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This article reviews the possibilities of using visitor monitoring in the management of a protected area in the example of the Moravian Karst Protected Landscape Area (pla). The Moravian Karst is the largest and most significant karst area in the Czech Republic. Its location near a large city and its easy accessibility mean that some of the pla’s parts are faced with the negative impacts of overly intensive tourism on rare natural sites and protected species. While the presence of visitors in a protected area is desirable, it is necessary to regulate their activities in the area. The pla management needs quality information for its decision making, such as on the current characteristics and behaviour of visitors. To obtain this information, primary marketing research was carried out at selected pla sites from May to September 2018. A questionnaire survey was used to obtain data from 2,100 visitors and to define the Moravian Karst visitor profile. We found that most visitors were attracted to caves open to the public and the Macocha gorge and that almost a quarter of respondents were planning to return to the pla within six months of questioning. A major challenge for the sustainable development of tourism in the area will be the fact that visitors to the caves are not interested in visiting other localities in the pla. The data obtained about visitors will be used by the pla management to formulate measures to redirect visitors from the most-visited locations to the less-visited ones. Keywords: sustainable tourism, marketing research, visitor profile, destination management, protected landscape area, Moravian Karst

The concept of walking in the sense of a specific way of life (the Hebrew verb hālaḵ hitp.) which pleases God forms the established tradition in the texts of the Old Testament and of the New Testament. This tradition influences (in the Psalms and outside the Psalms) the translation of the LXX. The translators into Greek have viewed the translation "walk" as insufficient in certain places. This is the reason why they interpreted walking with God (or before God) as gaining God’s favor (eὐαρeστέω as the equivalent of hālaḵ hitp.).

The article focuses on the design, construction and manufacture of an inspection vehicle intended to access difficult-to-reach places. The vehicle is able to monitor piping system failures at the view angle of 180° in deep depths and adverse environments. The individual components of the inspection vehicle, more detailed their programming and production on CNC machines are discussed. The vehicle is supposed to easily run into the piping systems and can be safely pulled out. A control system was created for motion and video signal transmission to the operator from above the ground. Aluminum alloy (EN AW 2024) is the predominant material of the manufactured components and at the same time has ideal processing and operating properties. Proposed inspection vehicle is a robust and functional solution with minimal maintenance.

The paper deals with the design and implementation of a system for monitoring the level of liquid in a remote reservoir without power supply from electric network. The hardware of the solution is based on Arduino Mega platform with GSM / GPRS sending data and level measurement using hydrostatic pressure gauge. An internet server is used to collect data in a database and to present data to users in a form of web pages. The work focuses on high precision, low power consumption and low cost of the final system. The paper describes the developed hardware and software. The paper also present testing of the system in real conditions.

The paper presents process of modelling of the CE108 Coupled Drives Apparatus developed by TecQuipment Ltd. This laboratory plant serves as a laboratory equipment representing a problem of modelling and control of producing continuous lengths materials, which is common in many industrial applications. A mathematical model based on the first principle modelling, which was created by designers of the plant, is used as a starting point. The characteristics of this initial model is compared with real-time measurements and the model is improved. Simulink representation of the model is designed and verified. The Simulink model is suitable for designing and verifying various control strategies not only for the CE108 Coupled Drives Apparatus but in general for similar industrial processes.

In the paper model of cogeneration plant with heat storage was developed for investigating the potential of implementing the heat storage. The model is used within the operation optimization to maximize the revenue from power sales. Receding horizon of 7 days is used for operational planning resulting in linear optimization problem. The method is used to evaluate the benefit of different sized storages using the real-life heat demand data for Malenovice district in Czech Republic and day-ahead electricity prices for a one-year horizon. The results show that the revenue from power sales can be significantly improved by shifting operation to the most commercially feasible points in time.

The images of the divine Sun in ancient cultures are the context of Old Testament expressions concerning the sun. The translator of the Hebrew text into Greek must have also been influenced by the culture that surrounded him. The translator provided his Greek-speaking Jewish contemporaries with a monotheist tuned paraphrase of the Hebrew text in Psalm 18:5c. His idea of the sun as an appropriate allegorical representation of God is based on linguistic and thematic default assumptions of the Hebrew text.

This paper deals with modeling and control of a hydraulic three-tank system. A process of creating a computer model in the MATLAB/Simulink environment is described, and optimal PID (proportional-integral-derivative) and model predictive controllers are proposed. The modeling starts with the creation of an initial mathematical model based on a first-principles approach. Then, the initial model is enhanced to obtain better correspondence with a real-time system, and parameters of the modified system are identified from measurements. The real-time system contains nonlinearities which cannot be neglected, and therefore are identified and included in the final mathematical model. The resulting model is used for a control design. As the real-time system has long time constants, usage of the Simulink model dramatically speeds up the design process. Optimal PID and model predictive controllers (MPC) are proposed and compared. Model predictive controllers for both a linearized model and the nonlinear system are proposed. The techniques described are not limited to one particular modeling problem, but can be used as an illustrative example for the modeling of many technological processes.

The article presents application of evolutionary algorithm for cost-optimal dispatch of ancillary services in power transmission system of the Czech Republic. The problem is formulated as a nonlinear optimization problem taking into account both continuous and discrete control variables. The optimal setting of control variables such as schedule for activation of regulation reserves, which minimizes the regulation energy costs while maintaining required control performance, are determined via evolution algorithm. The proposed control scheme is tested on historical data from the transmission system operator.

In this work the optimization of the local model network structure and predictive control that utilize the local model network to predict the future response of a plant is studied. The main idea is based on development of the local linear models for the whole operating range of the controlled process. The local models are identified from measured data using clustering and local least squares method. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. The manipulated variable adjustments are computed through optimization at each sampling interval. The parameters of the plant at each sampling point are derived from the linearization of local model network. The proposed identification and control method is illustrated by the simulation study on the nonlinear process.

The paper deals with modeling and adaptive control of the Twin Rotor MIMO System - a real-time laboratory plant by Feedback Ltd. The plant consists of two propellers, which can be controlled by external controller, and resembles a helicopter. An initial mathematical model is derived using first principles modeling and further improved to correspond more with behavior of the real-time plant. The mathematical model is transferred to Simulink environment and is used for design of an adaptive controller. Several self-tuning controller were tested and closed loop behavior of the nonlinear Simulink model and of the real-time plant is compared.

This paper presents new control laboratory course designed at the Faculty of Applied Informatics for the undergraduate courses in Automatic control. Experience obtained during the building process of Control Laboratory and the organization of student assignments for laboratory course is described. The objectives of the laboratory course are to integrate the fundamental skills from theoretical courses to solve engineering problems in dynamics and control and to encourage the problem-based learning.

This contribution presents a usage of Self-Tuning Controllers Simulink Library (STCSL) for control of the nonlinear systems using decentralized approach. The STCSL was created for design, simulation verification and especially real-time implementation of single-input/single-output (SISO) digital self-tuning controllers (STC). The proposed self-tuning controllers what are included in the library can be divided into three groups. The first group covers self-tuning PID controllers, the second group of described controllers is based on the polynomial approach and the third group contains the controllers derived on the other approaches (minimum variance control etc.). This paper is focused on decentralized control of multi-input/multi-output (MIMO) nonlinear systems in real time applications. The aim is to investigate simple discrete STC with respect to their usage in real-time decentralized control. A supervisory logic block is used in the control scheme to increase the stability of the recursive identification process by ensuring that just one of the controllers is in adaptive mode at a particular time. The logical supervisor is purposed as a simple and reliable technique for designing the supervisory system. The basic idea of the logical supervisor is based on assigning priorities to individual outputs of controlled multivariable system and on-line evaluation of criterions for each subsystem. The usage of this approach has been tested in real-time conditions for control of two-input/two-output (TITO) laboratory models. The STCLS is available free of charge at Internet site -http://www.utb.cz/stctool/.

The combination of the automatic control theory courses and practical implementation of the designed controller algorithms in real-time conditions is very important for training of the control engineers. This contribution presents a MATLAB-Toolbox for design, simulation verification and especially real-time implementation of single input - single output (SISO) discrete self-tuning controllers. The proposed adaptive controllers what are included into a Toolbox can be divided into three groups. The first group covers PID adaptive algorithms using traditional Ziegler-Nichols method for the setting of the controller parameters, the second group of described controllers is based on the pole placement design and the third group contains the controllers derived on the other approaches (dead-beat, minimum variance etc.). The controllers are implemented as an encapsulated SIMULINK blocks and thus allows users simple integration into existing SIMULINK schemas. The process of developing real-time applications using MATLAB Real-Time Workshop and several control courses is also presented. The MATLAB-Toolbox is very successfully used in Adaptive Control Course in education practice for design and verification of self-tuning control systems in real-time. It is suitable for design and verification of the industrial controllers, too. The MATLAB-Toolbox is available free of charge at Internet site - http://www.utb.cz/stctool/ .

This study deals with high-density polyethylene (HDPE) which was subjected the drop-weight test. HDPE is a semicrystalline thermoplastic polymer which is commonly used in many applications and mainly in the automotive industry because of its properties. The injection moulded HDPE samples were subjected the penetration test at different potential energies and the results were subsequently evaluated and discussed. The samples were tested in the range of potential energies from 30 to 230 J. The first sample penetration occurred at the energy 50 J. It was found out that the potential energy 30 J is too small for penetration of this material, which shows the impact resistance of HDPE. © The Authors, published by EDP Sciences, 2017.

The contribution presents a MATLAB-Toolbox for design, simulation and verification of single input single output (SISO) discrete self-tuning controllers. The proposed adaptive controllers who are included into a Toolbox can be divided into two parts. The first part covers PID adaptive algorithms using traditional Ziegler-Nichols method for the setting of the controller parameters, the second part of described controllers is based on the pole placement design. The MATLAB-Toolbox is very successfully used in Adaptive Control Course in education practice for design, simulation and verification of self - tuning control systems in real - time conditions. It is suitable for design and verification of the industrial controllers, too.

In this paper, multiple piecewise linearizations of a nonlinear process in different operating points are used within a Kalman filter bank which computes the conditional probabilities of various hypotheses that are modeled by the filters. State estimates provided by the Kalman Filters and local model parameters are weighted using conditional probabilities and then used within the predictive control framework. The proposed strategy is tested on the complex model of styrene polymerization process.

In the paper the fuzzy Kalman filter (KF) is proposed to allow for adaptation to changing properties of the controlled process. The fuzzy KF is used to estimate both states and unmeasured disturbances of the nonlinear process. Further, a Model Predictive Control (MPC) based on the fuzzy representation of the nonlinear process is formulated. The performance of the proposed estimation fuzzy scheme and predictive controller is evaluated through computer simulations of the pH neutralization process. The pH neutralization process is widely recognized as a difficult control problem due to the strong nonlinearity of the process.

This paper deals with state observers with respect to their usage in the model predictive control (MPC) based on state space model of the controlled system. In case of immeasurable states a state observer (filter) is used to calculate current states in each control step. The paper is especially focused to finite impulse filters (FIR) as these filters do not require knowledge of initial state - contrary to infinite impulse response (IIR) filters. Several linear filters are tested and compared with proposed filters based on quadratic and linear programming. Different filter lengths (horizons) were tested to investigate filters’ performance. Filters were tested in very noisy conditions to evaluate filter robustness and therefore its usability in real-time deployment. The simulations were carried out using data from a real-time laboratory (Amira DR300 Servo system). All the measurements and simulations were carried out in MATLAB/Simulink environment.

The paper presents an initial part of a modelling the Coupled Drives Apparatus CE108 developed by TecQuipment Ltd. The final model should be used in the process of control design for the apparatus. The model derived by the developers of the apparatus using first-principle modelling is used as a starting point for comparison of its properties with measured characteristics. The static and dynamic characteristics of the real-time apparatus are presented and discussed. Special attention is paid to principal differences between the model and the real-time apparatus. Real-time measurements are obtained using MATLAB / Simulink environment in connection with a Real-time Toolbox. Evaluation of the experiments is also performed in the MATLAB environment

The contribution is focused on a design of a control algorithm for a nonlinear servo system with disturbance rejection using adaptive control strategy. It is obvious that for a rejection of the measurable disturbance is a suitable control strategy based on polynomial approach. The regression (output error) models are used in the identification part, the parameter estimates of the process and disturbance models were computed using the least squares method extended by a directional (adaptive) forgetting. The controller synthesis is based on polynomial theory - pole assignment method. The designed controller was applied to a laboratory servo system Amira DR300 in real-time conditions.

Modern electric power systems are large-scale systems with a complex structure comprised of interconnected networks. The balance between the generation and consumption of electricity has to be maintained at any moment. Transmission System Operator (TSO) uses ancillary services for keeping the domestic power balance. Selection of the services can be viewed as an optimization problem. In this contribution the optimization problem is solved via the Self-Organizing Migration Algorithm which belongs to the class of evolutionary algorithms., 1M0567, Ministry of Education of the Czech Republic

Heat exchange belongs to the class of basic thermal processes which occur in a range of industrial technologies, particularly in the energetic and chemical industry. The process of heat exchange is often implemented by through-flow heat exchangers. It is apparent that for an exact theoretical description of dynamics of heat exchange processes it is necessary to use partial differential equations. Heat exchange is namely a process with distributed parameters. It is also necessary to take into account its nonlinear and stochastic character. In spite of these facts, most of thermal equipment is controlled by digital modifications of PID controllers at present. This paper deals with identification of a static behaviour of a through-flow heat exchanger and a design of an adaptive predictive controller for its control. The proposed adaptive predictive controller was verified by a real-time control of Multifunction Process Control Teaching System (MPCTS) - the Armfield PCT 40.

The paper presents application of evolutionary algorithm for cost-optimal dispatch of ancillary services in power Czech transmission system. The problem is formulated as a nonlinear optimization problem taking into account both continuous and discrete control variables. The optimal setting of control variables such as schedule for activation of regulation reserves, which minimizes the regulation energy costs while maintaining required control performance are determined via evolution algorithm. The proposed control scheme is tested on historical data from the transmission system operator.

The combination of the automatic control theory courses, simulation verification and practical implementation of the designed controller algorithms in real-time conditions is very important for training of the control engineers. This contribution present structure and usage of Self-tuning Controllers Simulink Library (STCSL) for real time control. The STCSL was created for design, simulation verification and especially realtime implementation of single input – single output (SISO) digital self-tuning controllers. The proposed adaptive controllers, which are included in the library, can be divided into three groups (PID controllers, controllers based on the polynomial approach and the controllers derived on the other approaches (minimum variance etc.). This Library is very successfully used in Adaptive Control Course in education practice for design and verification of self-tuning control systems in simulation and real-time conditions. It is suitable also for design and verification of industrial digital controllers. The highly nonlinear laboratory model, the DR300 Speed Control with Variable Load, has been chosen as example for real-time control. The STCSL is available free of charge at the Tomas Bata University internet site http://www.utb.cz/stctool/. INTRODUCTION One approach to adaptive control is based on recursive estimation of unknown system characteristics, gradually specifying them, and then monitoring possible changes. Using this knowledge, appropriate methods can be employed to design the optimal controller. This kind of controller, which identifies unknown processes and then synthesizes control (adaptive control with recursive identification), is referred to in the literature as a selftuning controller (STC). Self-tuning controllers use the combination of the recursive process identification based on a selected model process and a controller synthesis based on knowledge of parameter estimates of controlled process. In some STCs, the identification process does not serve to determine the estimates of the process model parameters ( ) ˆ k Θ ; rather, appropriate reparametrisation of the control loop can be used recursively to estimate the controller parameters directly. Therefore, it is necessary to find the relationship between the process input and output and define it straight from the controller parameters without recalculating them using the estimates of the process model parameters. These controllers are referred to as being implicit, whereas controllers using a synthesis from estimates of the process model parameters are called explicit (with direct identification). Presently, most of the explicit STCs are based on the Certainty Equivalence (CE) Principle. The basic principle of CE is that the model uncertainty is not considered. The parameter estimates of the process model, which are obtained by recursive identification, are used for the controller design. It is assumed that values of these estimates correspond to their actual values. The aim of this contribution is not only to inform potential users about Self-tuning Controllers Simulink Library STCSL (see Bobal and Chalupa, 2002) and to provide help for usage of the library in the simulation verification (see Bobal et al. 2008) but also to demonstrate the practical approach to a design of a control of the laboratory model in real-time conditions. RECURSIVE IDENTIFICATION The regression (ARX) model of the following form ( ) ( ) ( ) ( 1 T y k k k n k = − + Θ Φ ) (1) is used in the identification part of the designed controller algorithms, where ( ) 1 2 1 2, , ,..., , , ..., T na nb k a a a b b b ⎡ ⎤ = ⎣ ⎦ Θ (2) is the vector of the parameters and ( ) ( ) ( ) ( ( ) ( ) ( ) 1 1 , 2 , , 1 , 2 , , T k y k y k y k n u k u k u k nb − = − − − − − − ⎡⎣ − − − ⎤⎦ Φ ... ... ) , a (3) Proceedings 23rd European Conference on Modelling and Simulation ©ECMS Javier Otamendi, Andrzej Bargiela, Jose Luis Montes, Luis Miguel Doncel Pedrera (Editors) ISBN: 978-0-9553018-8-9 / ISBN: 978-0-9553018-9-6 (CD) is the regression vector (y(k) is the process output variable, u(k) is the controller output variable). The non-measurable random component n(k) is assumed to have zero mean value E[n(k)] = 0 and constant covariance (dispersion) R = E[n(k)]. The recursive least squares method for calculating of parameter estimates ( ) ˆ k Θ is utilized. Using the pure least squares method, the influence of all pairs of identified system inputs and outputs to the parameters estimates is the same. This property can be inconvenient for example when identifying a system with timevarying parameters. In this case, it is better to use least squares method with exponential forgetting where the influence of latter data to the calculation of the parameter estimates is greater then the influence of older data. The exponential forgetting method can be further improved by adaptive directional forgetting (Kulhavý, 1987) which changes forgetting coefficient with respect to changes of input and output signal. CONTROLLER ALGORITHMS The proposed self-tuning controllers which are included in the library are divided into three groups: • classical digital PI and PID controllers whose tuning is based on Ziegler-Nichols method, pole assignment method and its modifications, • controllers based on polynomial approach (deadbeat strong and weak version, pole assignment, controllers based on minimization of quadratic criterion), • controllers based on other approaches (minimum variance controllers, Dahlin’s controller, BanyaszKeviczky’s controller etc.). SELF-TUNING CONTROLLERS SIMULINK LIBRARY The Simulink is nowadays a word-wide standard in simulation, testing, and verification of behaviour of various dynamic systems. Simulink is a part of MATLAB system and supports linear or nonlinear systems modelled in continuous time, sampled time or a hybrid of the two. Systems can also be multirate, i.e. have different parts that are sampled or updated at different rates. Based on monograph Bobal, et al. (2005) a library of self-tuning controllers in MATLAB/Simulink environment was created. The purpose was to create a framework suitable for creating and testing of selftuning controllers. The library is available free of charge at internet site of Tomas Bata University in Zlin – www.utb.cz/stctool/ (see Bobal and Chalupa, 2002). The library was created using MATLAB version 6.5 (Release 13) and is compatible with all newer MATLAB versions. Controllers are implemented in the library as standalone Simulink blocks, which allows an easy incorporation into existing simulation schemes and an easy creation of new simulation circuits. Only standard techniques of Simulink environment were used when creating the controller blocks and thus just basic knowledge of this environment is required to start working with the library. Controllers can be implemented into simulation schemes just by the copy or drag & drop operation and their parameters are set using dialog windows. Another advantage of the used approach is a relatively easy implementation of userdefined controllers by modifying some suitable controller from the library. Nowadays the library contains over 30 simple single input single output discrete self-tuning controllers, which use discrete ARX models of second and third order for the on-line system identification. DESCRIPTION OF LABORATORY MODEL AMIRA DR300 Some of self-tuning algorithms were tested using a realtime laboratory model DR300 (Speed Control with Variable Load) by the Amira Company, Duisburg, Germany (see Fig. 1). Figure 1: Laboratory Model Amira DR300 A simplified scheme of the DR300 system is shown in Fig. 2.

The paper deals with the problem of modelling and control using the Local Model Network (LMN). The idea is based on development of multiple local models for the whole operating range of the controlled process. The local models are then smoothly connected using the validity or weighting functions to provide a nonlinear global model of the plant. For saving the computational load, linear model is obtained by interpolating the parameters of local models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behaviour of the process. The supervisory program, based on a nonlinear global model, computes desired values of manipulated variables leading to minimum utility consumption. The approach is verified in a control of model of a heat exchanger. INTRODUCTION Many of the processes in the chemical industry exhibit nonlinear behaviour. Their nonlinearities arise from the dynamics in chemical reactions, thermodynamic relationships, etc. Such processes are difficult to model and control. For such problems, there is a strong intuitive appeal in building systems which operate over a wide range of operating conditions by decomposing them into a number of simpler linear modelling or control problems. Multiple-model approaches for the modelling and control of nonlinear systems were proposed in the last decade (Murray-Smith and Johansen, 1997). Here local models are identified over the operating range of the process and form a global nonlinear model process by incorporating the validity function for each of these models (Johansen and Foss 1995). The basic principles of this modelling approach have been more or less independently developed in different disciplines like neural networks, fuzzy logic, statistics and artificial intelligence with different names such as local model networks (Johansen and Foss 1993), Takagi–Sugeno fuzzy model (Takagi and Sugeno 1985) or neuro-fuzzy model ( Narendra and Parthasarathy 1990; Zhang and Morris 2002). Similarities and differences between the LMN, radial basis function network (RBFN), fuzzy and Gaussian process model can be found in (Gregorcic and Lightbody 2008). Model Based Predictive Control (MBPC) or only Predictive Control is one of the control methods which have developed considerably over a few past years. The main advantage of this methodology is that it enables a simple treatment of input and output constraints (Maciejowski 2002), and copes in a natural way with multivariable systems. There are two ways to design controllers for local model structures, the linearization based and the local model-based approach. For linearization-based approach the local model network is linearized at the current operating point and linear controller is designed. The linearization of the LM network is very simple due to the structure of the model. In the second realisation a local controller is designed for each local model and the control output is then calculated as an interpolation of the local controller according to the current operating point. Both realisations have been widely used in the literature for control of nonlinear systems (Johansen et al., 2000;Mollov et all. 1999). In this paper the operating regions are distributed in the operating space using the steady-state characteristic of the process. The data for identification of the local models were created by application perturbations around the chosen operating points. The local models were identified using the least-squared method. The parameters of the validity functions were then optimized by minimizing the output error. Thus a nonlinear global model of the process was obtained. Predictive control strategy that uses parameters obtained from linearization of the global model was then applied to provide set point tracking of the output of the plant. The paper is organized as follows: Section II presents an overview of the local model network approach. The basics of Model Predictive Control are outlined in Section III. In Section IV, the heat-exchanger process is described. Section V describes the modelling of a heatexchanger using the LMN approach. In Sections VI a VII results of predictive control with and without the utility costs taken into consideration are presented. Proceedings 23rd European Conference on Modelling and Simulation ©ECMS Javier Otamendi, Andrzej Bargiela, Jose Luis Montes, Luis Miguel Doncel Pedrera (Editors) ISBN: 978-0-9553018-8-9 / ISBN: 978-0-9553018-9-6 (CD) LOCAL MODEL NETWORKS The basics of Local Model Networks approach are to decompose the operating space into zones where linear models are adequate approximations of dynamic behaviour within that regime, with a trade-off between the number of regimes and the accuracy of the global model. The architecture of LMN benefits from being able to incorporate the a priori knowledge about the system and conventional system identification methodology. The LMN structure also gives transparent and simple representation of the nonlinear system. In the LMN representation, each model is a local approximation of the modelling surface over a subspace of the operating space, which can be seen as an operating regime (Figure 1). Figure 1: The nonlinear input/output approximation (c) is obtained by combining three linear models (a) with validity functions (b) If linear local models are assumed, the structure of the ith submodel can be defined as follows

The majority of processes met in the industrial practice have stochastic characteristics and eventually they embody nonlinear behaviour. Traditional controllers with fixed parameters are often unsuitable for such processes because their parameters change. The changes of process parameters are caused by changes in the manufacturing process, in the nature of the input materials, fuel, machinery use (wear) etc. Fixed controllers cannot deal with this. One possible alternative for improving the quality of control for such processes is the use of adaptive control systems. Different approaches were proposed and utilized. One successful approach is represented by self-tuning controller (STC). This approach is also called system with indirect adaptation (with direct identification). The main idea of an STC is based on the combination of a recursive identification procedure and a selected controller synthesis. In this paper, the standard STC (non-predictive) approach is verified and compared with STC based on the Model Predictive Control (MPC). The verification of both methods was implemented by the real-time control of a highly nonlinear laboratory model, the DR300 Speed Control with Variable Load.

This contribution present structure and usage of Selftuning Controllers Simulink Library (STCSL). The STCSL was created for design, simulation verification and especially real-time implementation of single input - single output (SISO) digital self-tuning controllers. The proposed adaptive controllers which are included in library can be divided into three groups. The first group covers PID adaptive algorithms using traditional Ziegler-Nichols method for setting controller parameters, the second group of described controllers is based on the polynomial approach and the third group contains the controllers derived on the other approaches (minimum variance etc.). The controllers are implemented as an encapsulated Simulink blocks and thus allows users simple integration into existing Simulink schemas. The process of developing real-time applications using MATLAB Real-Time Workshop and several control courses is also presented. This Library is very successfully used in Adaptive Control Course in education practice for design and verification of selftuning control systems in simulation and real-time conditions. It is suitable also for design and verification of the industrial digital controllers. The STCSL is available free of charge at Internet site http://www.utb.cz/stctool/.