Borut Zupančič

Hybrid fuzzy model-based predictive control of temperature in a batch reactor

Processes in industry, such as batch reactors, often demonstrate a hybrid and non-linear nature. Model predictive control (MPC) is one of the approaches that can be successfully employed in such cases. However, due to the complexity of these processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the proposed hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between MPC employing a hybrid linear model and a hybrid fuzzy model was made. We established that the latter approach clearly outperforms the approach where a linear model is used.

Theoretical and experimental FUZZY modelling of building thermal dynamic response

In this paper this main advantages and disadvantages of two di5erent types of modelling: theoretical and experimental are presented and discussed. The theoretical modelling is based on energy balances, which gives the overall model described by di5erential equations. On the basis of developed theoretical model a complex simulator in the MATLAB-Simulink environment was implemented. The second part is devoted to experimental modelling. In this paper a fuzzy model represented by non-linear relations between input and output variables obtained by least-squares optimisation method is investigated. c � 2001 Elsevier Science Ltd. All rights reserved.

Modelling thermal processes in buildings using an object-oriented approach and Modelica

Abstract Most of today’s modelling and simulation concepts originate from the times and methods of analog computers. Usually, it is assumed that the model must be expressed in an explicit state-space form. Consequently, the topology of the system gets lost and any future extension and reuse of the model is tedious and error-prone. In other words, it is the modeller’s task to consider the computational order of the operations during a simulation. In this paper we discuss the re-implementation of a passive-solar- building simulator in an object-oriented environment; it was originally built in the non-object-oriented simulation environment of Matlab–Simulink. The former simulator was designed to resemble a real physical test chamber with regard to the thermal and solar radiation flows. However, due to the lack of object orientation in Matlab–Simulink it was very difficult to apply any configuration modifications and extensions. We start with a brief description of the mathematical modelling which includes thermal dynamics and solar radiation. Then the implementation in Modelica is presented. So, a much superior environment in comparison with Matlab-Simulink was obtained, giving us the possibility of high-level modular and object-oriented modelling. The model is also extremely efficient in multidisciplinary projects in which control-engineering specialists (our group) cooperate with specialists from civil engineering, because civil engineers can more easily understand graphical and textual models in Modelica than schemes in Simulink. We expect that such a model will fulfil and significantly improve several model properties in comparison to the Matlab–Simulink implementation, i.e., a better understanding of the influences of thermal and radiation flows on comfortable living conditions, a model-based control-system design, which will enable the harmonization of active and passive energy resources, important energy savings, and a very suitable environment for education in modelling, simulation and control.

A simulation of heat transfer during billet transport

This paper presents a simulation model for billet cooling during the billets transport from the reheating furnace to the rolling mill. During the transport, the billet is exposed to radiation, convection and conduction. Due to the rectangular shape of the billet, the three-dimensional finite-difference model could be applied to calculate the heat conduction inside the billet. The billets are reheated in a gas-fired walking-beam furnace and are exposed to scaling. The model takes into account the effect of the thin oxide scale. We proved that the scale significantly affects the temperature distribution in the billet and should not be neglected. The model was verified by using a thermal camera.

Computer-aided physical multi-domain modelling: Some experiences from education and industrial applications

Abstract For the purposes of this paper, computer-aided physical modelling means a type of modelling in which a computer-aided approach is used, with the basic aim being to maintain the physical structure of a real system or its topology as much as possible in the model. Bond graphs represent a very efficient and traditional approach. However, new, object-oriented and multi-domain tools based on the Modelica language are more appropriate for industrial staff or for the people who do not have a deep insight into modelling and simulation. In this paper we describe several educational and industrial application projects in the Dymola–Modelica environment: a process-systems library, two mechanical systems (an inverted pendulum and a laboratory helicopter), a model of thermal and radiation flows in buildings and two models of processes in mineral-wool production, i.e., a pendulum system and a recuperator system. We describe some experiences from these projects, but also from a more general use of the Matlab–Simulink and Dymola–Modelica environments over many years. One simple conclusion is that we need to educate with two approaches: a more physical and advanced acausal Modelica-like approach, but also a more traditional causal or block-oriented approach according to the historical CSSL standard. The important advantages and disadvantages of both approaches are described. The Modelica-based approach enables true ‘physical’ modelling with fully reusable components. However, there is a particular danger, i.e., users occasionally forget some basic modelling principles when using sophisticated libraries. The result is a very complex modelling structure that is relatively inefficient for the simulation and sometimes has many numerical problems. It is usually very difficult to detect the real reasons for that.

Feedforward control of a class of hybrid systems using an inverse model

Abstract: In this paper we describe the design of a control algorithm for MISO systems, which can be modelled as hybrid fuzzy models. Hybrid fuzzy models present a convenient approach to modelling nonlinear hybrid systems. We discuss the formulation of a hybrid fuzzy model, its structure and the identification procedure. This is followed by a derivation of the inverse model and its implementation in the control algorithm. The control scheme we are discussing splits the control algorithm in two parts: the feedforward part and the feedback part. In the paper, we deal with the feedforward part of the control algorithm, which is based on an inverse of a hybrid fuzzy model. Next, a batch-reactor process is introduced. The modelling of the batch reactor is tackled and the results of the simulation experiments using the proposed control algorithm are presented. The experiments involved controlling the temperature of a batch reactor using two on/off input valves and a continuous mixing valve. The main advantage of the proposed approach is that the feedforward part of the control algorithm can bring the system close to the desired adjusted feasible trajectory, which avoids the need for a very complex feedback part of the algorithm. Therefore, the control algorithm presents a low computational burden, particularly comparing to the standard model predictive control algorithms. These usually require a considerable computational effort, which often thwarts their implementation on real industrial systems. Nevertheless, we show that using the proposed control approach the hybrid fuzzy model framework presents a convenient option for modelling complex systems for control purposes in practice.

Modelling and simulation of a group of mobile robots

Abstract In this paper the mathematical background of the developed robot soccer simulator is presented. It involves robot and ball dynamic behaviour and focuses mainly on their collisions study. Vital parts of the simulator are explained and modelled in more detail, beginning with the simple model of ball and robot motion and continuing with a more complex approximate collisions models, where the real robot shape is taken into consideration. Some new ideas of collision formulation, realization and real robot shape inclusion are used. The implementation of the simulator is described and advantages for the usage of the realistic simulator are stated.

Model predictive control systems with discrete inputs

The paper proposes a model predictive control algorithm that can be applied to discrete-time hybrid systems with discrete inputs. The algorithm is based on performance driven reachability analysis. The algorithm abstracts the behavior of the hybrid system by building a tree of evolution. Nodes of the tree represent reachable state of process, and the branches connect two nodes if a transition exists between the corresponding states. To each node a cost function value is associated and based on this value, the tree exploration is driven. As soon as the tree exploration is finished, the corresponding input is applied to the system and the procedure is repeated.

SIMCOS - Digital Simulation Language with Hybrid Capabilities

SIMCOS — digitale Simulationsprache mit hybriden Eigenschaften. Diese Veroffentlichung behandelt die Simulationsmoglichkeiten der Progra-miersprache SIMCOS, die zur Simulation von dynamischen Systemen verwendet wird. Die Simulationsprache wurde paralell mit dem umfangreichen interaktiven Programmpaket fur Analyse und Entwurf von Regelsystemen ANA entwickelt Innerhalb dieses Pragrammpakets wird SIMCOS im Simulationsprogrammmodul UNICUS (Universal Computer Simulation) verwendet und dient zur Simulation von zeitkontinuirlichen dynamischen Strukturen. Auserdem wurde auch eine selbststandige Version der Simulationsprache SIMCOS entwickelt, die als erfolgs-reiches Mittel fur die Simulation von linearen, nicht linearen, zeitvarianten und anderen Systemen verwendet wird. Die Syntax der Simulationsprache SIMCOS ist nach dem Muster von CSSL und ACSL gemacht. Das Basiskonzept ist die Ubersetzung von Grundmodulen in Fortranmodule, die mit der SIMCOS Bibliothek gebunden werden. Zusatzlich zur Funktionen anderer Simulationsprachen wurden gesteurte Integratoren eingefuhrt, die die Simulation dem Hybridrechnen angenahert haben. Fur die Bedurfnisse des Diskretreglerentwurfes wurden diskrete Blocke eingefuhrt, die nur in den Abtastpunkten ausgefuhrt werden. Diese Blocke ermoglichen eine einfache Simulation von hybriden Systemen (digitaler Regler, zeitkontonuirlieber Prozess) mit verschiedenen Abtastzeiten.

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.