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This paper presents the development of a new Aerodynamic Ball Levitation Laboratory Plant at the Center of Modern Control Techniques and Industrial Informatics (CMCT&amp, II). The entire design process of the plant is described, including the component selection process, the physical construction of the plant, the design of a printed circuit board (PCB) powered by a microcontroller, and the implementation of its firmware. A parametric mathematical model of the laboratory plant is created, whose parameters are then estimated using a nonlinear least-squares method based on acquired experimental data. The Kalman filter and the optimal state-space feedback control are designed based on the obtained mathematical model. The designed controller is then validated using the physical plant.

This paper deals with hybrid modelling and control of cyber-physical systems, which are part of a recently introduced phenomenon Industry 4.0. As a case study we look at a hydraulic system with hybrid dynamics represented by coupled tanks. The dynamical system contains two discrete modes, specifically with and without interaction. The nonlinear hybrid system was modelled using s-functions and then its linear description was created in the discrete PWA representation with the help of HYSDEL modelling framework. Both s-functions and HYSDEL are part of MATLAB/Simulink environment. After validation of the discrete PWA representation in comparison with the nonlinear hybrid system, LQR synthesis with reference trajectory tracking was designed for both discrete modes. Subsequently optimal control designed on the linear hybrid system in the discrete PWA representation was verified on the nonlinear hybrid system.

The papers presents an implementation of the predictive state space control algorithm, called Model Predictive Control (MPC). This control algorithm is verified on the Ball and Plate laboratory model, called B&P_KYB, for the reference trajectory tracking. The control algorithm is first verified using the derived nonlinear simulation model in Matlab/Simulink. Since simulation results are acceptable, an experiment is realized on the real laboratory model. The results of the experiment are demonstrated as the time response of the ball position and the voltage.

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.

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.

The paper provides two approaches for design of generalized predictive control (GPC) algorithm for nonlinear and time-variant dynamic system. In classical approach of GPC strategy is considered method of instantaneous linearization for calculating of linearized model parameters from known analytic description of nonlinear system. The other purpose of this paper is to show an intelligent approach in which a feed-forward neural network (multi layer perceptron-MLP) is used for modeling and predictive control of the non-linear system. The possibility of an on-line estimation of actual parameters from off-line trained neural model of the non-linear system using the gain matrix is considered in the algorithm of GPC. The neural model is linearized by means method of instantaneous linearization in each sample and an estimated parameters from neural NARX model of the non-linear system are used for design of GPC algorithm. The validity of classical and neural GPC strategy is tested by computer simulations in Matlab/Simulink language using architecture of S-functions of the library PredicLib.

ALICE Controls data produced by commercial SCADA system WINCCOA is stored in ORACLE database on the private experiment network. The SCADA system allows for basic access and processing of the historical data. More advanced analysis requires tools like ROOT and needs therefore a separate access method to the archives. The present scenario expects that detector experts create simple WINCCOA scripts, which retrieves and stores data in a form usable for further studies. This relatively simple procedure generates a lot of administrative overhead - users have to request the data, experts needed to run the script, the results have to be exported outside of the experiment network. The new mechanism profits from database replica, which is running on the CERN campus network. Access to this database is not restricted and there is no risk of generating a heavy load affecting the operation of the experiment. The developed tools presented in this paper allow for access to this data. The users can use web-based tools to generate the requests, consisting of the data identifiers and period of time of interest. The administrators maintain full control over the data - an authorization and authentication mechanism helps to assign privileges to selected users and restrict access to certain groups of data. Advanced caching mechanism allows the user to profit from the presence of already processed data sets. This feature significantly reduces the time required for debugging as the retrieval of raw data can last tens of minutes. A highly configurable client allows for information retrieval bypassing the interactive interface. This method is for example used by ALICE Offline to extract operational conditions after a run is completed. Last but not least, the software can be easily adopted to any underlying database structure and is therefore not limited to WINCCOA.