Unification of multiple models for complex system development

In the design of automotive product, the constant evolution of customer requirements and international regulations leads to new considerations of the sys-tem design process. The authors propose a modeling approach for complex system design based on the coupling of collaborative models and heterogeneous experts’ (i.e. authoring) models used for product behavior assessment. The approach aims at modeling a system at different systemic and temporal levels in the design pro-cess and allows a flexible navigation with the possibility of changing or adding models in the design space. The purpose behind the use of this approach is to lead to an optimal design solution in the context of innovative design for complex sys-tem

Bond-graph Input-State-Output Port-Hamiltonian formulation of memristive networks for emulation of Josephson junction circuits

A bond graph Input-State-Output Port-Hamiltonian formulation of memristive networks for Josephson junction circuits in state space is presented. The methodology has applications to the modeling of SQUIDs and other non-linear transducers and enables the formulation of input-output models of complex components embedded in non-linear networks

Bond graph modelling of chemoelectrical energy transduction

Energy-based bond graph modelling of biomolecular systems is extended to include chemoelectrical transduction thus enabling integrated thermodynamically-compliant modelling of chemoelectrical systems in general and excitable membranes in particular. Our general approach is illustrated by recreating a well-known model of an excitable membrane. This model is used to investigate the energy consumed during a membrane action potential thus contributing to the current debate on the trade-off between the speed of an action potential event and energy consumption. The influx of Na+ is often taken as a proxy for energy consumption; in contrast, this paper presents an energy based model of action potentials. As the energy based approach avoids the assumptions underlying the proxy approach it can be directly used to compute energy consumption in both healthy and diseased neurons. These results are illustrated by comparing the energy consumption of healthy and degenerative retinal ganglion cells using both simulated and in vitro data

Bond graphs and object-oriented modelling - a comparison

Bond graph modelling was devised by Professor Paynter at the Massachusetts Institute of Technology in 1959 and subsequently developed into a methodology for modelling multidisciplinary systems at a time when nobody was speaking of object-oriented modelling. On the other hand, so-called object-oriented modelling has become increasingly popular during the last few years. By relating the characteristics of both approaches, it is shown that bond graph modelling, although much older, may be viewed as a special form of object-oriented modelling. For that purpose the new object-oriented modelling language Modelica is used as a working language which aims at supporting multiple formalisms. Although it turns out that bond graph models can be described rather easily, it is obvious that Modelica started from generalized networks and was not designed to support bond graphs. The description of bond graph models in Modelica is illustrated by means of a hydraulic drive. Since VHDL-AMS as an important language standardized and supported by IEEE has been extended to support also modelling of non-electrical systems, it is briefly investigated as to whether it can be used for description of bond graphs. It turns out that currently it does not seem to be suitable

Bond graph based frequency domain sensitivity analysis of multidisciplinary systems

Multidisciplinary systems are described most suitably by bond graphs. In order to determine unnormalized frequency domain sensitivities in symbolic form, this paper proposes to construct in a systematic manner a bond graph from another bond graph, which is called the associated incremental bond graph in this paper. Contrary to other approaches reported in the literature the variables at the bonds of the incremental bond graph are not sensitivities but variations (incremental changes) in the power variables from their nominal values due to parameter changes. Thus their product is power. For linear elements their corresponding model in the incremental bond graph also has a linear characteristic. By deriving the system equations in symbolic state space form from the incremental bond graph in the same way as they are derived from the initial bond graph, the sensitivity matrix of the system can be set up in symbolic form. Its entries are transfer functions depending on the nominal parameter values and on the nominal states and the inputs of the original model. The sensitivities can be determined automatically by the bond graph preprocessor CAMP-G and the widely used program MATLAB together with the Symbolic Toolbox for symbolic mathematical calculation. No particular program is needed for the approach proposed. The initial bond graph model may be non-linear and may contain controlled sources and multiport elements. In that case the sensitivity model is linear time variant and must be solved in the time domain. The rationale and the generality of the proposed approach are presented. For illustration purposes a mechatronic example system, a load positioned by a constant-excitation d.c. motor, is presented and sensitivities are determined in symbolic form by means of CAMP-G/MATLAB

Bond graph model-based fault accommodation in power electronic systems

The paper presents a bond graph model-based approach to active fault tolerant control (FTC) that makes use of residuals of analytical redundancy relations (ARRs). The latter ones are computed in order to decide whether a fault has occurred. Given a single fault hypothesis can be adopted, an advantage is that the time for isolating a fault among potential fault candidates that contribute to an ARR by means of parameter estimation may be saved and as long as ARR residuals are within their thresholds no input reconstruction at all is needed. It is shown that ARR residuals can be used for estimation of faults that can be isolated. ARR based input reconstruction is demonstrated by application to a buck-converter driven DC motor as a simple example of a switched power electronic system for which an averaged bond graph model is used. Scilab simulation runs confirm analytical results. If a required input cannot be determined analytically, it can be obtained by numerically solving a differential-algebraic equations (DAE) system