François E. Cellier

Modeling of Multibody Systems with the Object-Oriented Modeling Language Dymola

The object-oriented modeling language Dymola allows the physical modeling of large interconnected systems based on model components fromdifferent engineering domains. It generatessymbolic code for different target simulators. In this paper, a Dymola class library for the efficient generation of the equations of motion for multibody systems is presented. The library is based on aO(n) algorithm which is reformulated in an objectoriented way. This feature can also be interpreted as a bond graph oriented modeling of multibody systems. Furthermore a new algorithm for a certain class ofvariable structure multibody systems, such as systems with Coulomb friction, is presented, which allows the generation of efficient symbolic code.

Automated formula manipulation supports object-oriented continuous-system modeling

Digital continuous-system simulation languages are discussed. It is demonstrated how sophisticated automated formula manipulation can be used to automatically generate state-space models from an object-oriented description of a physical system. The two major complications, algebraic loops and structural singularities, occur frequently as a consequence of couplings between submodels (objects), and these difficulties can often be dealt with by automated formula manipulation. A software tool, Dymola, in which the various formula manipulation techniques have been implemented, is presented. Dymola is an object-oriented continuous-system modeling language and a model manipulator that can be used to generate models in several simulation languages.<<ETX>>

Artificial Neural Networks and Genetic Algorithms

In Chapters 12 and 13, we have looked at mechanisms that might lead to an emulation of human reasoning capabilities. We approached this problem from a macroscopic point of view. In this chapter, we shall approach the same problem from a microscopic point of view, i.e., we shall try to emulate learning mechanisms as they are believed to take place at the level of neurons of the human brain and evolutionary adaptation mechanisms as they are hypothesized to have shaped our genetic code.

Computer Aided Control Systems Design

This paper reflects our experience with Computer Aided Control System Design (CACSD). A first section briefly presents a CACSD-system for student use (INTOPS) which has been developed by our group some years ago. Our experience with this software system is outlined. A second section discusses a few state-of-the-art CACSD-systems developed by other research groups. In particular, the systems developed by K.J. Astrom and his group are mentioned (SIMNON, IDPAC, MODPAC, POLPAC, and SYNPAC), as they seem to be among the most advanced systems currently available. A next chapter discusses the advents of a more general expression parser at hand of the MATLAB matrix manipulation program. It is shown that, though MATLAB was not really Designed for the implementation of control algorithms, many control problems can be formulated and solved very elegantly by means of MATLAB. A new CACSD-system (IMPACT), which is currently under development by our group, is then presented. This system shall enhance the capabilities of MATLAB by encompassing additional operations and data structures particularly useful in control system analysis and design. A final chapter concludes this discussion by mentioning some perspectives for further development.

Combined qualitative/quantitative simulation models of continuous-time processes using fuzzy inductive reasoning techniques

Abstract A new mixed quantitative and qualitative simulation methodology based on fuzzy inductive reasoning is presented. The feasibility of this methodology is demonstrated by means of a simple hydraulic control system. The mechanical and electrical parts of the control system are modeled using differential equations, whereas the hydraulic part is modeled using fuzzy inductive reasoning. The technique is described in detail in the first part of this paper. The example is shown in the second part of the paper. The mixed quantitative and qualitative model is simulated in ACSL, and the simulation results are compared with those obtained from a fully quantitative model. The example was chosen as a simple to describe, yet numerically demanding process whose sole purpose is to prove the concept. Several practical applications of this mixed modeling technique are mentioned in the paper. but their realization has not yet been completed

Mixed quantitative:qualitative modeling and simulation of the cardiovascular system

The cardiovascular system is composed of the hemodynamical system and the central nervous system (CNS) control. Whereas the structure and functioning of the hemodynamical system are well known and a number of quantitative models have already been developed that capture the behavior of the hemodynamical system fairly accurately, the CNS control is, at present, still not completely understood and no good deductive models exist that are able to describe the CNS control from physical and physiological principles. The use of qualitative methodologies may offer an interesting alternative to quantitative modeling approaches for inductively capturing the behavior of the CNS control. In this paper, a qualitative model of the CNS control of the cardiovascular system is developed by means of the fuzzy inductive reasoning (FIR) methodology. FIR is a fairly new modeling technique that is based on the general system problem solving (GSPS) methodology developed by G.J. Klir (Architecture of Systems Problem Solving, Plenum Press, New York, 1985). Previous investigations have demonstrated the applicability of this approach to modeling and simulating systems, the structure of which is partially or totally unknown. In this paper, five separate controller models for different control actuations are described that have been identified independently using the FIR methodology. Then the loop between the hemodynamical system, modeled by means of differential equations, and the CNS control, modeled in terms of five FIR models, is closed, in order to study the behavior of the cardiovascular system as a whole. The model described in this paper has been validated for a single patient only.

SAPS-II: A new implementation of the systems approach problem solver

Abstract In this paper, we describe a reimplementation of the Systems Approach Problem Solver that was originally designed and implemented by H. J. J. Uyttenhove.1,2 In this reimplementation, emphasis was put on a clean and flexible user interface that allows the user to conveniently combine several of the SAPS basic operations for solving more complex problems. SAPS-II3 was implemented as a toolbox (function library) within the framework of the CTRL-C program,4 an interactive matrix manipulation language developed originally for computer-aided control system design, and enhanced later for other purposes such as signal analysis and statistical operations.5,6 From the point of view of the CTRL-C software, the SAPS-II library can be viewed as yet another enhancement for the purpose of systems analysis and synthesis. From the point of view of the SAPS-II software, CTRL-C can be viewed as a software shell for convenient embedding of the SAPS algorithms.

Linearly Implicit Quantization-Based Integration Methods for Stiff Ordinary Differential Equations

Abstract In this paper, new integration methods for stiff ordinary differential equations (ODEs) are developed. Following the idea of quantization-based integration (QBI), i.e., replacing the time discretization by state quantization, the proposed algorithms generalize the idea of linearly implicit algorithms. Also, the implementation of the new algorithms in a DEVS simulation tool is discussed. The efficiency of these new methods is verified by comparing their performance in the simulation of two benchmark problems with that of other numerical stiff ODE solvers. In particular, the advantages of these new algorithms for the simulation of electronic circuits are demonstrated.

Design of a simulation environment for laboratory management by robot organizations

This paper describes the basic concepts needed for a simulation environment capable of supporting the design of robot organizations for managing chemical, or similar, laboratories on the planned U.S. Space Station. The environment should facilitate a thorough study of the problems to be encountered in assigning the responsibility of managing a nonlife-critical, but mission valuable, process to an organized group of robots. In the first phase of the work, we seek to employ the simulation environment to develop robot cognitive systems and strategies for effective multi-robot management of chemical experiments. Later phases will explore human-robot interaction and development of robot autonomy.

Hierarchical non-linear bond graphs: a unified methodology for modeling complex physical systems

Bonds graphs have been around for a quarter of a century. While originally intended for modeling mechanical systems, they have meanwhile found widespread applications in many areas of physical system modeling. Bond graphs are a very appealing tool for modeling physical systems, because they represent the flow of power through a system. Since energy and mass are the only tradable goods in our physical universe, a bond graph model is more likely to reflect physical reality than a model derived by use of any other modeling methodology. However, bond graphs, like all graphical techniques, become unwieldy when applied to complex sys tems. Also, bond graphs were traditionally used to model predominantly linear systems. This paper introduces new concepts for modeling complex physical systems through hierarchical bond graphs which can include arbitrary non-linearities. It introduces a software tool that can be used to implement these hierarchical non-linear band graphs. Finally, a new application area for bond graphs will be discussed. It will be demonstrated how these hierarchical non-linear bond graphs can be used to model chemical reaction kinetics and chemical thermodynamics together in very general terms also farther away from equilibrium than traditional approaches would permit.