Modelling and Simulation of Hybrid Systems

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Modelling and Simulation of Hybrid Systems

Krister Edstrom

Dept. of Electrical Engineering

Linkoping University, S-581 83 Linkoping, Sweden email: edstrom@isy.liu.se , phone: +46 13 28 40 29

Jan-Erik Stromberg

Dept. of Signals, Sensors and Systems

Royal Inst. of Technology, S-100 44 Stockholm, Sweden email: janerik@s3.kth.se , phone: +46 8 790 73 26

1 Introduction

Used in this particular context, the term hybrid system refers to a combination of objects which result in a mixture of discrete and continuous behaviours 6, 5, 2]. In order to distinguish between a physical system and its model, we also need the term mode-switching system 9]. A mode-switching system is a physical system which is naturally, but not necessarily, modelled as a hybrid system.

Consider for instance an electrical mode-switching system containing fast switching components such as diodes, thyristors and relays combined with continuous dynamic com- ponents such as inductors, capacitors and electrical motors. In a very detailed model, even the transitions of the switching components would be modelled as continuous dynamic transitions. The resulting model would then become a sti system of continuous equa- tions. In a more practical model, the transitions of the fast switching components would be idealised to instantaneous transitions.

One concrete example of such an electrical system is the power converter in gure 1. A more detailed discussion of this example and its model is found in e.g. 9]. Note that hybrid systems are just as relevant in domains di erent from the electrical. Take for instance mechanical systems with clutches or free-wheeling devices and hydraulic systems with solenoid valves or check-valves.

Due to the combination of discrete and continuous behaviour, hybrid systems are inherently dicult to analyse formally. Numerical simulation therefore plays an important role in the analysis of such systems.

Hybrid systems occur when there are reasons to approximate fast dynamics by instan-

taneous transitions. Such approximations may be attractive since they can potentially

simplify the model itself and lower the computational complexity during simulation. The

word 'potentially' is important here, since the opposite can just as well hold. In case the

model is constructed in the wrong way, or an inappropriate numerical solver is used, the

result may be less favourable than for a more detailed fully continuous model.

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3

l

0

+

; u

1 u

lm

1

2 i

0

4 i

4 i

2

i

3 im

i1

i +

c0 +

; u

2 rm

Figure 1: A traditional direct current power converter an example hybrid system.

Consider an electrical circuit with

n

binary switches. See e.g. gure 1 where

n

= 4.

One 'wrong way' of constructing a model for that system would be to build a separate model for each one of the 2

n

(16 for

n

= 4) combined states of the switches. Then we would also have to specify each one of the transitions between these 2

n

models. Now we may have a hybrid system which properly describes the behaviour of the circuit, but we are certainly not confronting an acceptable modelling task. Rather we would like to construct one model once, no matter the number of switches appearing in the circuit.

2 Ideal mechanisms

When building simulation models for physical systems, we frequently use models of ideal physical mechanisms such as ideal resistance, ideal capacitance, ideal viscous friction and ideal elasticity. We combine them appropriately to form models of real non-ideal compo- nents. Quite naturally we do this without bothering so much about how the individual mechanism models are implemented.

Some of the advantages of adopting this approach, are the following. First, we do not start the modelling process from scratch, but start from a set of pre-de ned higher level modelling elements. Secondly, the structure (topology) of the circuit can be maintained in the model. This feature clearly facilitates the maintenance of the model with respect to changes in the circuit. Thirdly, modelling assumptions made become more clear, since there are explicit references to the mechanisms considered rather than implicit references hidden inside a piece of code or a set of formulae.

In this perspective, it seems natural to extend the classical set of ideal mechanisms by an ideal switch mechanism 10]. An ideal electrical switch would then be characterised by no leakage (0 current) in state 'o ' and no voltage drop in state 'on'. Also, the transitions between the two states would be instantaneous.

However, when approximating thyristors, relays, clutches or hydraulic valves by such ideal switches, it seems impossible to stick to only one such standardised model. In particular, it seems like the model will have to change depending on the environment of the switch 3]. Hence it is no longer possible to combine ideal mechanism models arbitrarily, and hence it is no simple to make local changes in a model.

We will next see how this problem can be better understood and be potentially solved

by means of switched bond graphs.

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3 Switched bond graphs

Switched bond graphs are derived from 'classical' bond graphs as originally presented by Henry M. Paynter in 8]. To explain the bond graph language, we rst show a bond graph model of the power converter see gure 2. There is a small number of elements

F:i Sw:

1

Se:u

F:im

I:lm

R:rm

E:v

1

Sw:

2

Sw:

3

C:c

0 F:i

3

F:i

4

I:l

0 E:v

2

E:v

0

u

1

u

2 Sw:

4

Figure 2: A bond graph model of the power converter.

in the switched bond graph language. As seen in the gure, there is almost a one-to-one correspondence between the elements in the graph and the components in the circuit. The important di erence is that the elements in the bond graph are ideal mechanism models which are generalised to all physical domains.

If we compare the hydraulic domain with the electric, we see that volumetric ow,

q

m

3=

s] and current,

i

A] have similar properties they both describe some sort of ow.

For an electric capacitor the voltage

v

V] is proportional to the integral of the current

i

. Similarly, the pressure

p

Pa] in a hydraulic accumulator is proportional to the integral of the ow

q

. In the bond graph language the generalised

C

-element therefore models an ideal electric capacitance as well as an ideal hydraulic accumulation. In analogy, the

I

-element models ideal inductance, the

R

-element ideal resistance and the

Se

-element ideal voltage sources. The generalisation exempli ed above is easily extended to other domains such as the mechanical and the thermal.

Two elements in the bond graph in gure 2 are not directly recognised from the electrical circuit, namely the two junctions,

E

and

F

. Together with the bonds they describe how the components interact. The

E

(common e ort) junction tells us that the connected elements are a ected by the same e ort, i.e. voltage or pressure. In electrical terms this corresponds to parallel connection. The

F

(common ow) junction indicates that the elements are a ected by the same ow, i.e. current or volumetric ow. This corresponds to series connections in electrical circuits.

It is further noticeable that the product of pairs

q

and

p

as well as pairs

i

and

v

de nes

power in Watts. Therefore the bond graph language introduces a domain-independent

pair of generalised power variables referred to as ow

f

and e ort

e

respectively. A bond,

i.e. the half arrow in the bond graph language, represents potential ow of energy. With

each such bond we associate one e ort (

v

or

p

) and one ow (

i

or

q

) variable. The actual

energy ow through that bond is therefore given by the product

ef

(

vi

or

pq

). The half

arrows point in the direction of positive power ow, i.e. the direction of the power ow

when the product of e ort and ow is

>

0 (

vi>

0 or

pq>

0).

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e

f

A B

e

f

A B

Syntax Semantics

A B

e

f

A B

e

f

Figure 3: The meaning of the causal stroke of bond graphs.

The

Sw

-element models switching components in the circuit. It has two modes, zero

ow and zero e ort, and switches between them when its transition conditions are met.

An ideal diode, for instance, has two modes, namely conducting and blocking. When conducting, the voltage over the diode is zero, and it acts as a zero e ort source. When the diode is blocking, the current through the diode is zero and the diode acts as a zero

ow source. The transition conditions for an ideal diode is

v >

0 to go to the conducting mode and

i<

0 to go to the blocking mode. This is a typical element to be modelled as a

Sw

-element in the bond graph. Other such examples are clutches and valves.

Now we see how a switched bond graph describes a hybrid system. The complete hybrid system is described by one single switched bond graph, and when focusing on a speci c mode, all switches are xed either as zero e ort sources or as zero ow sources.

We have now illustrated the similarity between bond graphs and the topology of electric circuit diagrams, and that bond graphs can easily be used across domains. This similarity relation is quite natural since energy normally ows between components which are physically connected to each other. There are however some additional unique bond graph language features which we will next turn to.

4 Graphical analysis of switched bond graphs

One of the unique features of the graphical bond graph language, is the possibility to superimpose information about causality on the energy oriented graph structure. Causal- ity tells us about the computational order in which the variables in a model should be evaluated. Hence causality plays a crucial role in the derivation of executable simulation code.

The causality of a bond graph is represented by causal strokes added to the energy bonds. These strokes can be seen as arrow heads pointing in the direction of the e ort variable's computational order. The meaning of the causal stroke is best summarised by gure 3 in which

A

and

B

are two arbitrary bond graph elements and

A

and

B

represent the computations associated with the simulation code for the two elements. The full arrows in the rightmost part of the gure point in the direction of the data ow.

Rules for the assignment of causality can now be formulated in terms of graphical

syntax rules. Further, since causality assignment rules can be derived from physical

principles, violation of the rules will immediately reveal and isolate aws in the model or

in the actual system design. For instance, if there is a violation of the rules associated

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with a source element, then we have isolated a potentially severe aw. This particular case corresponds to the parallel connection of e.g. two voltage sources with di erent output values or engaging two clutches with di erent xed speeds.

If the rules of a storage element is violated we have an indication of another type of problem, in this case that large transients may appear in the real system. See for instance the parallel connection of two capacitors initially charged to two di erent voltages.

The switch (

Sw

) di ers from other bond graph elements in a peculiar way, since it will cause the overall causality to change from one time to another. In one mode it assigns the e ort to zero, and has to be viewed as a zero e ort source, and the causality stroke will therefore be put next to the junction. In the other mode it acts as a zero ow source, and the causality stroke has to be put by the switch. This explains why it is so inherently dicult to employ ideal switch models if it is not done in a proper way.

If we consider a speci c mode in the power converter, namely the mode in which



1

,

2

and

3

are conducting and

4

is blocking, we see that there is a causal conict, see gure 4. Looking closer at the system, we see however that this mode will never be reached because of the transition conditions of the diode

1

and the thyristor

2

. Causal propagation and analysis is further described in 7, 9].

F:i

Se

0 :

3

C:c

0 F:i

3

F:i

4

I:l

0 Se:u

F:im

I:lm

R:rm

E:v

2

E:v

0 E:v1

Se

0 :

2 Se

0 :

1

Se

0 :

3

C:c

0 F:i

3

F:i

4

I:l

0 Se:u

F:im

I:lm

R:rm

E:v

2

E:v

0 E:v1

Sf

0 :

2 F:i Se

0 :

1

Sf

0 :

4

Sf

0 :

4 Severecausalconict

Figure 4: Two modes of the power converter, one conict mode and one without conicts.

5 Using switched bond graphs for simulation

When simulating a bond graph model, the rst step is to propagate causality as described above. This is algorithmic, so a good bond graph based simulation tool will do the propagation and visualise the result for the user, allowing him to perform the analysis described above. When this is done, equations describing the system behaviour can be derived. This is also algorithmic and hence performed by the simulation tool, see e.g.

11, 7].

If the model to be simulated is hybrid, we rst have to initialise the system in a speci c

mode. Next we can replace all the switches with sources according to the de nition of

the ideal switch. Then causality propagation and analysis can be made and simulation

code for this mode be derived much in the same way as for classical bond graphs. The

code is simulated until a transition condition is met, the new mode is entered, causality

propagated, analysed and simulated. This is repeated until the end-time for simulation

is reached. Simulation of hybrid systems is also discussed in 1, 4].

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6 Conclusions

A switched bond graph is a compact graphical representation of a mode-switching physical system which we want to model as a hybrid system. The representation contains all information needed for automatic generation of simulation code. Hence switched bond graphs relieves the modeller from the burden of having to specify 2

n

models for a circuit with

n

switches. Like classical bond graphs, switched bond graphs also provides powerful means for graphical analysis and debugging of the model. Switched bond graphs also help understanding the inherent diculties involved with the use of ideal switch models.

References

1] Mats Andersson. Object-Oriented Modeling and Simulation of Hybrid Systems. PhD thesis, Lund Institute of Technology, 1995.

2] Michael Stephen Branicky. Studies in Hybrid Systems: Modeling, Analysis, and Control. PhD thesis, MIT, 1995.

3] F. Cellier, H. Elmqvist, and M. Otter. Bond graph modeling of variable structure systems. In Proc. of the Second Int. Conf. on Bond Graph Modeling and Simulation (ICBGM'95), Las Vegas, 1995.

4] Hilding Elmqvist, Francois E. Cellier, and Martin Otter. Object-oriented modeling of hybrid systems. In Dymola - Dynamic Modeling Language, Selected Publications.

Dynasim, 1994.

5] T. Henzinger et al. Proc. of the dimacs workshop on veri cation and control of hybrid systems, 1995. LNCS to appear.

6] R.L. Grossman, A. Nerode, A.P. Ravn, and H. Rischel. Proc. of the rst international workshop on theory of hybrid systems, 1993. LNCS 736.

7] Dean C. Karnopp, Donald L. Margolis, and Ronald C. Rosenberg. System Dynamics, A Uni ed Approach. Wiley Interscience, 1990.

8] H.M. Paynter. Analysis and design of engineering systems. MIT Press, Cambridge, Mass, 1961.

9] Jan-Erik Stromberg. A Mode Switching Modeling Philosophy. PhD thesis, Linkoping University, 1994.

10] Jan-Erik Stromberg, Jan Top, and Ulf Soderman. Variable causality in bond graphs caused by discrete e ects. In Proc. of the First Int. Conf. on Bond Graph Modeling and Simulation (ICBGM'93), San Diego, 1993.

11] Johannes van Dijk. On the role of bond graph causality in modelling mechatronic

systems. PhD thesis, University of Twente, 1994.

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