efficient modeling and simulation of multidisciplinary systems across the internet heřman mann...
TRANSCRIPT
![Page 1: Efficient Modeling and Simulation of Multidisciplinary Systems across the Internet Heřman Mann Computing and Information Centre Czech Technical University](https://reader038.vdocuments.mx/reader038/viewer/2022110323/56649d925503460f94a796ec/html5/thumbnails/1.jpg)
Efficient Modeling and Simulation of Multidisciplinary Systems
across the Internet
Heřman Mann
Computing and Information Centre
Czech Technical University in Prague
TUTORIAL
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Tutorial objectives
After attending this tutorial you should be able to:• understand the difference between various approaches to
modeling and their suitability to different tasks• be able to apply the concepts of multipole modeling in
different physical domains • be motivated to try the simulation software system DYNAST
freely accessible across the Internet• be aware of the importance of physical-level simulation for
reliable control design• be prepared to introduce a unified approach to engineering
dynamics at you school (if you are a teacher)• interested in visiting the DynLAB web-based course on
modeling and simulation (to be fully completed soon)
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Kernel engineering tools
Modeling = procedure to simplify investigation of their dynamic behavior
Simulation = imitation of dynamic behavior of real systems
Analysis = relating system behavior to a changing variable or parameter
Diagnostics = indicating the reason for a system failure
Why engineers need these tools?• to better understand behavior of existing dynamic systems• to predict, verify and optimize behavior of designed systems• to detect, localize and diagnose faults in engineering products
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Multidisciplinary approach
Contemporary engineering crosses borders between traditional disciplines:
• different physical domains– electrical, magnetic, mechanical, fluid, thermal, ...
• different levels of modeling abstraction– conceptual, functional, physical, virtual prototyping, (digital) control,
diagnossis, ...
• different levels of modeling idealization– (non)linear, time (in)variable, parameter (in)dependent, …
• different model descriptions– equations, transfer functions, block diagrams, multipoles, ...
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Efficiency of simulation
In the past:– efficiency of simulation was evaluated with regard to its demand of
computer time only
Nowadays:– the computer time is so inexpensive that the cost of simulation is
dominated by the cost of personnel qualified to be able • to prepare the input data• to supervise the computation• to interpret the results
Therefore: – efficient simulation software should provide
• automated equation formulation• robust computational algorithms• user-friendly interface
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Design procedure
• Design proceeds through several levels of abstraction– conceptual– functional (e.g., control design)– physical (e.g., real or virtual prototyping)– technological
• Different system descriptions are used– geometric (blue – topological (geometric dimensions of subsystems are not shown, only
their interactions) – behavioral (internal interactions of subsystems are not shown, only
their external behavior)
• Design proceeds through several levels of granularity (perpendicular to the design-space diagram)
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Design space
trajectory of ideal design procedure (real one in many loops)
blocks multipoles
design space
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Modeling & simulation procedure
1. System definition• system separation from its surroundings• system decomposition into subsystems• identification of subsystem energy interactions
2. Model development• subsystem abstraction and idealization• identification of subsystem parameters
3. Formulation of• equations for subsystems • equations for subsystem interactions• combined and reduced equations
4. Equation solution 5. Interpretation of the solution
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Simulation using Simulink
1. System definition• system separation from its surroundings• system decomposition into subsystems
2. Model development• subsystem abstraction and idealization• parameter identification
3. Formulation of• equations for subsystems • equations for subsystem interactions• combined and reduced equations
4. Composition of a block diagram 5. Block-diagram analysis 6. Interpretation of the solution
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Block Diagram Algebra
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Block diagram applications
Graphical representation of• causes-effects relations
– inputs: causes– outputs: effects
• explicit equations– inputs: independent variables– outputs: dependent variables
• control structures– inputs: excitations, disturbances– outputs: desired variables
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Copying lathe (1)
Geometric description
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Copying lathe (2)
Behavioral description (block diagram for control design)
master-shape waveform
workpiece-shape waveform
force exerted by cylinder
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Copying lathe (3)
Topological description (multipole diagram for physical design)
source of pressure
source of master- shape waveform r
cylinder mass
model of workpiece resistance
slide-bed friction
F
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Multipole diagrams
• can be set up based on mere inspection of the modeled real systems without any equation formulation or block-diagram construction
• equations underlying the system models can be not only solved, but also formed automatically by the computer
• they project geometric configuration of real dynamic systems onto their topological configuration
• they portray graphically energy interactions between subsystems in the systems
• they can be combined with block diagrams, which represent a special case of multipole diagrams)
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Multipole modeling
• Principles of multipole modeling
• Concept of across and through variables
• Postulates of continuity and compatibility
• Advantages of multipole modeling
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Investigation of dynamic behavior
Dynamic behavior of a dynamic system is governed • by the flow of energy and matter between subsystems of the
system and between the subsystems and the surroundings• by storing energy in the subsystems or releasing it later as
well as by changes from one form to another.
Therefore, before starting any dynamic investigation of a system we should clearly
• separate the system from its surroundings• decompose the system into its disjoint subsystems
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Multidisciplinary system (1)
Tachom eter
Busline
E lectronicam plifier
H ydraulicm otor
O utputsynchro
Inputsynchro
Compensatingnetwork
H ydraulicvalve
Load
D em odulator
G ear
C ontro l
Source ofpressure
Shaft
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Multipole models
Multipole model approximates subsystem mutual energy interactions assuming that
• the interactions take place just in a limited number of interaction sites formed by adjacent energy entries into the subsystems
• the energy flow through each such entry can be expressed by a product of two complementary power variables
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Tachom eter
Busline
E lectronicam plifier
H ydraulicm otor
O utputsynchro
Inputsynchro
Compensatingnetwork
H ydraulicvalve
Load
D em odulator
G ear
C ontro l
Source ofpressure
Shaft
Multidisciplinary system (2)
Subsystems are separated by energy boundaries, sites of energy interactions are denoted by small circles
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Multidisciplinary system (3)
Tachom eter
Bus
line
E lectron icam plifier
H ydraulicm otor
O utputsynchro
Inputsynchro
Compensatingnetwork
Hyd
raul
icva
lve
Load
D em odulatorG
ear
Source of
pressure
Shaft
Energy interactions between subsystems are characterized exclusively by energy flows through the sites of interactions at the energy boundaries
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Multidisciplinary system (4)
Tachom eter
Bus
line
E lectron icam plifier
H ydraulicm otor
O utputsynchro
Inputsynchro
Compensatingnetwork
Hyd
raul
icva
lve
Load
D em odulatorG
ear
Source of
pressure
Shaft
The energy boundaries are detached and the energy interactions areinterconnected with the energy entries of subsystems by ideal links
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Multipole constitutive relation
vB vC
vD
vE
A D
CB
EvA
iB iC
iD
iE
A D
CB
EiA
( )c( )b
A D
CB
E
( )a
5 - pole across variables through variables
Each multipole can be characterized by a constitutive relation between its across and through variables expressed by means of a combination of
• physical elements• blocks• equations• look-up tables
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Power variables
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Measurement of variables
Direct measurement of through variables requires including the measuring instrument between disconnected adjacent energy entries
Across variables are measured between distant energy entries without disconnecting them
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Postulate of Continuity
a
b
c
Through variables a, b, c :
a + b + c = 0
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Postulate of Compatibility
a
b
c
Across variables a, b, c :
a + b + c = 0
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Reference across-variables
Measurement of reference across variables
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Non-mechanical elements
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Simple electrical system
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Simple hydraulic system
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Mechanical elements
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Simple translational system
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Simple rotational system
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Cold rolling mill
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Unified approach to modeling
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Other approaches (1)
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Other approaches (2)
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Additional advantages
• multipole models can be developed once for the individual subsystems and stored to be used any time later
• this job can be done for different types of subsystems by specialists in the field
• submodels can be represented by different descriptions suiting best to the related engineering discipline or application
• submodel refinement or subsystem replacement can be taken into account without interfering with the rest of the system model
• mixed-level modeling is allowed
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Mechanical systems
• Translational systems
• Rotational systems
• Coupled mechanical systems– Rotary-to-rotary couplings– Rotary-to-linear couplings– Linear-to-linear couplings
• Planar systems
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Jumping ball
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Translatory systems
y
k dmg
yAAy
yd
yS yA
mA
( )a ( )b
yS
mg
yd
k
m
d
m2 m1FdF
v2 v1
l l
F
kR
kB0 l0
d2 d1
Fd
CAR 2CAR 1
m1 m2
lF
v1 v2( )c( )b( )a
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Quarter-car model
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Motor on vibration isolator
stop characteristic
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Impact of a long spring
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Torsional pendulums
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Weight-lifting mechanism
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Rotary-to-rotary coupling
B
A
A
B
n
Pure transformer
Coupling ratio:
Power consumption:
0 BBAA P
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Coupled gears
B
A
A
B
n
Coupling ratio:
Power consumption:
0 BBAA P
Pure transformer
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Gear trains (part 1)
Gear train Configuration n
External
spur gears
Internal
spur gears
Beveled
gear pair
b
a
r
r
b
a
r
r
b
a
r
r
Model
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Gear trains (part 2)
Gear train Configuration n
Planetgear
Skewgear pair
b
a
r
r
b
a
r
r
Model
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Belt-and-pulley or chain-and sprocket
ba rrn / barrn
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Gear train with backlash
Backlash characteristics
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Rotary-to-linear couplings
B
A
A
B
F
xn
Coupling ratio:
Power consumption:
0 BBAA xFP Pure transformer
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Rotary-to-linear convertion
mg
y
Ar
m, J
A
mgm
Ay An J
n r
( )a ( )b
n = - 1/r
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Rack-and-pinion gear-train
rn /1
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Movable rack-and-pinion assembly
rn /1
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Pulley or sprocket assembly
rn
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Lead screw assembly
Pn P … screw pitch
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Slider crank
20
2
0
)sin(
)sincos(sin
1
yrl
yrrr
x
n
A
AA
A
BA
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Linear-to-linear coupling
B
A
A
B
F
F
x
xn
Coupling ratio:
Power consumption:
0 BBAA xFxFP Pure transformer
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Levers and pulleys
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Lever systems
k
k
mg
m
CB
A
l
ymg
By
CyAy
mk
( )a ( )b
n kl
k
mg
m
A B C
D
B'
l1 l2
l3v t( )
y
Ayna Cy
By B'ym mg
( )a ( )b
nal /l1 2
nb
nb l /l2 3
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Planar oblique throw
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Central star and planet
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Math pendulums
n
m mmg
Ax
xAB yAB
Ay
Bx Byx
y mA
B
mg( )a ( )b
n yABxAB
xy
A
B
C
xB xC0
C2
n1
m1
m1
m2
m2m2g
m1g
n2
CxxC
xB
yB
yC
By
Cy
n 1 yBxB
n 2
y y BC x x BC
( )b( )a
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Planar systems
x
y
mC
mA
B
mg BxdC
xB
mC
( )a ( )b
n
mmg
Ax
xAB yAB
Ay
By
m n yABxAB
mg
m
AB
k
n yABxAB
( )a ( )b
m mg
yAB
xABnBxAy
kyAB
xAB
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Translatory joint fixed to frame
Multipole model
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Translatory joint between bodies
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Revolute joints
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Body with revolute joints
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Two-link planar robot
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Physical 2-pendulum with friction
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Truck with active damping
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Truck model
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Electrical & electronic systems
CMOS inverter
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Pulse-width modulator
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Astable multivibrator
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Three-phase thyristor rectifier
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Electro-mechanical systems
Conductor moving in a magnetic field
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Coils in a magnetic field
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ac rotational transducer
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Movable-core solenoid
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Permanent magnet DC machine
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Chopper-driven dc motor
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Movable-plate condenser
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Reluctance machine
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Three-phase stepping motor
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Electromagnetic relay
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Magnetic levitation of a ball
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Chopper-driven dc motor
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Fluid-power systems
Q
( ) ( )a b
Gf
Q
pB
Cf1 Cf2
Lf
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Valve for flow control
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Fluid-mechanical transducers
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Fluid-damped car suspension
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Two-stage relief valve
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Relief valve in a system
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Spool valves
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FPN simulation benchmark
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DYNAST software system
for efficient simulation of multidisciplinary engineering systems
freely accessible across the Internet at
http://virtual.cvut.cz/dyn/
DYNAST has been designed • for practicing engineers to enhance efficiency and quality of
their work• for engineering students to accelerate and deepen their
understanding of system dynamics• for remote engineering teams to support their collaboration
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DYNAST distributed simulation environment
Web browser
DYNAST Shellfor submitting diagrams or equations and for plotting
CORTONAfor 3D animation
of simulated systems
MATLABfor design of control for
simulated systems
Learning mng. systemfor course delivery
DYNAST Solverfor forming and solving
equations
DYNAST Publisherfor documenting simulation experiments & submodels
DYNAST Monitorfor assisting learners in
modelling and simulation
Internet
Client Server
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DYNAST Solver
provides the computation power for the DYNAST system.
It can• compute transient and steady-state (static) solution of
systems of nonlinear algebro-differential equations • formulate these equations for multipole diagrams that may be
combined with block diagrams and/or equations• compute Fourrier analysis of the periodic steady-state solution• linearize nonlinear system models and provide system
transfer functions and responses in a semisymbolic form• compute frequency-domain characteristics in different forms
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DYNAST Solver
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Semisymbolic analysis
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DYNAST Shell
provides a user-friendly working environment for DYNAST Solver.
Thanks to its wizard dialogs, users do not need to learn a simulation language.
DYNAST Shell allows for • submitting equations in textual and diagrams in graphical form• syntax analysis of the submitted problem for errors • processing the submitted problem by DYNAST Solver• plotting the resulting data in different graphical forms• creating graphical symbols and models for new components• processing of reports on simulation experiments and models• communication with the clients’ Matlab control-design toolset
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Submitting a component model
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DYNAST Shell -- symbol editor
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DYNAST Publisher
is a LaTeX-based documentation system installed on the server for automated publishing of – reports on simulation experiments and – descriptions of library submodels
Publisher 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 into PostScript, PDF and HTML formats.
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DYNAST Monitor
allows design managers or tutors to observe from any site on the Internet the data files and diagrams the users are submitting to DYNAST Solver from their client computers.
The supervisor can communicate with the users across the Internet and assist them in solving their problems.
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DYNAST in control design
functional level
physical level
Control synthesis
Control designverification
Controlledsystem
Controlobjectives
Plant to be controlled
Model reduction
Real-partsimplementation
MATLAB domain
DYNAST domain
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Modeling using MATLABExample of the paper-and-pencil procedure necessary for the equation formulation and their transformation before MATLAB can be used to compute the open-loop response:
D. Tilbury, B. Messner: Control Tutorials for Matlab at http://www.engin.umich.edu/group/ctm/
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Inverse pendulum experiment
Multipole model of the open loop in DYNAST working environment
pendulum model
sensor of d/dt
cart inertia
source of force F
cart friction
sensor of dx/dt integration of dx/dt sensor of x
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DYNAST as modelling toolbox for Matlab
Validation of the open-loop model in DYNAST
Export of open-loop transfer functions to MATLAB environment in M-file
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Analog PID control of inverse pendulum
Closed-loop model in DYNAST based on control design in MATLAB
Closed-loop verification in
DYNAST
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DYNAST & MATLAB
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Current control curriculum criticised
for
• exposing students to rigor math before motivating them by practical engineering issues
• presenting ‚textbook‘ problems carefully engineered to fit the ‚underlying‘ theory
• using computers to carry old exercises without exploiting them efficiently
Future Directions in Control Education, IEEE Control Systems, October 1999
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Considerations for control education
1. Automatic control education currently has a very narrow approach ...
2. It is necessary to attach greater importance to all the design cycle of a control system
3. Modelling and identification ... are a key factor for achieving a good design ...
S. Dormido Bencomo: Control Learning: Present and Future, IFAC Congress, Barcelona 2002
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DynLAB web-based courseon modeling and simulation
Geez, Joe, – now I wish I took that DynLAB course !
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EU project DynLAB
The goal of the project within the Leonardo da Vinci EU program
is to develop the
Course on modeling and simulation of controlled multidisciplinary systems
in a virtual lab
Project consortium:– Czech Technical University in Prague– Ruhr-Universität, Bochum– Institute of Technology Tallaght, Dublin– EAS, Fraunhofer Institut, Dresden – University of Sussex, Brighton
Project website: http://virtual.cvut.cz/dynlab/
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Innovative style of the course
• introducing learners to dynamics through simple examples to stimulate their interest before exposing them to rigor math
• exposing learners to a unified, systematic and efficient methodology for realistic modelling of multidisciplinary systems
• giving learners access to a powerful tutor-monitored simulation system across the Internet
• exploiting computers not only for equation solving, but also for their formulation to minimise learners’ distraction from dynamics
• giving learners a better ‘feel’ for the topic by problem graphical visualisation and interactive virtual experiments
• allowing different target groups to select an individual paths through the course both for self-study and remote tutoring
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Visualization of system dynamics
3D movable model multipole diagram robot-arm trajectoryvisualized by CORTONA set-up in DYNAST Shell simulated by DYNAST
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Learning modes in DynLAB
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Ball-and-beam virtual experiment