# Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

**ABSTRACT** The DynLAB project developed by an inter-national consortium aims at motivating young people to engineering study, and at improving engineering training using innovative didactic and technological approaches. The resulting web-based course is supported across the Internet by a software environment including a robust DYNAST simulation engine, publishing and monitoring tools, and a large collection of re-solvable examples including 3D virtual experiments. DYNAST solves nonlinear algebro-differential equations submitted in a textual form. For a system model submitted in a graphical form characterizing the system real configuration DYNAST formulates the underlying equations automatically. It is also capable of providing the system semisymbolic analysis in time- and frequency-domains. Besides this, DYNAST can be also used across the Internet as a modeling toolbox for the MATLAB control-design toolset.

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- Joining forces in engineering education towards excellence: Proceedings SEFI and IGIP joint annual conference, Miskolc, July. 01/2007;
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**ABSTRACT:**Principles of virtual model construction for design of controllers of controlled electrical drive and corresponding remote experiment for electric drives are presented. The described remote experiment is based on custom developed motor control hardware controlled by PLC and ControlWeb SW is used for graphical user interface development and remote operation. A combination of the presented hardware and software solutions enables quick and easy development of different remote control experiments. The paper is completed by an example of a remote motor position control of a hoisting apparatus.Mechatronika. 07/2008;

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

Learning Dynamics and Control

Using Remotely Tutored Simulation

and Virtual Experiments

H. Mann, M. Sevcenko

Czech Technical University / Computing and Information Centre, Prague, Czech Republic

Abstract—The DynLAB project developed by an inter-

national consortium aims at motivating young people to

engineering study, and at improving engineering training

using innovative didactic and technological approaches. The

resulting web-based course is supported across the Internet

by a software environment including a robust DYNAST

simulation engine, publishing and monitoring tools, and a

large collection of re-solvable examples including 3D virtual

experiments. DYNAST solves nonlinear algebro-differential

equations submitted in a textual form. For a system model

submitted in a graphical form characterizing the system real

configuration DYNAST formulates the underlying equations

automatically. It is also capable of providing the system

semisymbolic analysis in time- and frequency-domains.

Besides this, DYNAST can be also used across the Internet as

a modeling toolbox for the MATLAB control-design toolset.

I.

II.

•

using computers to carry out old exercises without

radical modification of the curriculum to incorporate

computers in a way fully exploiting their contem-

porary capabilities

PROJECT DYNLAB

A project called DynLAB – Course on Dynamics of

Multidisciplinary and Controlled Systems in a Virtual Lab

has been developed by an international consortium [2]. The

emphasis and style of the course differs from most of the

existing courses by a number of innovative features:

•

exposing learners to a novel systematic and efficient

methodology for realistic modeling of multi-

disciplinary system dynamics applicable to electrical,

magnetic, thermal, fluid, acoustic and mechanical

dynamic effects in a unified way

•

introducing learners to the methodology through

simple, yet practical, examples to stimulate their

interest in engineering before exposing them to

rigorous theory and advanced mathematics

•

giving learners a better ‘feel’ for the topic by problem

on-line simulation, graphical visualization, and by

interactive virtual experiments

•

allowing different target groups to select individual

paths through the course tailor-made to their actual

needs and respecting their background

•

allowing both for self-study and remote tutoring with

investigative and collaborative modes of learning

•

integrating computers into the course curriculum

consistently and giving learners a hands-on

opportunity to acquire the necessary skills

•

exploiting the computers not only for equation

solving, but also for their formulation minimizing

thus learners’ distraction from their study objectives

•

giving learners the opportunity to benefit from

‘organisational learning’,

knowledge recorded during previous problem solving

both in academia and industry

The intended target groups of the DynLAB course are

students wishing to complement the traditional courses,

distance-education students at different levels of study,

practicing engineers as a part of their life-long learning as

well as teachers intending to innovate the courses they

teach.

Index Terms—control, dynamics, e-learning, Internet, remote

simulation, virtual experiments.

INTRODUCTION

The subject of dynamics and control underlies all

aspects of modern technology and plays the determining

role in the world-market competition of engineering

products. Its importance increases with the ever-growing

demands on operational speed, efficiency, safety,

reliability, or environmental protection of the products.

Nevertheless, national authorities and entrepreneurs in

many countries report lack of qualified engineers as well

as a critical overall decline of interest in engineering study

among young people. Professional associations call for

radical changes in the engineering curriculum and for new

innovative approaches to vocational training (e.g. [1]).

The existing courses on dynamics and control are

criticized namely for

•

discouraging young people from engineering study by

overemphasis on theory and mathematics at the

expense of practical engineering issues in the

curriculum

•

separating courses on dynamics analysis along the

borders between the traditional engineering discip-

lines despite the fact that most of the contemporary

engineering products are of multidisciplinary nature

•

presenting ‘textbook’ problems carefully engineered

to fit the standard ‘underlying’ theory without having

the students to undertake realistic modeling

i.e. from utilizing

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

III. MULTIDISCIPLINARY DYNAMICS

The engineering systems become more and more

complex with regards both to the number of their

components as well as to the variety of phenomena

affecting their dynamics, either in a useful or undesirable

way. The phenomena involved in system dynamics might

come simultaneously from different energy domains

treated traditionally by different engineering disciplines

(electrical, electronic, magnetic, mechanical, fluid,

acoustic, thermal, etc.). To be able to cope with the

contemporary systems, engineering students should be

introduced into an approach to system dynamics treating

phenomena from different domains in a unified way. Such

a unified approach into engineering study gives students a

more comprehensive view of the real world.

There are two additional advantages to the unified

courses on system dynamics besides the ability to cope

with the contemporary systems. First, a properly planned

curriculum which includes such a course avoids

unnecessary duplication. This allows introducing more

advanced material into the study program. Second, a

unified course is consistent with the growing tendency of

students to decide on their branch of engineering relatively

late in their study. Such a course is also appropriate as a

part of a program of continuing education for graduated

engineers who received their degrees earlier.

To understand and predict the dynamic behavior of such

systems as well as to design them, engineers resort to

computer-assisted modeling, simulation and analysis.

Introduction of these techniques allows engineers

considering a larger variety of designed systems and for

their thorough verification before the system prototypes are

constructed and tested experimentally. Testing by

simulation is the only option in cases where experimen-

tation is too expensive or dangerous. Simulation also helps

to maintain the systems, and in the case of a system failure

it can be used to diagnose the failure cause. As

computation techniques allow for introducing new products

of higher-quality into the market faster, they help in

acquiring higher profits.

Simulation is also a well proven learning tool helping to

facilitate learners’ comprehension of dynamics and control

principles. A modern course on engineering dynamics

should consider multi-level modeling. When designing a

complex system, engineers resort to dynamic models of

several levels of system abstraction and idealization. The

design process usually starts by the conceptual design

phase of the highest abstraction and aims towards the

technological phase in which an assembly or some other

way of system production is designed. Between these, also

two intermediate design phases, both concerned about

system dynamics, can be recognized.

In the functional design phase, interactions between the

system components are assumed to take form of physically

dimensionless signals, states and disturbances. Typically,

the design of automatic control, of digital circuitry

architecture, or of imbedded software is carried within this

phase. The physical design phase is concerned about

implementation of the system architecture, functions and

signals in terms of physical phenomena and quantities.

Both useful and undesired multi-domain physical effects

are considered here in terms of energy transfer,

accumulation and dissipation. The interrelationships of

these effects are governed by physical laws.

DYNAST SOFTWARE

IV.

A.

B.

C.

Efficient simulation

In the past, efficiency of simulation was evaluated with

regard to its demand of computer time only. Nowadays,

however, the computer time is so inexpensive that the cost

of simulation is dominated by the cost of personnel

required to prepare the input data, to supervise the

computation and to interpret the results. Therefore, an

efficient simulation software tool should minimize

demands on its users’ time and qualification. In the other

words, software should be sufficiently user-friendly and

computationally robust.

When evaluating simulation software one should take

into consideration its application area. Models of high

abstraction and idealization used in the conceptual design

of control, for example, can be conveniently represented by

block diagrams. Using such block diagrams for physical-

level models is, however, a cumbersome and error prone

task. It requires an involved manual formulation of the

underlying equations and, in addition, manual construction

of block diagrams representing the equations.

DYNAST simulation software

To comply with the above mentioned efficiency

requirements, the DYNAST software package was chosen

as the kernel tool for multidisciplinary system simulation

within the DynLAB course.

DYNAST provides there:

•

solution of nonlinear differential and/or algebraic

equations submitted in a natural textual form

•

simulation of real dynamic systems the models of

which are submitted in a graphical form resembling

the real system configuration (the underlying

equations are then formulated automatically)

•

semisymbolic-form transfer functions and responses of

automatically linearized system models

•

online support for simulation, virtual experiments, and

monitoring of submitted tasks

•

modeling toolbox for MATLAB and Simulink

Multipole modeling

The automatic formulation of equations for physical-

level modeling of multidisciplinary systems is based in

DYNAST on multipole modeling. Such a modeling

procedure starts with decomposition of the modeled

systems into disjoint subsystems. This idea is similar to free

body diagrams in mechanics or control surfaces in

thermodynamics, for example. A subsystem multipole

model approximates the subsystem energy interactions

with the rest of the system under the assumptions that

•

the interactions take place just in a limited number of

interaction sites formed by adjacent energy entries

into the subsystems (like fluid inlets, electrical

terminals, translating

connections, heat-transferring contact surfaces, etc.)

•

the energy flow through each such entry can be

expressed by a product of two complementary power

variables (force – velocity, torque – angular velocity,

volume flow rate – pressure, electrical current –

voltage, magnetic flux rate – magnetic voltage, or

entropy flow – temperature)

or rotating mechanical

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

Each energy entry into a subsystem is represented in its

multipole model by a pole associated with a pair of the

power variables. In graphical symbols of individual

multipoles, the poles are denoted by pins, i.e. by short line

segments sticking out of the symbol outlines. Dynamics of

a complete system is represented graphically by a multipole

diagram consisting from symbols of subsystem multipole

models. The sites of energy interaction between adjacent

energy entries are portrayed in the diagram by the diagram

nodes. The energy entries interacting mutually are

represented in the diagram by pins interconnected to the

same node by line segments called links. The links can be

viewed as idealized subsystem interconnections capable of

transferring energy in both directions without any

dissipation, accumulation or delay.

In each energy domain, the most rudimental multipoles

represent pure twopoles like pure energy sources,

accumulators and dissipaters. Energy conversion from one

domain to another one can be modeled by pure transducers.

Only four different pure transducers are needed to model

all kinds of electro-magnetic, electro-mechanical, magneto

mechanical, fluid-mechanical, rotary-rectilinear, and other

energy conversions. Multipole models of real subsystems

like electronic or fluid devices, various mechanisms,

motors or sensors, heating or cooling units, etc., can be

build up from the pure multipoles. The multipoles can be

combined also with blocks or equations.

Fig. 1 shows the cross-section of a copying lathe and the

corresponding multipole model representing dynamics of

the lathe mechanical and hydraulic components.

The most important advantage of multipole diagrams

over block diagrams or bond graphs is in the isomorphism

between their structure and the geometric configuration of

the modeled real systems. In fact, multipole diagrams are

mappings of real system representations from the

geometric onto the topological space. The practical

consequence of the isomorphism is that the multipole

diagram can be set up in a kit-like fashion in the same way

in which the real system is assembled from its subsystems.

Such a modeling procedure can be based on mere

inspection of the modeled real system. Recollect that a

block diagram is just a graphical representation of a set of

equations. The line segments interconnecting blocks are

associated with just one variable that can propagate in one

direction only.

(b)

(a)

Figure 1. (a) Copying lathe, (b) its multipole model.

D.

V.

A.

The DynLAB course is delivered within a web-based

learning environment supporting

collaboration and their communication with a tutor.

Investigative learning is encouraged by a large collection

of solved problems and virtual experiments. The examples

can be resolved and modified in an interactive way across

the Internet. This gives the learners a hands-on oppor-

tunity to acquire the necessary skills in solving real-life

problems.

Submodel libraries

DYNAST is accompanied by libraries of submodels for

electronic and fluid-power devices, electro-mechanical

transducers, mechanism parts, control units, etc. The

submodel dynamics can be described by a combination of

multipoles, blocks, and equations nested in a hierarchical

way. The libraries are open for easy addition of user-

defined submodels and their symbols. Each DYNAST

submodel description is encapsulated in an independent

file. The default values of submodel parameters can be

overridden by values specified in terms of constants or

symbolic expressions. Fig. 1 shows the dialog used to

specify parameters of a hydraulic cylinder making a part of

a machine.

Using the multipole approach is also of several other

important advantages:

•

multipole models can be developed, debugged, tuned

up and validated once for ever for the individual

subsystems independently of the rest of the system,

and once they are formed they can be stored in

submodel libraries to be used any time later

•

this job can be done for different types of subsystems

(e.g., fluid power devices, electronic elements,

electrical machines, mechanisms, etc.) by specialists

•

the submodel dynamics can be represented by

different descriptions each of them suiting best to the

related engineering discipline (lagrangian equations in

mechanics, circuit diagrams in fluid power or

electronics, block diagrams in control, etc.)

•

modeling refinement or subsystem replacement (e.g.,

replacement of an electrical motor by a hydraulic one)

can be taken into account by a submodel replacement

without interfering with the rest of the system model

LEARNING ENVIRONMENT

Distributed simulation system

learners’ mutual

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

Figure 2: Environment for remote modeling, simulation and virtual experiments.

As shown in Fig. 2, the kernel of the distributed

simulation system is formed by the DYNAST Solver that

the learners can access across the Internet in several ways

including even e-mail. Setting up multipole and block

diagrams directly on a web page is enabled by the

schematic editor DYNCAD formed by a Java applet.

DYNCAD converts diagrams into the DYNAST input

language and sends the data to the DYNAST Solver across

the Internet. After the computation results are sent back

and plotted on the client-computer screen.

Remote tutoring is enabled by the software tool called

DYNAST Monitor that is linked to the DynLAB server

across the Internet. It allows tutors to observe textual data

and graphical-form diagrams submitted by learners to the

DYNAST Solver. Tutors can not only monitor learners’

activities, but they can also communicate with them, assist

them in solving their problems and correct their errors if

necessary. DYNAST Monitor appeared to be very useful

even for tutors sharing the same computer room with

learners.

C.

Support for control design

(a)

B.

User-friendly simulation environment

Even more comfortable and user-friendly mode of

access to DYNAST Solver provides DYNAST Shell. This

mode requires, however, downloading and installing this

free software on client computers with MS Windows.

DYNAST Shell has been designed for a wide variety of

tasks in a way suitable to users of different levels of

qualification and experience. All operations are intuitive

and they are supported by a context-sensitive help system.

A built-in syntax analyzer is continuously checking the

submitted data. Dialog windows (wizards) in DYNAST

Shell allow for submitting data without knowledge of the

input language (though DYNAST input language is very

user-friendly and sounds natural to engineers). The input

data is directly interpreted without any compilation delay.

DYNAST Shell includes graphical editors for multipole

and block diagrams as well as for submodel symbols. A

special dialog box for each new submodel is formed

automatically as shown in the screenshot of the DYNAST

Shell graphical interface shown in Fig.1b.

DYNAST Shell can also communicate with the server-

based DYNAST Publisher. It is a documentation system

for automated publishing reports on simulation experi-

ments and descriptions of library submodels using LaTeX.

The systems extracts automatically the relevant parts of the

input data and captures the submitted multipole or block

diagrams as well as the resulting output plots and includes

them into the documents. The documents can be converted

by the server software into PostScript, PDF and HTML

formats.

(b)

(c)

(d)

Figure 3: (a) Inverted pendulum, (b) multipole model,

(c) analog PID control, (d) digital PID control.

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

In control design, the modeling efficiency of DYNAST

can be combined to a great advantage with the control-

design power of the MATLAB toolsets. Using DYNAST, a

model of the plant to be controlled can be easily set up in a

graphical form and then used to validate the open-loop

model. At the same time, DYNAST is able to compute the

required plant transfer-function poles and zeros and to

export them to MATLAB in an M-file. When the control

feedback loop is designed in the MATLAB environment

and added to the plant model in DYNAST, the complete

nonlinear control system is verified in DYNAST. In the

case of digital control design, the control system

configuration is implemented in Simulink. The block

representing there the controlled plant remains, however, in

DYNAST and communicates across the Internet with the

rest of the diagram using the Simulink S-function.

Let us consider analog-PID control design for the plant

in the form of the inverted pendulum given in Fig 3a. As it

is shown in [4], a considerable number of manual

operations is necessary before MATLAB can be exploited

for computation of transfer functions for such a plant.

DYNAST allows for avoiding all these tedious manual

operations. Fig. 3b shows a multipole model of the

pendulum, which has been set up using the web-based

schematic editor DYNCAD. After verification of the plant

open-loop responses using DYNCAD, an M-file with the

plant transfer functions is exported across the Internet to

MATLAB installed on a client computer. The plant PID

control design by means of MATLAB can than proceed as

described in [4]. Then the resulting feedback loop can be

added to the plant model in DYNCAD as shown in Fig. 3c.

Finally, the complete nonlinear control system is verified

in DYNCAD.

Also in the case of digital-PID control design the

transfer-function data for the plant model is first exported

to MATLAB. Then the digital control design can proceed

as described in [4]. To verify the design in this case, the

digital feedback loop is implemented in SIMULINK as

shown in Fig. 3d. The large square block represents there

the plant multipole model in DYNAST shown already in

Fig. 3b. The communication across the Internet between

(c)

(d)

(b)

Figure 4. Three tank virtual experiment.

(a)

this model remaining in DYNAST and the rest of the

diagram implemented in SIMULINK is enabled by the

SIMULINK S-function available for downloading at [2].

D. Virtual experiments

To stir up learners’ interest in dynamics and control as

well as to enhance their understanding of the topics, the

course text is augmented by 3D virtual experiments, most

of them interactively controllable. So far, the following

experiments are available at the project website: Carriage

& pendulum, Two tanks, Ball and beam, Gyro pendulum,

Three tanks, VTOL (Vertical Take Off and Landing)

aircraft emulator, Chemical reactor, Hydraulic cylinder,

Optical tracker, Two-link planar robot. The motions of

objects in all the experiments are driven by DYNAST

across the Internet. The only software the learners need to

download and install on their computers to be able to

observe the experiments, is the Cortona freeware VRML

browser.

As an example, Fig. 4a shows the three tank virtual

experiment. By clicking the mouse over the screen of their

computer students can adjust the red level marks on tanks 1

and 2, open or close any of the 6 valves interconnecting the

tanks with each other or with an outlet, and they can switch

on the pumps. This allows students to try to control the

system manually in such a way that the levels in tanks 2

and 3 reach the level marks as soon as possible and stay

there. Then they can go to the automatic control exploiting

the default PID control, or they can test a control algorithm

of their own design. The default control is illustrated by

Fig. 4b, Fig. 4c shows the physical-level multipole model

of the controlled plant. Submodels of motors, pumps,

valves and open tanks are included in a lower hierarchical

modeling level.

Fig. 5a shows a 3D virtual geometric model of a robot

the motion of which is governed by DYNAST simulation

across the Internet. The simulation utilizes the model of

robot dynamics given in Fig. 5b. Besides the robot motion,

learners can observe plotted responses of various robot

variables like the arm trajectory shown in Fig. 5c, for

example.

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Learning Dynamics and Control Using Remotely Tutored Simulation and Virtual Experiments

VI.

(c)

(a)

(b)

Figure 5: Robot: (a) virtual model, (b) dynamic model, (c) robot-arm trajectory.

LEARNING MODES

A short survey of different learning modes supported in

DynLAB environment is given in Table 1. Novices are

motivated to a more involved engineering investigation of

system dynamic behavior by ‘playing’ with movable 3D

virtual-reality models of various systems. They can change

the model parameters and excitation while observing the

model behavior not only qualitatively, but also

quantitatively using virtual measuring instruments. In the

next step, students can model, simulate and analyze

dynamic behavior of given systems. The most advanced

students are supposed to design controlled dynamic

systems, to verify the designs and to optimize them.

TABLE I.

LEARNING MODES IN THE DYNLAB COURSE.

Course assignment

Learning

objective

Prerequisites

high-school math and

physics

high-school math and

physics

fundamentals of

system dynamics

introduction to

dynamic modeling

formulation of system

equations

introduction to

control design

advanced dynamic

modeling

Given Task

stirring up interest in

dynamics

introduction to

dynamic modeling

more advanced

dynamic modeling

formulation of system

equations

introduction to

control design

introduction to

system design

design of virtual

experiments

3D virtual model

of a real system

configuration of

a real system

configuration of

real components

configuration of

a real system

plant specification

& control objectives

system specification

to modify system parameters or excitation and to observe changes

in the system dynamic behavior

to set up the corresponding multipole diagram and to simulate the

system dynamic behavior

to set up multipole models and symbols of components, store

them in a library, and validate their dynamic behavior

to form system equations, to solve them, and to compare the

solution with the multipole-based simulation results

to reduce the model, to design control, and to verify the design

using the plant unreduced model

to design the plant as well as its control, then to verify and

optimize the overall design

to design the virtual 3D geometric model, to set up the dynamic

model, and to write the simulation script

experiment

specification

CONCLUSIONS

The paper has presented a brief overview of a distributed

software environment for a web based course on dynamics

and control utilizing the Dynast modeling and simulation

package. The course content as well as the software is

available on the web at http://virtual.cvut.cz/dynlab/. Major

features of the course are the viewpoint adopted the

novelty of some of the examples, the ease of being able to

study the effects of parameter variations and other aspects

available with simulations created for user involvement;

and the presence of some virtual reality experiments which

can be operated manually or in closed loop.

ACKNOWLEDGEMENTS

The authors wish to acknowledge the support of the

Leonardo da Vinci Programme in funding the project and

the contributions to it of the other partners. In particular the

contribution to the virtual reality experiments [5] by C.

Schmid, the Ruhr-Universität partner, is greatly appre-

ciated.

REFERENCES

[1] “Future Directions in Control Education”, IEEE Control Systems,

Vol. 19, No. 5, Oct. 1999.

[2] Website of DynLAB, Pilot Project of the EU Leonardo da Vinci

Vocational Training Programme, at http://virtual.cvut.cz/dynlab/

[3] Mann, H., M. Ševcenko: “Internet-Based Collaboration and

Learning Environment for Efficient Simulation or Control Design”.

Proc. Congress ASME, DSC 71, Washington D.C., 2003.

[4] Messner, B. and D. Tilbury: Control Tutorials for MATLAB,

University of Michigan/ Prentice Hall 2000,

http://www.engin.umich.edu/group/ctm/

[5] Schmid, C.: “A remote laboratory using virtual reality on the Web”.

Simulation, 73 (1999), 13-21.

AUTHORS

H. Mann is with the Computing and Information

Centre, Czech Technical University, Zikova 4, CZ-166 35

Prague 6, Czech Republic (e-mail: mann@ vc.cvut.cz).

M. Sevcenko is Prof. Mann’s doctoral student (e-mail:

sevcenko@ vc.cvut.cz).

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