference trajectory, which is verified in Matlab/Simulink language.

This article deals with experimental identification and control of laboratory helicopter model CE 150 manufactured by company Humusoft. Structure of the identified system was approximated by linear black-box models. Discrete Input/Output Auto-Regressive Moving Average model with eXternal input (ARMAX) and its state space equivalent were used. Parameters of the models were estimated by regression techniques using System Identification Toolbox for Matlab. Acquired models were validated using simulations, residual analysis and real-time control. Input/output data necessary for identification were obtained by measurements from laboratory model and were processed using Real-Time Toolbox for Matlab. Based on acquired mathematical models input/output and state space controllers were designed (input/output pole placement with integration, state space pole placement with integration and observer). Designed controllers were implemented in Matlab environment using the Real Time Toolbox and their performance was verified by real-time control of the helicopter model.

The paper deals with the design of optimization predictive control algorithms, which are based on the input-output ARX and CARIMA models of the dynamic systems. The paper also deals with the simulation verification of predictive control algorithms on a nonlinear hydraulic system with different settings of various control algorithms parameters, with a system subject to noise, or disturbance. There is mentioned a brief theoretical derivation and the particular formulas for concrete predictive control methods in the paper. The paper includes a flow chart diagram for predictive control methods programming and the control structure, where predictive control algorithms were used in nonlinear hydraulic system control. Nonlinear differential equations, which express hydraulic system’s dynamic are presented, too. The results of generalized predictive control algorithms comparison are expressed by the control action and the hydraulic system’s output in a graphs form.

This paper introduces a solution to the reference t rajectory tracking problem done by a differential wh eeled mobile robot Khepera II. The paper includes a kinematic part and a dynamic part of the mathematical model of mobile robot. In this paper two approaches of the artificial intelligence are used i.e. genetic algorithm approach from evolutionary ctechniques and theory of neural networks. Genetic algorithm is used for pa rameters optimizing PID controller and K parameter s o-called parameter speed of rotation at the tracking reference trajectory in to defined control structure. For the creation forw ard and inverse neural model by the approach of neural networks are used forward neu ral networks of MLP type. The neural models are ver ified using Neural Network Toolbox. The forward neural model of the mobil e robot is implemented into the IMC control structur e together with the inverse neural model, which is used as a nonparamet ric neural controller. The purpose of the designed control structure is tracking the defined trajectory of the mobile robot using ap proaches of the artificial intelligence, which are v erified by the simulations in the language Matlab/Simulink.