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Holistic system modeling in mechatronics

Robin Chhabra, M. Reza Emami ⇑Institute for Aerospace Studies, University of Toronto, 4925 Dufferin Street, Toronto, Ontario, Canada M3H 5T6

a r t i c l e i n f o

Article history:Received 10 March 2010Accepted 6 October 2010Available online 18 November 2010

Keywords:Bond graphsConcurrent designHolistic design criteriaMechatronic system modelingMechatronicsMultidisciplinary system design

a b s t r a c t

This paper outlines an alternative modeling scheme for mechatronic systems, as a basis for their concur-rent design. The approach divides a mechatronic system into three generic subsystems, namely general-ized executive, sensory and control, and links them together utilizing a combination of bond graphs andblock diagrams. It considers the underlying principles of a multidisciplinary system, and studies the flowof energy and information throughout its different constituents. The first and second laws of thermody-namics are reformulated for mechatronic systems, and as a result three holistic design criteria, namelyenergy, entropy and agility, are defined. These criteria are formulated using the bond graph representationof a mechatronic system. As a case study, the three criteria are employed separately for concurrent designof a five degree-of-freedom industrial robot manipulator.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Mechatronics is a synergistic approach to the design,development and manufacturing of multidisciplinary engineeringsystems, where the emphasis is on the physical integration andinformation communication amongst various subsystems in aholistic fashion [1]. The synergy must be rooted into how such sys-tems are viewed as a unified physical entity, instead of a collectionof several subsystems. Consequently, the criteria based on whichmechatronic systems are designed and evaluated must refer to uni-versal characteristics, in addition to specific performance indexes.In the traditional design approaches different modeling schemesare employed for the subsystems separately, and a collection ofobjectives are defined to be optimized given the constraints thatare enforced by each individual subsystem. These approaches oftenundermine the interconnection between the subsystems, whichcan play a significant role in the overall performance of a multidis-ciplinary system. The necessity of collaboration and informationcommunication amongst the subsystems of a mechatronic systemhighlights the importance of a holistic approach to its modeling,which is capable of viewing all the subsystems with different phys-ical domains from a unified perspective.

In this paper a generic interpretation of mechatronic systems isintroduced based on the notion of information and energy flowthroughout the system. Accordingly, a combination of bond graphsand block diagrams is used to represent mechatronic systems. Theanalogy between mechatronic and thermodynamic systems is

formalized, and the laws of thermodynamics are reformulatedfor defining some global design criteria that deem the system asa whole.

Several attempts have been launched to unify the dynamicequations of mechatronic systems that consist of strongly-coupledmechanical, electrical, and control components. For instance, lineargraph theory has been utilized for modeling various physicalsystems since the mid 1960s. It was originally employed in model-ing of electrical networks [2], but gradually extended to multi-body systems [3], and eventually mechatronic systems [4]. Thisapproach tries to graphically connect the topological representa-tion of a system to the physical characteristics of engineering com-ponents. The intention is to reduce the complexity of the governingequations in a formal way. Physical variables, namely through (e.g.force or current) and across (e.g. displacement or voltage), on eachedge are defined based on their methods of measurement [5]. Theterminal relations between the variables and the system topologyare used to extract a system of mixed differential and algebraicequations in a matrix form [6].

Alternatively, in the early 60s the concept of energy and energyexchange was realized as a common notion to all components of asystem with different physical disciplines [7]. Later, the concept ofport in electrical circuits was generalized to power port, labeled aspower bond, in an arbitrary physical domain [8]. The resultingmodeling scheme, called bond graphs, is a domain-independentgraphical description of dynamical behaviour of multidisciplinarysystems. Similar to the graph theoretic approach, it emphasizesthat the analogy between different domains is more than the puremathematical equations, and the actual physical concepts are anal-ogous. Therefore, in bond graphs components of a system are rec-ognized by the energy they supply or absorb, store or dissipate, and

0957-4158/$ - see front matter � 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.mechatronics.2010.10.003

⇑ Corresponding author. Tel.: +1 416 946 3357; fax: +1 416 946 7109.E-mail addresses: [email protected] (R. Chhabra), emami@utias.

utoronto.ca (M. Reza Emami).

Mechatronics 21 (2011) 166–175

Contents lists available at ScienceDirect

Mechatronics

journal homepage: www.elsevier .com/ locate /mechatronics


reversibly or irreversibly transform [9]. This technique wasextended to complex mechanical systems in three-dimensionalspace by introducing multibond (vector bond) graphs [10,11], andit has recently been applied for modeling and design of mechatron-ic systems [12–15]. In [16,17], mechatronic systems have beenmodeled by bond graphs, and the multi-objective design problemis solved using genetic algorithms. In this paper bond graphs areutilized to introduce an energy-based method of modeling for ageneric subsystem of any mechatronic system, called generalizedexecutive subsystem. This method is helpful to the synergy ofthe model in two ways: (a) by unifying the definition of systemconstituents in various physical domains, and (b) by consideringthe interconnections between different subsystems in the form ofenergy transmission. As a result, the system can be deemed as awhole in a design process.

The following section gives a quick review of the bond graphsthat are extensively used in the coming sections. In Section 3,mechatronic systems are formally defined, and a suitable modelingscheme for their concurrent design is suggested. In Section 4, thefirst and second laws of thermodynamics are formulated for amechatronic system using bond graph notations. Section 5 definesthree holistic design criteria, namely energy, entropy and agility,based on the laws of thermodynamics for mechatronics. Section6 presents a case study, where the developed criteria are used sep-arately for the concurrent synthesis of a five degree-of-freedom(d.o.f.) industrial robot manipulator, and the three differentdesigns are compared. Some concluding remarks are made inSection 7.

2. Bond graph modeling: an overview

A bond graph model represents the entire dynamics of a systemby considering the power exchange between its different compo-nents or subsystems. This method unifies the system componentsmodeling by categorizing the system variables, such as velocity,force, current and voltage into two major groups called flow and ef-fort. These two generalized variables can be multiplied at each in-stant to identify the amount of power transmitted between thecomponents of the system. The resulting simulation scheme, atthe computational level, uses the dynamic equations of the systemcomponents and initial and boundary conditions to determine thesystem variables according to the principle of conservation ofenergy. The mathematical model deduced through this techniqueis equivalent to the complete dynamic model of the system,including non-linear terms. For example, for the mechanicalsubsystem of the manipulator studied in Section 6, this model isa graphical representation of the recursive Newton–Euler equationsof motion [15].

2.1. Basic elements

Bond graphs are graphical representation of a system as a com-bination of its components (or subsystems) and their relationshipin terms of power transactions. The system constituents are shownby letters, and the transaction of power between each pair isshown by a directed line, indicating the reference direction of thepower flow. (A half-arrow is used for the direction of power flowto distinguish it from a signal flow in a block diagram representa-tion.) The directed lines are called (power) bonds. Each power bondconnects two system constituents through their gates, called(power) ports. Each component or subsystem can have multipleports. Fig. 1 illustrates a basic bond graph. The amount of power(P) transmitted at each instant (t) can be determined by the prod-uct of two power variables (or power conjugates), namely flow (f)and effort (e) [15]. Flow and effort can be scalars or vectors,

depending on the system constituents. In case of vectors, the bondis shown by a double-line (half) arrow, and the power is the scalarproduct of flow and effort vectors:

PðtÞ ¼~eðtÞ �~f ðtÞ: ð1Þ

Table 1 lists the analogies used for effort and flow in variousphysical domains [9]. Although power conjugates can be differentphysical entities in different domains, their product always repre-sents the power as a scalar quantity. In a bond graph system mod-el, physical components are mapped onto conceptual elementsthat can capture their full dynamic behaviour [8]. These elementsare divided into three main categories: (a) single-port, (b) dou-ble-port and (c) multi-port elements.

2.1.1. Single-port elementsSingle-port elements consist of three generic types: source (sink)

elements (S), storage elements (I, C), and dissipative elements (R)[12]. The S element is an ideal source (sink) of either flow (Sf) or ef-fort (Se). This element contains a power port, and the bond is eithercoming out of the element (for a source) or going toward it (for asink). The flow or effort that a source (sink) provides (drains) canvary in time. In this case, the source (sink) is identified by MSf orMSe, standing for modulated source (sink). A modulated source(sink) has an additional port, called signal port, to identify theamount of flow or effort at each time.

Storage elements accumulate energy and release it back into thesystem. Depending on whether ~p �

R~eðtÞdt (generalized momen-

tum) or~q �R~f ðtÞdt (generalized displacement) is conserved, storage

elements are divided into two categories: C and I. In type C, e.g., acapacitor or spring, the generalized displacement is conserved,whereas in type I, e.g., an inductor or mass, the generalized momen-tum is conserved. Both types of storage elements transform the en-ergy reversibly. The irreversible transformation of energy that ispumped to the environment, as heat, noise, etc., is often modeledas loss or waste of energy, shown by a dissipative (R) element.Table 1 presents the element analogies in various energy domains.The corresponding constitutive equations of I, C, and R elements,respectively, are:

~eIðtÞ ¼ bI d~f IðtÞdt

; ð2aÞ

~f CðtÞ ¼ bC d~eCðtÞdt

; ð2bÞ

~eRðtÞ ¼ bR~f R; ð2cÞ

where bI and bC are the resistance of the I and C elements against thechange of flow and effort, respectively, and bR is the resistance of theR element against the flow transmission.

Vector Power Bond

BA

BA

Power Ports

(a)

(b)

e

f

e

f

Power Bond

Causal Strokes

Fig. 1. (a) Power bond and, (b) vector power bond connecting power ports ofelements A and B.

R. Chhabra, M. Reza Emami / Mechatronics 21 (2011) 166–175 167


2.1.2. Double-port elementsIt can be shown that independent of energy domains only two

types of ideal double-port elements can exist [8]. The first type istransformer (TF), which relates effort at one port to effort at theother port by a (matrix) parameter called transformer ratio (N),

~e1ðtÞ ¼ N~e2ðtÞ !energy conservation~f 2ðtÞ ¼ N~f 1ðtÞ: ð3Þ

If N is a function of time, the element should have a signal portin addition to the power ports. This kind of transformer is calledmodulated transformer and represented by MTF. Transformers areutilized either in the same energy domain, e.g., gearboxes and pul-leys, or between different domains, such as electromotors andwinches.

The second type is gyrator (GY), which relates the flow at oneport to the effort at the other port by a (matrix) parameter calledgyrator ratio (H),

~e1ðtÞ ¼ H~f 2ðtÞ !energy conservation~e2ðtÞ ¼ H~f 1ðtÞ: ð4Þ

A gyrator is called modulated gyrator (MGY) when H is variablein time. Gyrators are mostly transducers representing domaintransformation such as DC-motors, pumps and turbines.

2.1.3. Multi-port elementsIn order to disseminate power between the subsystems distrib-

uting elements are required. These elements are denoted as junc-tions [9]. For a physical system, interchanging ports must haveno influence on the constitutive equations of junctions. The portsymmetry and power continuity properties result in two kinds ofjunctions, called 0- (zero) and 1- (one) junctions [15]. In 0-junctionthe amount of effort remains constant in all ports. Thus, the powercontinuity equation turns into:

~e1ðtÞ ¼~e2ðtÞ ¼ . . . ¼~enðtÞ;~f 1ðtÞ þ~f 2ðtÞ þ . . .þ~f nðtÞ ¼ 0;

ð5Þ

where n is the number of ports. An example of 0-junction is parallelconnection in electrical circuits.

On the other hand, 1-junction maintains flow the same at allports. Series connection in electrical circuits is an example of1-junction. Considering power continuity in an 1-junction, theconstraint equation can be derived as:

~f 1ðtÞ ¼~f 2ðtÞ ¼ . . . ¼~f nðtÞ;~e1ðtÞ þ~e2ðtÞ þ . . .þ~enðtÞ ¼ 0:

ð6Þ

2.2. Causality

In the bond graph representation, for each element, either ofpower conjugates can be considered as incoming or outgoing sig-

nals. However, at the computational level one of the power vari-ables must be chosen as the input and the other one as theoutput of a port. The output signal is computed based on the con-stitutive equations. The decision of assigning causality is denotedby the causal stroke in the bond graph notation. Causal stroke is aperpendicular line at one end of the power bond that indicatesthe direction of the effort signal as whether it comes toward or goesfrom the element. The resultant graph is called causal bond graphsthat can be interpreted as block diagrams with bi-directional signalflows between different pairs of its elements. However, causalityassignment to each element cannot be completely arbitrary.Depending on the element’s constitutive equation, the elementports can impose constraints on the connected bonds. There arefour different constraints that should be treated in the causalityanalysis of the bond graphs [9].

2.2.1. Fixed causalityFixed causality occurs when the equations only allow one of the

power variables to be the outgoing or incoming signal. For in-stance, for flow sources (sinks) the output (input) is the knownflow signal.

2.2.2. Constrained causalityAt TF (or MTF), GY (or MGY), 0-junction, and 1-junction there

exist equations between power variables of different ports. There-fore, the causality of a particular port imposes the causality of theother ports. In a transformer element one of the ports has effort-out causality, and the other port should have effort-in causality.On the other hand, at a gyrator element both ports have either ef-fort-out or effort-in causality. At a 0-junction element, where allthe efforts are the same, one port must bring in the effort and allother ports must bring in the flow. At a 1-junction element oneport should bring in the flow and the rest should bring in the effort.

2.2.3. Preferred causalityAt the storage elements, the causality indicates whether the

integration or differentiation of the input signal should be oper-ated, with respect to time, to calculate the output signal. The inte-gration is always preferred because of less computational errors.

2.2.4. Indifferent causalityFor all other bond graph elements, e.g., an R element, no con-

straint can be considered in assigning the causality. Hence, thedirection of the input–output signals can be assigned arbitrarily,unless other elements impose a preferred direction.

Table 1Bond graph elements in various energy domains.

Energy domain Effort (e) Flow (f) Generalizedmomentum (p)

Generalizeddisplacement (q)

C I R

Translationalmechanics

Force Velocity Momentum Displacement Spring Inertia Damper

Rotationalmechanics

Torque Angular velocity Angularmomentum

Angle Rotationalspring

Moment ofinertia

Rotationaldamper

Electronics Voltage Current Linkage flux Charge Capacitor Inductor ResistanceMagnetics Magnetomotive

ForceMagnetic fluxrate

– Magnetic flux Magneticcapacitor

– Reluctance

Hydraulics Total pressure Volume flow rate Pressuremomentum

Volume Reservoir Fluid inertia Flow resistance

Thermodynamics Temperature Entropy flow rate – Entropy Heat capacitor – Heat resistanceChemical Chemical

potentialMolar flow – Molar mass – – –

168 R. Chhabra, M. Reza Emami / Mechatronics 21 (2011) 166–175


3. Mechatronic system modeling

A mechatronic system is a complex combination of several sub-systems. Generally, the subsystems can be divided into three cate-gories, namely: Generalized Executive, Sensory and Control. Flow ofenergy or information links these three generic subsystems to eachother [18]. Fig. 2 shows the graphical representation of mechatron-ic subsystems.

The generalized executive subsystem is the integration betweenactuator elements and executive mechanisms. The inputs of theactuators are various forms of energy, and the outputs are varioustypes of motion that match with the attached mechanisms. Themechanisms receive the corresponding motion, and create anotherform of motion. Depending on the type of the actuators, the flow ofvarious sorts of energy can be traced throughout a generalized exec-utive subsystem. Although in the generalized executive subsystemthere exist several types of elements and subsystems from differentphysical domains, the universal concept of power and power ex-change is common to all of them. Consequently, an energy-basedmodel can deem all the subsystems together with their interconnec-tions, and introduce generic criteria suitable for mechatronic systemdesign. As it was discussed in Section 2, bond graphs are able to offersuch a universal model of mechatronic systems.

A sensory subsystem consists of sensors and signal processingunits that perceive flow or effort in a bond graph model, and pro-cess them to send out the required feedback signals for controllingflow or effort of the generalized executive subsystem, such as veloc-ity, force and temperature. It also attempts to make the systemaware of its surrounding by measuring the environmental proper-ties. The measured state parameters are transmitted to the controlsubsystem to generate control signals. Therefore, the input to thesensory subsystem is typically in the form of energy, and the out-put is the signals that carry the amount of power variables.

The control subsystem is composed of microprocessors (micro-controllers) and drivers. The information sent by sensory subsystemis received and processed based on the utilized control laws. Theprocessed information flows to the driver, where this informationis translated to the amount of energy that should be provided tothe actuators of the generalized executive subsystem by the energysources. Therefore, the input to this subsystem is a set of measuredpower variables and the output is the energy that is required for con-trolling flow or effort of the generalized executive subsystem.

This subsystem also plays a significant role in mechatronics.The drivers can be represented by simple MTF elements in bondgraphs, which receive the control signal from the controller, anddistribute the supplied energy accordingly. In the computationalstage, bond graphs are converted into block diagrams that arecommonly used to simulate control systems [15]. Hence, a blockdiagram representation of a control subsystem can be merged

with the bond graph model of the generalized executive subsys-tem. However, to complete the loop and attach a control subsys-tem to bond graphs, transition elements, i.e. elements of sensorysubsystem, are required. The ideal sensors, which are used inthe proposed modeling scheme, can be simply represented by sig-nal flows going out of the bond graphs. They carry the value of ef-fort or flow, and enter the block diagrams. Therefore, thecombination of bond graphs and block diagrams as two powerfultools of simulation will result in an alternative modeling schemefor mechatronic systems in the concurrent design process. Detailsof creating such a model are illustrated in Section 6, where a fived.o.f. robot is simulated.

4. Thermodynamics of mechatronic systems

From a thermodynamic perspective, a mechatronic system canbe considered as a control mass that can exchange energy withits environment. This control mass together with the environmentform an isolated system (Fig. 3). The first law of thermodynamics,which is another expression of the universal physical law of theconservation of energy, can be restated in terms of bond graph no-tions as: ‘‘the increase in the energy of a system (E) is equal to thechange of input energy provided to the system (Ein) by a supplierand through the source elements, minus the change of the effectivework done by the system on its surrounding (W) at the sink ele-ments and minus the change of heat dissipation (Qirr) of the dissi-pative elements.” In differential form this law can be formulatedas:

dE ¼ dEin � dW � dQirr: ð7Þ

The Effective work is any useful type of energy that is ex-changed between a mechatronic system and its environment.

The energy of the system can be in a form of either introversiveor extroversive.

Definition 1. Introversive system energy: For a mechatronicsystem, introversive energy (iE) is the sum of all types of energy,including internal dynamic or potential energy, stored in thestorage elements of a system due to the interconnection of thesystem elements.

Definition 2. Introversive dynamic energy: For a mechatronic sys-tem, introversive dynamic energy (idE) is the total energy stored inthe I storage elements that conserve the generalized momentum.

Definition 3. Introversive potential energy: For a mechatronic sys-tem, introversive potential energy (ipE) is the total energy stored inthe C storage elements that conserve the generalized displacement.

Actuators Effective Work

Generalized Executive Subsystem

DriversInformation

Control Subsystem

MechanismsEnergyControl Laws Energy

Sensory Subsystem

Energy

Flow

/Eff

ort

Fig. 2. Graphical representation of generic mechatronic subsystems.



Based on the above definitions:

dðiEÞ ¼ dðidEÞ þ dðipEÞ: ð8Þ

Definition 4. Extroversive system energy: For a mechatronicsystem, extroversive energy (eE) is the sum of all forms of energythat are transacted between the environment and the system as awhole because of the physical constraints or external fields. Thistransaction is shown in the bond graph model of the systemthrough source or sink elements, in addition to Ein and W.

Based on the above definitions, (7) can be rewritten as:

dðidEÞ þ dðipEÞ þ dQirr ¼ dEin � dðeEÞ � dW; ð9Þ

where the change of energy state of the system elements on the lefthand side is calculated from the total energy transaction betweenthe system and environment at the sink and source elements onthe right hand side of the equation.

Furthermore, the second law of thermodynamics that is basi-cally the universal law of increasing entropy can be restated as:‘‘the total entropy of an isolated system, i.e. the mechatronic sys-tem and its environment, which is not at equilibrium, tends to in-crease, and it approaches its maximum value at equilibrium.”Therefore,

dsgen ¼ dsþ dsenv P 0; ð10Þ

where s and senv are the entropy of system and environment,respectively, and sgen is the entropy generation of the isolated sys-tem. One can view the environment surrounding a mechatronic sys-tem as an infinite heat reservoir, isolated from the outside world,with temperature Tenv that is assumed to be equal to the tempera-ture of the boundary of the system.

The input energy to the system can be divided into heat (Qrev),as the only form of energy that can alter the entropy, and theremaining other forms of input energy (E0in):

dEin ¼ dE0in þ dQrev : ð11Þ

Accordingly, for every mechatronic system one can define asubsystem, namely thermodynamic subsystem, which merely ex-changes heat and can be considered separately from the otherparts of the system. The heat exchange process is assumed revers-ible, and any irreversibility effect can be modeled by a thermal dis-sipative element. Based on the definition of entropy inthermodynamics,

ds ¼ dQrev

Tenv: ð12Þ

In the same manner, the entropy change of the environment canbe represented as:

dsenv ¼ �dQTenv

; ð13Þ

where dQ is the total heat transferred to the system; hence,

dsgen ¼dQrev

Tenv� dQ

Tenv¼ dQirr

TenvP 0: ð14Þ

Subsequently, by substituting (7) in (14),

dW 6 dðTenvs� Eþ E0inÞ � dWmax; ð15Þ

which implies that the change of effective work of the system is lessthan a maximum value (dWmax) during the transient phase of thesystem, and it reaches its maximum at equilibrium when dsgen = 0.Therefore, the work loss of the system, Wloss, can be defined as:

dWloss � dWmax � dW ¼ dsgenTenv : ð16Þ

Equilibrium occurs once the entropy generation reaches itsmaximum value, which according to (14) is equivalent to dQirr = 0.

5. Holistic design criteria

This section attempts to define a number of criteria that signifythe performance of a mechatronic system independently of indi-vidual subsystems, and based on the laws of thermodynamicsusing a bond graph system representation.

5.1. Energy criterion

Any mechatronic system is designed to perform a certainamount of effective work on its environment using the supplied in-put energy. Based on (9) the input energy (Ein) does not completelyconvert into the effective work (W), since portions of this energyare either stored (iE) or dissipated (Qirr) in the system by its com-ponents or transacted with the environment through physical con-straints or external fields (eE). The cost energy, defined as the sumof the energy of the system and the dissipated energy, cE � E + Qirr,is the overhead energy for performing an effective work using thesupplies. The lower the cost energy, the higher the efficiency ofsystem is. Therefore, as a design criterion cE(W(t);t, X) should beminimized with respect to the design variables, X, for a desired to-tal effective work within a time span of [0, tf] (wr ¼

R tf0

_WðtÞdt).From the first law of thermodynamics,

cEðt;XÞ ¼ Einðt;XÞ �WðEin; t;XÞ: ð17Þ

Therefore, the best design (X*) in the set of design availabilities(A) can be achieved by

cEðtf ; X�Þ ¼minX2A

cEðtf ; XÞ: ð18Þ

In the bond graph system model the supplied energy is the in-put energy that is provided to the system at the source elements.As explained in Section 2, source elements are divided into flow(Sf) and effort (Se). The supplied power at the ith source element(Si), _Ein;i is

_Ein;iðt;XÞ ¼~eS;iðt; XÞ �~f S;iðt; XÞ; ð19Þ

where~eS;i and~f S;i are the effort and flow in Si, respectively. The costenergy for the period of [0, tf] can be calculated as:

cEðtf ; XÞ ¼XNe

i¼1

Z tf

0

_Ein;iðt; XÞdt þXNf

i¼1

Z tf

0

_Ein;iðt; XÞdt �wr

¼XNe

i¼1

~eS;i �Z tf

0

~f S;iðt; XÞdt þXNf

i¼1

~f S;i �Z tf

0

~eS;iðt; XÞdt �wr

¼XNe

i¼1

~eS;i �~qS;iðtf ; XÞ þXNf

i¼1

~f S;i �~ps;iðtf ; XÞ �wr ;

ð20a-cÞ

Environment, (Tenv)

Isolated System

dW

irrdQrevdQ

indE

Subsystem

micThermodyna

SystemcMechatroni

dE

ds

Fig. 3. Energy representation of a mechatronic system.



where Ne and Nf are the number of effort and flow source elements,respectively, and ~qS;i and ~pS;i are the generalized displacement andmomentum of Si, respectively.

5.2. Entropy criterion

Based on the second law of thermodynamics, after a change insupplied energy, a mechatronic system reaches its equilibriumstate once sgen approaches its maximum. Eq. (16) shows that whilereaching the equilibrium state the system capacity for doing workreduces continuously. Thus, the less the work loss, the higher thesystem aptitude is to perform effective work on the environment.Therefore, as an alternative design criterion Wloss(Ein(t); X) shouldbe minimized with respect to X. Referring to (16), this is equivalentto minimizing sgen(t; X) or Qirr(t; X), and accordingly it is called en-tropy criterion. Given a unit step change of supplied energy, theequilibrium time, denoted by teq(X), is the time instant after whichthe rate of change of heat dissipation remains below a smallthreshold, e,

teqðXÞ ¼ Infft0 : 8t > t0_Q irrðt;XÞ < eg: ð21Þ

Consequently, the best design is attained in the set of designavailabilities by

Q irrðteqðX�Þ; X�Þ ¼minX2A

QirrðteqðXÞ; XÞ: ð22Þ

In bond graphs the power wasted in the ith dissipative element(Ri), _Q irr;iðt; XÞ, is calculated as:

_Q irr;iðt;XÞ ¼~eR;iðt; XÞ:~f R;iðt; XÞ; ð23Þ

where ~eR;i and ~f R;i are effort and flow in Ri, respectively. Subse-quently, the total heat dissipation is

Q irrðteqðXÞ; XÞ ¼XNR

i¼1

Z teqðXÞ

0

_Qirr;iðt; XÞdt

¼XNR

i¼1

bRi

Z teqðXÞ

0k~eR;iðt; XÞk2dt; ð24Þ

where NR is the number of dissipative elements, k � k denotes thenorm of a vector, and bRi is the resistance of Ri in its constitutiveequation.

5.3. Agility criterion

For systems whose response time is a crucial factor the rate ofenergy transmission through the system, or agility, can be a propermeasure of design. Thus, the design criterion can be defined suchthat after a unit step change of some or all input parameters thetime that the system needs to reach a steady state is minimized.A system reaches the steady state when the rate of its internal dy-namic energy, i _dE, becomes zero.

In the bond graph notation, idE is the total energy that is storedin I elements, and its time derivative is calculated as:

i _dEðt; XÞ ¼XNI

i¼1

~eI;iðt; XÞ:~f I;iðt; XÞ

¼XNI

i¼1

1bIi

~eI;iðt; XÞ:Z t

0

~eI;iðs; XÞds ¼XNI

i¼1

1bIi

~eI;iðt; XÞ~pI;iðt; XÞ;

ð25a;bÞ

where NI is the total number of I elements and~pI;i and bIi are the gen-eralized momentum and the resistance against the flow change of Ii,respectively.

Therefore, given a unit step change of input parameters, the re-sponse time, denoted by T(X), is the time instant after which therate of change of idE remains below a small threshold, d,

TðXÞ ¼ Infft0 : 8t > t0i _dEðt;XÞ < dg: ð26Þ

As a design criterion, when the response time reaches its mini-mum value with respect to the design variables in the set of designavailabilities the best design is attained:

TðX�Þ ¼minX2A

TðXÞ: ð27Þ

6. Case study

In this section kinematic, dynamic and control parameters of anindustrial five d.o.f. serial-link manipulator, CRS CataLyst-5, areconcurrently re-designed based on the three criteria proposed inthe previous section. The kinematic characteristics are definedbased on the standard Denavit–Hartenberg (D–H) convention[19]. Length (li), offset (di) and twist (ai) of the ith link are deemedas the kinematic design variables. Each link is modeled as anL-shaped circular cylinder along link length and offset, with thecylinder radius (ri). Thus, assuming a constant density for the ithlink, ri, li and di can be utilized to calculate the link dynamic param-eters, i.e., mass, moment of inertia and location of the centre ofmass. Fig. 4 depicts the robot manipulator and the link coordinateframes and definition of their D–H parameters. A servo controller(PI) with velocity feedback and feedforward is modeled to controlthe displacement of each manipulator joint. The control designparameters for the ith joint include proportional (Pi), integral (Inti),velocity feedback (Kvfb,i) and velocity feedforward (Kvff,i) gains.Therefore, the design problem deals with eight design parametersfor each link, i.e., three D–H parameters, link cross-section radiusand four control gains, and consequently, 40 variables in total.

The design problem is defined as: find the best values for theabove-mentioned design variables, from their design availabilities,such that the design criterion, i.e., energy, entropy, or agility, be-comes minimum, given the constraint that the end-effector (EE)overall position error determined for a set of desired trajectories re-mains lower than a certain amount. The average of the EE positionerror over the set of desired trajectories is a time-varying function,Eav(t).

EavðtÞ ¼1N

XN

i¼1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxiðtÞ � xd;iðtÞÞ2 þ ðyiðtÞ � yd;iðtÞÞ

2 þ ðziðtÞ � zd;iðtÞÞ2q

;

ð28Þ

where (xd,i(t), yd,i(t), zd,i(t)) are the coordinates of a point of the ithdesired trajectory at the time instant t, (xi(t), yi(t), zi(t)) are the coor-dinates of the EE point following the ith desired trajectory at thesame time. The number of desired trajectories is N.

The time average of Eav(t) can be considered as the EE overallposition error, denoted by Etot, and it is formulated as:

Etot ¼1tf

Xnt

i¼1

EavðtiÞðti � ti�1Þ; ð29Þ

where ftiji ¼ 0; . . . ; nt ; t0 ¼ 0; tnt ¼ tf g is a sequence of instants inwhich the desired trajectories are given. The initial configurationfor the design problem is that of the existing industrial manipulator.

A bond graph model of a generic serial-link n d.o.f. robot manip-ulator including joint modules and controllers is illustrated inFig. 5. In this figure, the flows and efforts are distinguished basedon their location at the corresponding power bond. The symbolon the right-hand-side (at the bottom) of a vertical (horizontal)power bond is effort and the one on the left-hand-side (at the



top) of a vertical (horizontal) power bond is flow. This model doesnot take into account the uncertainty of the design parameters. Thegeneralized executive subsystem consists of the electric motorsand the mechanical bodies connected at the joints. The sensorysubsystem contains a number of flow sensors mounted at thejoints, and the control subsystem is composed of n position con-trollers represented by block diagrams.

The bond graph model of the mechanical subsystem of the ro-bot manipulator is derived based on the exchange of power andmovement between the constituents of the physical system, whichresults in an alternative representation of system dynamics to theNewton–Euler formulation for a generic serial-link manipulatorexpressed in (30a-c) and (31a,b). The boundary conditions are zeroangular and linear velocities (flow) at the base and constant forceand zero moment (effort) at the end-effector. These equations canbe extracted by converting the bond graph model of the systemto the block diagrams and tracking the appropriate signals in themodel [15].

ixi ¼ iRi�1ði�1xi�1 þ i�1zi�1hiÞ; ð30aÞ

ivCi¼ iRi�1

i�1v i�1 � ði~ri þ i~rCiÞixi; ð30bÞ

iv i ¼ iRi�1i�1v i�1 � i~ri

ixi; ð30cÞ

where ixi is the angular velocity of link i expressed in frame i, andivCi

and iv i are the linear velocities of the centre of mass and frameorigin of link i expressed in frame i, respectively. iRi�1 is the rotationmatrix between frames i and i � 1, i�1zi�1hi is the angle between linki � 1 and i about the joint axis (joint angle), iri is the distance be-tween the origins of frames i and i � 1, irCi

is the position of centreof mass of link i, both measured and expressed in frame i. This set ofequations shows the flow propagation throughout the mechanicalsubsystem. And,

i�1i�1fi;i�1 ¼ iRT

i�1iifiþ1;i �mi

ig þddtðmi

ivCiÞ

� �; ð31aÞ

i�1i�1Ti;i�1 ¼ iRT

i�1iiTiþ1;i � i~rCi

iifiþ1;i þ

ddt

iIiCixi

� �þ i~ri þ i~rCi

� �ii�1fi;i�1

� �;

ð31bÞ

where i�1i�1fi;i�1 and i�1

i�1Ti;i�1 are the force and moment acting from linki on link (i � 1) at the origin of frame (i � 1) and expressed in frame

(i � 1), mi is the mass of link i, and iICiis the moment of inertia of

link i about its centre of mass and ig is the gravitational accelera-tion, both expressed in frame i. The skew-symmetric matrices i~ri

and i~rCiare built from iri and irCi

such that

x ¼x1

x2

x3

264375) ~x ¼

0 �x3 x2

x3 0 �x1

�x2 x1 0

264375:

Eqs. (31a) and (31b) reflect flow of effort in the bond graph mod-el of the serial-link manipulator.

The bond graph representation of the electric motors consists oftwo physical domains, i.e., electrical and mechanical. A gyrator ele-ment, using the torque coefficient of the motor (Kmi

) as the gyratorratio, relates these two domains. The electrical part includes a volt-age supply, a motor driver that is modeled by an amplificationgain, and a simple RL circuit. Using Kirchhoff’s circuit law, for eachjoint i one can write

dci

dt¼ 1

lmi

ðuai� rmi

ci � KmihmiÞ; ð32Þ

where ci, uai, hmi

, lmiand rmi

are the motor armature current, voltageoutput of the motor driver, angular displacement of the motor shaft,armature inductance, and total resistance of the armature winding,respectively.

The mechanical domain consists of the motor shaft moment ofinertia jmi

, viscous friction at the bearings, and transmission systemwith ratio gi. Therefore, the dynamic equation becomes:

d _hi

dt¼ gi

jmi

Kmici � gisi �

li_hi

gi

!; ð33Þ

where hi, li and si are the angular displacement of the joint, frictioncoefficient, and the reaction torque on the ith joint module, respec-tively. The actuators parameters of the CRS CataLyst-5 have beenused in this simulation, as listed in Table 2.

The bond graph model of the robot manipulator was pro-grammed in MATLAB� Simulink and a gradient-based, constrainednon-linear optimization algorithm, called fmincon, was employedto optimize the design criteria, separately. In each optimizationloop, the design variables are changed in the simulation and a de-sign criterion is determined by calculating the power transmissionat the corresponding constituents of the system. The cost energy isevaluated based on (20a-c) by calculating _Ein;iðtÞ at all effort sources

li The length of common normal between Zi-1 and Zi along Xi

i The angel between Zi-1 and Zi measured about Xi

di The distance from Xi-1 to Xi measured along Zi-1

i The angel between Xi-1 and Xi measured about Zi-1

Z4

X4

Y4

Z0

X0

Y0

Z2

X2

Y2

Z3

X3

Y3

Z1

X1

Y1

Z5

X5

Y5

Z4

X4

Y4

Z0

X0

Y0

Z2

X2

Y2

Z3

X3

Y3

Z1

X1

Y1

Z5

X5

Y5

Fig. 4. The CRS CataLyst-5 manipulator, its schematic and link coordinate frames and D–H parameters.



in the electric motors and _WðtÞ at the effort sink in the EE. Entropycriterion is calculated by (24) where _Qirr is equal to the sum of thepower transmission at all R elements in Fig. 5 and teq = tf for all con-figurations. In the case of agility criterion, all desired angles forjoint controllers are given a unit step at t = 0. Based on (25a,b)and (26), i _dEðtÞ and accordingly T are computed by determiningpower transmission at all I elements of the system. The three sep-arate designs based on energy, entropy and agility criterion areshown in Table 3. The initial kinematic, dynamic and control de-sign variables were according to the existing configuration of theCRS CataLyst-5 that was designed through the traditional parti-tioned approach. In this case study, the intention is to minimizeeach of the holistic design criteria separately and maintain Etot, de-fined in (29), below a threshold of 10 mm. It is worth mentioningthat having a threshold for Etot results in upper bounds for the finalposition error and the maximal error of each considered trajectory.These two errors are equally significant in the design of a robotmanipulator. In addition, fmincon attempts to minimize the con-straint, as well as the design criterion. Thus, convergence of theoptimization algorithm leads to the convergence of the final posi-tion error and the maximal error of each trajectory.

According to Table 3, the design based on the energy criterionresults in the reduction of the radius of the first and second linksby 16.8% and 6.8%, respectively, and increase of that of the lastthree links by 31.5%, 122% and 195%, respectively, with respectto the initial configuration. Regarding the kinematic design vari-ables, the length of links 1–5 has changed by +95, �9.1, +3.3,+121.7, +45.6 mm, respectively. Furthermore, the offset for links1–5 has changed by �65.4 mm, +32.7 mm, +18.4 mm, +37.3 mmand +78.6 mm, respectively. The only significant change in the linktwist has happened for link 4 by 36.2%. These modifications resultin the change of the mass of first three links for �22.6%,�5.4%,+87.8%, respectively, and the last two links have become3.9 and 17.5 times heavier, respectively. As a result, the total massof the robot has been decreased by 1%, which is one reason forreducing the energy consumption at the joints. From the controlpoint of view, the proportional gain of links 2 and 4 has increasedby 10% and 13%, respectively. The integral gain of the first, secondand fifth links has also increased by 17%, 48% and 62%, respectively.Moreover, the velocity feedback gain of joint 2, 3 and 5 has chan-ged by 14.8%, 18.2% and 25%, respectively. All other control gainshave been modified slightly by less than 10% to improve the perfor-mance and accuracy of the manipulator. All of the above men-tioned changes are in the direction of reducing the overall massof the robot and increasing the precision and smoothness of thesystem behaviour to minimize the cost energy. This energy-baseddesign leads to 20.1% reduction in the cost energy, but entropyand agility criteria increase in this design by 7% and 35.9%,respectively.

For the design based on the entropy criterion, the radius of thefirst three links has changed by +5.5%, �6.5% and �8.3%, respec-tively. The length of links 1, 4 and 5 has increased by 39.8, 21.123.2 mm, respectively, and that of the second and third links hasreduced by 26.7 mm. In addition, the twist of links 1, 2 and 4 haschanged by 18.9, 8.1 and 8.3 degrees, respectively. These modifica-tions lead to +26.3%, �21.3%, �24.8%, +7.4% and +67.5% change of

(a)

(b)

(c)Fig. 5. Bond graph representation of (a) a serial-link manipulator, (b) an electricmotor, and (c) the controller block diagram.

Table 2Motor parameters used in the simulation (CRS CataLyst-5).

Vi (V) rmi (Ohm) lmi (mH) Kmi (N.m/A) jmi(g.cm2) gi li (N.m.s/rad)

Link 1 4 3.3 3 0.2587 68 1/72 0.0001Link 2 3.6 1.8 2.5 0.4414 300 1/72 0.0001Link 3 3.6 1.8 2.5 0.4414 300 1/72 0.0001Link 4 4 3.3 3 0.2587 68 1/19.6 0.0001Link 5 4 3.3 3 0.2587 68 1/9.8 0.0001



the mass of the 5 links, respectively. Considering the controlparameters, a notable increase in the proportional gain of the lasttwo links can be observed for 15.7% and 19.5%, respectively. Theintegral gain of the third and forth links has decreased by 7.3%and 8%, respectively. The feedforward gain of the last four jointshas also changed by �6.7%, �6.9%, +12% and +17.4%, respectively.All other design parameters have been modified by less than 5%in order to minimize the design criterion and keep Etot below thethreshold. In this design, the irreversible heat exchange has beenreduced by 23.3%, and energy and agility criteria have also beenenhanced with respect to the initial configuration by 12% and87%, respectively.

Considering the design solution based on the agility criterion,except for the first link whose radius has decreased by 5%, the ra-dius of the other four links has increased by 29.6%, 14.1%, 7% and65%, respectively. The length of links 1–5 has changed by57.6 mm, �25.7 mm, �30.6 mm, 16.7 mm and 29 mm, respec-tively. Table 3 also shows an increase in the offset of the last fourlinks by 57.4, 74.9, 23.4 and 33.5 mm, respectively. Furthermore,the twist of the first four links has been modified by 6.3, �5.9,13.9 and 13 degrees, respectively. Overall, these modifications re-sult in +9.3%, +88.1%, +52.8% and +17.3% change in the mass ofthe first four links, respectively, and the last link has become3.6 times heavier. Regarding to the control design, the proportionalgain of the last three links has increased by 13.4%, 21.3% and 7.3%,respectively. The feedback gain of the third and fifth link has alsoincreased by 14.9% and 17.8%, respectively. Besides, the integralgain of the last three links has decreased by 6.7%, 13.5% and 7%,respectively. In terms of the feedforward gain, links 2 and 3 showa change of �6.7% and +8.1%, respectively. The rest of the designparameters have been modified by maximum 5%. As a result, forstep input trajectories the manipulator response is almost 10 timesfaster and the energy and entropy criteria have changed by +1.3%and �11.7%, respectively.

Fig. 6 depicts the average of the EE position error, formulated in(28), for a set of desired trajectories considered in the design,including step, ramp, pick-and-place, and periodic. For all of thedesign solutions the error variation for almost the entire operationtime remains below the error curve for the initial configuration.The EE overall error, Etot, for the initial and alternative designs iscalculated from (29) and listed in Table 3. For the design basedon energy, entropy and agility criteria Etot has decreased by 75%,56.4% and 49.5%, respectively. Note that the end-effector errorfor the initial configuration is 18.0 mm, which is more than the

10 mm threshold set in the design. An interesting observation isthe way that each design criterion affects the error variation. Theagility criterion seems to result in relatively high error variations,due to the fact that it attempts to speed-up the system response,while by finding a proper combination of control gains and kine-matic and dynamic parameters it tries to maintain the overall errorbelow the threshold. In case of energy criterion, the system re-sponse begins by a slightly larger error but tends to rather smoothout the error variation, because energy criterion does not favourdrastic control commands at the joints to save energy. The entropycriterion attempts to reduce the wasted irreversible energy, in theform of heat generated by friction and electric resistance. This is re-flected in the end-effector error response as a relatively constantcurve with local oscillations to ensure that the equilibrium timeremains as short as possible.

From Table 3, all three design solutions result in notablechanges in kinematic, dynamic, and control parameters comparedto those of the existing design of the CRS CataLyst-5 manipulator.Each solution improves the corresponding criterion as well as theEE overall error. Nonetheless, the entropy-based design seems toenhance all three design criteria simultaneously, and hence itcould present a reasonable compromise for all three criteria andthe EE overall error.

Table 3Design results.

ri (mm) li (mm) di (mm)

Joint 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Initial 65.6 27.7 24.1 10.0 10.0 0.0 254.0 254.0 0.0 0.0 254.0 0.0 0.0 0.0 0.0Final (Energy) 54.6 25.8 31.7 22.2 29.5 95.0 244.9 257.3 121.7 45.6 188.6 32.7 18.4 37.3 78.6Final (Entropy) 69.2 25.9 22.1 9.7 10.3 39.8 227.3 227.3 21.1 23.2 248.2 2.2 0.0 0.0 18.7Final (Agility) 62.3 35.9 27.5 10.7 16.5 57.6 228.3 223.4 16.7 29.0 249.5 57.4 74.9 23.4 33.5

ai(�) Pi Inti

Joint 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5Initial �90.0 0.0 0.0 �90.0 0.0 20.48 22.26 13.00 12.00 10.05 0.100 0.100 0.150 0.200 0.100Final (Energy) �88.6 2.6 7.77 �57.4 17.3 20.17 24.51 12.73 13.58 9.53 0.117 0.148 0.156 0.212 0.162Final (Entropy) �71.1 8.3 �1.5 �81.9 �0.2 20.79 23.92 13.34 13.89 12.01 0.095 0.095 0.139 0.184 0.098Final (Agility) �83.7 �5.9 13.9 �77.0 0.3 20.14 21.53 14.74 14.56 10.78 0.103 0.101 0.140 0.173 0.093

Kvfb,i Kvff,i Criterion EE overall error

Joint 1 2 3 4 5 1 2 3 4 5 cE(J) Qirr(J) T(s) Etot(mm)Initial 41.1 39.7 24.1 23.6 22.4 44.38 48.25 33.29 25.00 23.00 21.95 6.39 4.34 18.3Final (Energy) 39.3 45.6 28.5 22.1 28.0 44.08 50.38 32.19 26.62 25.01 17.53 6.84 5.90 4.5Final (Entropy) 41.7 40.4 25.2 24.6 23.4 46.50 45.00 31.00 28.00 27.00 19.32 4.90 0.55 8.0Final (Agility) 40.6 41.5 27.7 24.2 26.4 45.30 45.02 36.00 26.27 24.25 22.23 5.64 0.41 9.2

Fig. 6. The average of end-effector position error for a set of desired trajectoriesbased on different criteria.



7. Conclusions

In this paper, mechatronic systems were deemed as energy sys-tems, and first and second laws of thermodynamics were reformu-lated for defining several design criteria using bond graph notation.This holistic system modeling approach is suitable for concurrentdesign of mechatronic systems. Three design criteria, namelyenergy, entropy and agility, were introduced. The choice of thecriterion for a design problem is up to the designer, and it alsodepends on the critical performance measures. For instance, fordesigning many systems that are used in the space industry wherepower efficiency is important, energy criterion can be employed,whereas for thermodynamic systems entropy criterion is usuallyappropriate, and for systems that are designed to operate fast,agility criterion can be optimized. As a case study, the bond graphmodel of a five d.o.f. industrial serial-link manipulator wasdeveloped, and its kinematic, dynamic, and control parameterswere re-designed concurrently through optimizing each of thedeveloped holistic design criteria separately. The resulting designsshow superior performance compared to the existing manipulatorconfiguration, which highlights the significance of synergy in thedesign of mechatronic systems through proper means.

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