doc. Ing. Lukáš Ferkl, Ph.D.

This article presents modelling and control of a climatic chamber with on/off actuators. Controlled environmental variables in the chamber are temperature, relative humidity and illumination. The system is first decoupled by nonlinear transformation and a linear model is identified using subspace methods; further, a model predictive control strategy for processes with on/off actuators is presented. The controller incorporates set-point filtering to deliver smooth climate transitions. The control results from a real climatic chamber are attached.

The objective of this paper is to introduce a new method based on the framework of Deterministic Stochastic Petri Nets (DSPN) for a complex stochastic modelling of repairable systems and for their complete analysis including criticality and importance of components. The method proposed here is divided into two major stages - a modelling and an analysis phase. The aim of the first stage (the modelling phase) of the approach is to create a stochastic model of a behavior of a repairable system (from the point of view of the dependability theory) using the concept of DSPN. The output model is incorporating not only a state automaton of the system but also all mechanisms, which are modelling causes of transitions between system states, Common Cause Failures and dependencies between events. The DSPN model obtained from the first stage is used as an input for the second stage of the presented method (analysis phase). The analysis stage is able to present the same qualitative and quantitative measures and parameters of the system as standard Markov diagram analysis or fault/success tree analysis (e.g. steady-state probabilities of system/component states, system/component availability, minimal cut/path). So one DSPN model is consisting of information which can be normally obtained through two or three different analyses. First added information, which is not directly evident from classical methods, is a basic semi-qualitative analysis, which shows us all possible minimal combinations of failures and repairs with their importance. These parameters, based on well-known Petri nets properties, are important for improving a reliability performance of the analysed repairable system.

As the buildings account for about 40% of global final energy use, the efficient building climate control can significantly contribute to the saving effort. Predictive control can be used to operate buildings in energy and cost effective manner instead of conventional room automation such as PID, weather-compensated controllers or Rule-Based Controllers (RBC). However, the predictive controller has (besides many advantages as the possibility to incorporate the restriction directly into the controller design or handling of MIMO systems in a simple natural way) a drawback - it is the necessity of a proper mathematical model of the controlled system. Therefore, adequate attention should be paid to the procedure leading to its acquirement. In this paper a multi-step ahead error minimization approach to a building modeling is presented and influence of the solar radiation on the quality of the constructed model is examined. Moreover, the results are demonstrated on a real control of six-floor building of the Czech Technical University in Prague.

Most of the industrial applications are multiple-input multiple-output (MIMO) systems that can be identified using the knowledge of the system's physics or from measured data employing statistical methods. Currently, there is the only class of statistical identification methods capable of handling the issue of the vast MIMO systems - subspace identification methods. These methods, however, as all the statistical methods, need data of a certain quality, i.e., excitation of the corresponding order, no data corruption, etc. Nevertheless, combination of the statistical methods and a physical knowledge of the system could significantly improve system identification. This paper presents a new algorithm which provides remedy to the insufficient data quality of a certain kind through incorporation of the prior information, namely a known static gain and an input-output feed-through. The presented algorithm naturally extends classical subspace identification algorithms, that is, it adds extra equations into the computation of the system matrices. The performance of the algorithm is shown on a case study and compared to the current methods, where the model is used for an MPC control of a large building heating system.

Low energy buildings have been attracting much attention lately. Most of the research is focused on the building construction or alternative energy sources. Recently, there has been an intense research in the area of Model Predictive Control (MPC) for buildings. The main principle of such a controller is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, the thermal comfort is represented by a static range for the operative temperature according to the international standards. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index, however, is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, compare the control performance both to the conventional and predictive control strategies, point out that the proposed optimal control problem formulation shifts the savings potential of classical MPC by additional 11% and finally, the quality of the fulfillment of the thermal comfort will be addressed.

Recently, there has been an intensive research in the area of Model Predictive Control (MPC) for buildings. The key principle of MPC is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, according to international standards, the thermal comfort is represented by a static range for the operative temperature. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index is, however, a nonlinear function of various quantities, which limits the applicability and scalability of the control problem formulation. At first, PMV-based formulation is stated, the main differences between typical MPC problem formulation and PMV based formulation are outlined, a computationally tractable approximation of the nonlinear optimal control problem is presented and its accuracy is validated. Finally, control performance is compared both to a conventional and predictive control strategies and it turns out that the proposed optimal control problem formulation shifts the savings potential of typical MPC by additional 10-15% while keeping the comfort within levels defined by standards.

Even though modern control has emerged in numerous control applications, a building automation is still a field where the position of the classical control is almost exclusive. The main reason is that for the synthesis of a predictive controller a decent model for control is needed. In the field of building climate control, it is still problem to obtain a model of large building in an explicit form suitable for control. Most of the approaches either use building modeling software to get detailed model, which is unfortunately in implicit form; or the model is built-up as a first principle model, which usually ends-up as an extreme simplification of the reality. In this paper, a building model identification procedure is presented, wherein the building model is built-up as a first-principle model using a simulation software (detailed, precise, however in implicit form), and then a state-space model is identified by means of subspace identification methods. The main focus of the paper lays on a case study of a large office building, and the entire process of its identification.

This paper discusses an application of Petri Nets for qualitative and quantitative reliability analysis of discrete event systems. Various types of methods have been used in reliability engineering, which deal with various phenomena in this field. One of such approaches includes modeling the system states by a Markov diagram and the associated probabilities by Fault Trees, Success Trees, etc. In our approach, we translate the widely used reliability analysis methods into the language of Petri nets, which enables us to describe the system reliability and maintainability in one consistent concept.

In order to provide decision-making mechanism for application of model-based control of heating systems in buildings, a method has been proposed that assumes the energy balance calculation as the performance bound for the modelbased controller. If the performance bound is significantly lower than the actual energy consumption, a more accurate estimate is made by means of dynamic model identification and modelbased controller simulation. Said method has been tested as a case study on a health care center located in Prague, Czech Republic.

This paper presents model predictive controller (MPC) applied to the temperature control of real building. Conventional control strategies of a building heating system cannot make use of the energy supplied to a building. Moreover dropout of outside temperature can lead to underheating of a building. Presented predictive controller uses both weather forecast and thermal model of a building to inside temperature control. By this, it can utilize thermal capacity of a building and minimize energy consumption. It can also maintain inside temperature at desired level independent of outside weather conditions. The models of multiple input multiple output systems (MIMO) can be identified by means of subspace methods. Oftentimes, the measured data used for identification are not satisfactory and need special treatment. During the 2009/10 heating season, the controller was tested on a large university building and achieved savings of 17-24% compared to the present controller.

Predictive control in buildings has undergone an intensive research in the past years. Model identification plays a central role in a predictive control approach. This paper presents a comprehensive study of modeling of a large multizone office building. Many of the common methods used for modeling of the buildings, such as a detailed modeling of the physical properties, RC modeling, etc., appeared to be unfeasible because of the complexity of the problem. Moreover, most of the research papers dealing with this topic presents identification (and control) of either a single-zone building, or a single building sub-system. On contrary, we proposed a novel approach combining a detailed modeling by a buildingdesign software with a black-box subspace identification. The uniqueness of the presented approach is not only in the size of the problem, but also in the way of getting the model and interconnecting several computational and simulation tools.

Many approaches to risk analysis in tunnels have been proposed by both international and national authorities over the last few years. Many safety problems have been discussed and a large number of important risk factors and hazards in tunnels have been identified. The concept of risk analysis in the scope of tunnel risks is, however, still under development; particularly an overall idea about the risk management concept is still missing. The paper introduces the concept of risk analysis in the scope of risk management and employs methods well-known in aeronautics and aircraft industry, yet, still unused in tunnels. The proposed methodology enables building and refurbishing costs minimization subject to preservation of satisfactory safety level. The outcomes of the proposed method have clear technical and economic interpretation and create a strong support tool for the decision making process. The paper also includes a case study of the Strahov tunnel in Prague, Czech Republic.

The building of the Faculty of Mechanical Engineering, Czech Technical University in Prague - Dejvice, has been heated by a ceiling radiant heating system (''Crittall'' system) since 1961. One building block has been equipped by means which enable the use of the pipes of the radiant heating system for ceiling radiant cooling. As optimization of this system is desired, the first step to be performed is the identification. Because of a complicated physical description of the entire system, and a set of measured data which is large enough to perform statistical identification, a decision was made to identify the system by ARMAX model identification and subspace identification methods. The resulting identified model shows a standard deviation of 0.2-0.3 °C on the verification data. The model identified by the subspace methods, enhanced by a Kalman filter, shows a standard deviation of 0.063 °C for out

The implicit model predictive control based on models identified by subspace identification methods was implemented and tested on a large university building. The control was improved by incorporating the weather prediction into the model. The performance of said controller was estimated in an experiment, wherein two almost identical building blocks were compared - one controlled by the model predictive control, and the other one by the existing weather-compensated heating controller. The model predictive control achieved energy consumption lower by approximately 10 %. Based on the positive results, an implementation was developed, which is suitable for commercial applications.

Finding a suitable dynamic, linear, time-invariant model is a major obstacle for the use of model-predictive control of buildings. While finding models based on physical properties of the system is time consuming, statistical models need the system to be excited, which is not always possible. This paper presents possibilities for finding a suitable model based on subspace identification methods for unexcited data and data containing a specially designed identification experiment, and presents practical experiences gained while finding suitable models for a large building. Finally, some other possibilities of finding a model by incorporating prior information to the identification process are discussed, and the performance of the models is evaluated with respect to their use as a part of an MPC controller.

The Strahov Road Tunnel is the oldest tunnel of the city circle in Prague. The main goal of the documentation elaborating process was to find the insufficiency in the field of safety issue (special target was an emergency ventilation system) and to recommend appropriate safety increasing mitigations for the tunnel manager. Beside the incident statistics analysis the documentation includes the comparison of the SRT solution with technical norms and standards, in which the conditions of the tunnel construction, equipment and operation are also checked. Because of the construction specifics in tunnel emergency ventilation system was the fire scenario analysis worked out. On the basis of this analysis the list of mitigation was elaborated. The Probabilistic Risk Assessment (PRA) was applicated to assess the risk of combinations of the mitigations with regard to the constructional and operational costs, so the best combination of the mitigations could be recommended for implementation.

Most of the industrial applications are MIMO systems, that can be be identified using knowledge of the system's physics or from measured data employing statistical methods. Currently, there is the only class of statistical identification methods capable of handling the issue of vast MIMO systems - subspace identification methods. These methods need data of certain quality. Nevertheless, the combination of statistical methods and physical knowledge of the system could significantly improve system identification. This paper presents a new algorithm which provides remedy to insufficient data quality of certain kind through incorporating of prior information, e.g. known static gain or input-output feedthrough. The presented algorithm naturally extends classical subspace identification algorithms. The performance of the algorithm is shown on a case study and compared to current methods, where the model is used for an MPC control of a large building heating system.

Budova Fakulty strojní ČVUT v Praze - Dejvicích je od roku 1961 vytápěna teplovodním systémem vytápění umístěným ve stropě (systém"Crittall"). V jednom bloku bylo instalováno zařízení umožňující použití rozvodů podlahového vytápění k stropnímu sálavému chlazení. Protože bylo rozhodnuto přistoupit k optimalizaci tohoto systému, byla prvním krokem jeho identifikace. Vzhledem ke komplikovanému fyzikálnímu popisu celého systému a k dostatečné množině naměřených dat bylo rozhodnuto systémidentifikovat statistickými metodami, konkrétněmetodami Subspace identifikace (4SID) implementovanými ve specializovaných knihovnách v prostředí Scilab, jež mají tu výhodu, že kromě samotného modelu navrhují i pozorovatele stavu. Výsledný identifikovaný model vykazuje na ověřovacích datech odchylku do 1 °C, v okolí pracovního bodu do 0,5 °C. Tento model je již vhodný k nasazení ve vícerozměrných regulátorech, jako je např. MPC.

The circulation system of the heating water can be used for distribution of heat throughout a building even without an active heat supply - the water may absorb the heat in the rooms exposed to the solar heat and deliver it to the rooms in shade. In Central Europe, this is particularly useful when the outdoor temperatures are close to the required inside temperatures and solar energy can play a significant part in the building heating system. The model of the circulation system during a full circulation mode can help the heating system operators to predict the possible effect of said system and thus save the energy. The model was identified by means of subspace identification methods and connected to a Kalman filter.

Many approaches to Risk Analysis in tunnels have been proposed by both international and national authorities over the last few years. They address the specific environment of tunnels and try to estimate the risk levels in order to make the tunnels safer. Many topics have been discussed and a large number of important risk factors and hazards in tunnels have been identified. However, the concept of Risk Analyses in the scope of tunnel risks is still under development; particularly an overall idea about the Risk Management concept is still missing. In last two years, the Feramat Cybernetics and the Czech Technical University in Prague have performed a research and comparative study of the Risk Analysis methods used in tunnels along with Risk Analysis and Management methods as used by aircraft industry - particularly National Aeronautics and Space Administration (NASA), Federal Aviation Association (FAA) and United States Department of Defence (DoD).

Budova fakulty strojní v Praze - Dejvicích je od roku 1961 vytápěna podlahovým vytápěním umístěným ve stropě (systém "Crittall"). V jednom bloku bylo instalováno zařízení umožňující použití rozvodů podlahového vytápění k stropnímu sálavému chlazení. Protože bylo rozhodnuto přistoupit k optimalizaci tohoto systému, byla prvním krokem jeho identifikace. Vzhledem ke komplikovanému fyzikálními popisu celého systému a k dostatečné množině naměřených dat bylo rozhodnuto systém identifikovat statistickými metodami, konkrétně metodami Subspace identifikace (4SID) implementovanými ve specializovaných knihovnách v prostředí Scilab, jež mají tu výhodu, že kromě samotného modelu navrhují i pozorovatele stavu.

The Blanka tunnel is a 5.7 km long highway tunnel under construction in Prague, Czech Republic. Because of its complicated topology and very strict environmental restrictions, the synthesis of ventilation control for normal conditions turns out to be fairly challenging. The air flow is restricted to leave the tunnel through traffic portals - it has to be aspirated by ventilation centers and released by exhaust shafts and chimneys. To achieve this goal, control strategy was designed based on the mathematical programming principles. The designed controller, which is inspired by model-based predictive controllers (MPC) used in heavy industry, is energy optimal by definition, adapts to changes in operational conditions and requires significantly less design time than traditional approaches.

A control scheme for highway tunnels is designed based on a static model of the highway tunnel. The controller is designed to keep the exhaust levels inside the tunnel below given limits. The control is then simulated on a dynamical model of a highway tunnel.

A Linear Programming Approach for Ventilation Control in Tunnels

This paper presents a program for simulation of exhaust levels inside highway tunnels. The exhaust dynamics is modelled by discretized mass balance equations with constant diffusion coefficient. The car movement - needed for estimation of exhaust production - is simulated by microscopic car following model. For higher reliability of data, a suitable filter has to be designed to eliminate high measurement noise. The simulation program is then verified by real data from tunnel Mrázovka in Prague, Czech Republic.

Řízené větrání automobilového tunelu podle konkrétních provozních podmínek má držet koncentrace zplodin pod hygienickými limity. Navržený způsob řízení toho dosahuje při minimální spotřebě elektřiny k pohonu proudových ventilátorů v tunelu s podélným větráním. Navržený algoritmus řízení je ověřen simulací vybraného úseku tunelu.

The operation of large highway tunnels has to meet several requirements, e.g. energy consumption optimization, minimal influence on the surrounding environment etc. As the tunnel system is typically very complex, a controller designed according to modern control design methods could be very efficient.

Tunnel ventilation simulation of the City Ring in Prague

A control scheme for highway tunnels is designed based on a static model of the highway tunnel. The controller is designed to keep the exhaust levels inside the tunnel below given limits. The control is the simulated on a dynamical model of a highway tunnel.

A dynamic model of ventilation, traffic and exhaust inside a highway tunnel has been designed and implemented to simulate conditions inside a brand new highway tunnel Mrazovka in Prague, Czech Republic. A simulation has been performed to verify the data collected from the real tunnel. As a result, a statistical analysis of the simulated and real data is possible. Based upon the data analysis, a simulation program performance is judged as well. For the relevant data sets, an identification of possible measurement errors occurring at the sensors inside the tunnel is performed.

A simulation model of highway tunnel is derived for ventilation, traffic and exhaust. This model incorporate Bernoulli equations, microscopic car-following model and mass balance equation for a component with konstant density and constant diffusion coefficient. The model is verified by the data from tunnel Mrázovka. Then a GUI is made for the simulation.

A detailed simulation has been proposed and implemented for a complexroad tunnel to be constructed in Prague, Czech Republic. The work bringsnew approach to functional and spatial decomposition of traffic, ventilation andexhaust tunnel subsystems allowing a simulation of dynamics. The discretizationmethod provides a highly realistic and detailed behavior of the tunnel system. Inaddition, simulation of emergency situations is available. The simulation has beenimplemented in Matlab.

Následující článek pojednává o tvorbě simulačního modelu soustavy vzduchotechnik a okolního prostředí připravovaného tunelu Špejchar - Pelc-Tyrolka. V rámci provedených prací byl navržen komplexní systém simulace vzduchotechniky orientovaný na návrh systému řízení technologie tunelu. Tento návrh také umožní specifikování struktury regulace a optimalizaci provozních parametrů chování vzduchotechniky. Součástí navrhovaného řešení je také systém dynamické simulace dopravy, který také umožňuje studium emisního zatížení okolního prostředí. Simulace byla provedena pomocí produktu Matlab 7.0. Použitá kombinace simulačních a softwarových řešení vytvořila dobrý precedens pro použití v této oblasti a splnila všechny požadavky.

Článek pojednává o tvorbe simulacního modelu soustavy vzduchotechnik připravovaného tunelu Špejchar - Pelc-Tyrolka. Simulace byla provedena pomocí produktu MATLAB 7.0. Výsledkemprovedených prací je systém simulace vzduchotechniky umožnující optimalizaci provozníchparametru chování vzduchotechniky s ohledem na rízení technologických cástí tunelu. Výsledek kombinace použitých simulacních a softwarových rešení vytvoril dobrý precedens pro použitív této oblasti.