Rand Model Designer
Training for beginners I
HYBRID SYSTEMS
INMOTION. MADRID 2017
YURI SENICHENKOV
Event-driven dynamical systems:
Classical
A) continuous (differential equations)
and
B) discreet dynamical systems (difference equations)
Hybrid
C) Piecewise continuity (differential and equations difference equations)
Graphical Language
State Machines (StateFlow)
Behavior-Charts (Rand Model Designer)
Event-driven dynamical systems
State 1
State 2
Usual notation
State 1. State equation
d 2
g
d d
   sin( )  
;  ( 0)   0 ;
2
dt
l
m dt
Event 1. When
   p  ( p ) 2  0
Actions:
Event 2. When
d
dt
lnew : l  l p ;  new :  
   p  ( p ) 2  0
Actions:
lnew : l ;  new :  
 0
t 0
l
l  lp
l  lp
l
Name, Folder and Model type
Predefined Automaton
Two-state system
Local Activity
Equations
Variables
Final Automaton
Run
Experiment_1
Variable(T)=F(parameter)
Parameter dependence
Repot
2D-Animation
Control Panel
Active experiment
3D-Animation
composition of objects
Adding Line
Line + Sphere
Control Panel + 3D-imige
Time estimation
Time estimation Result
Tracing
Project files
_trace
Save model as a Class
Transform to compound
object
Model with three objects
Parameters for the object
New run
Back to hybrid system
The initial variant of model
Checking model
Home Work
Modify our two-state system in one-state system.