Difference between revisions of "Particle steering problem (TACO)"

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(Particle steering problem (TACO))
 
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This page contains a model of the [[Particle steering problem]] in [http://www.ampl.org AMPL] format, making use of the TACO toolkit for AMPL control optimization extensions. The original model using a collocation formulation can be found in the [http://www.mcs.anl.gov/~more/cops/ COPS library].
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This page contains a model of the [[Particle steering problem]] in [http://www.ampl.org AMPL] format, making use of the TACO toolkit for AMPL control optimization extensions. This classical problem can e.g. be found in <bib id="Bryson1975" />. The original model using a collocation formulation can be found in the [http://www.mcs.anl.gov/~more/cops/ COPS library].
 
Note that you will need to include a generic [[support AMPL files|AMPL/TACO support file]], OptimalControl.mod.
 
Note that you will need to include a generic [[support AMPL files|AMPL/TACO support file]], OptimalControl.mod.
 
To solve this model, you require an optimal control or NLP code that uses the TACO toolkit to support the AMPL optimal control extensions.
 
To solve this model, you require an optimal control or NLP code that uses the TACO toolkit to support the AMPL optimal control extensions.
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let u.type := "u1";
 
let u.type := "u1";
  
minimize time:  
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minimize time: eval(t,tf);
eval(t,tf);
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subject to
 
subject to
  
dy1: diff (y[1],t) = y[2];
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dy1: diff (y[1],t) = y[2];
dy2: diff (y[2],t) = a*cos(u);
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dy2: diff (y[2],t) = a*cos(u);
dy3: diff (y[3],t) = y[4];
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dy3: diff (y[3],t) = y[4];
dy4: diff (y[4],t) = a*sin(u);
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dy4: diff (y[4],t) = a*sin(u);
  
 
ivc{i in 1..4}: eval(y[i],0) = 0;
 
ivc{i in 1..4}: eval(y[i],0) = 0;
  
by1d: eval(y[2],tf) = 45;
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by1d: eval(y[2],tf) = 45;
by2: eval(y[3],tf) = 5;
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by2: eval(y[3],tf) = 5;
by2d: eval(y[4],tf) = 0;
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by2d: eval(y[4],tf) = 0;
  
 
option solver ...;
 
option solver ...;
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* Mathematical notation at [[Particle steering problem]]
 
* Mathematical notation at [[Particle steering problem]]
 
* [[:Category:AMPL | AMPL]] (using a fixed discretization) at the [http://www.mcs.anl.gov/~more/cops/ COPS library]
 
* [[:Category:AMPL | AMPL]] (using a fixed discretization) at the [http://www.mcs.anl.gov/~more/cops/ COPS library]
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== References ==
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<biblist />
 
   
 
   
 
[[Category:AMPL/TACO]]
 
[[Category:AMPL/TACO]]

Latest revision as of 21:32, 30 December 2015

This page contains a model of the Particle steering problem in AMPL format, making use of the TACO toolkit for AMPL control optimization extensions. This classical problem can e.g. be found in [Bryson1975]Address: New York
Author: Bryson, A.E.; Ho, Y.-C.
Publisher: Wiley
Title: Applied Optimal Control
Year: 1975
Link to Google Scholar
. The original model using a collocation formulation can be found in the COPS library. Note that you will need to include a generic AMPL/TACO support file, OptimalControl.mod. To solve this model, you require an optimal control or NLP code that uses the TACO toolkit to support the AMPL optimal control extensions.

AMPL

This is the source file particle_taco.mod

# ----------------------------------------------------------------
# Particle steering problem using AMPL and TACO
# (c) Christian Kirches, Sven Leyffer
#
# Source: COPS 3.1 collocation formulation - March 2004
# ----------------------------------------------------------------
include OptimalControl.mod;
 
param Pi := 3.14159265358979;
param a := 100.0;
 
var t;
var tf := 1, >= 0, <=10;
var y{1..4};
var u >= -Pi/2, <= +Pi/2;
let u.type := "u1";
 
minimize time: eval(t,tf);
 
subject to
 
dy1: diff (y[1],t) = y[2];
dy2: diff (y[2],t) = a*cos(u);
dy3: diff (y[3],t) = y[4];
dy4: diff (y[4],t) = a*sin(u);
 
ivc{i in 1..4}: eval(y[i],0) = 0;
 
by1d: eval(y[2],tf) = 45;
by2:  eval(y[3],tf) = 5;
by2d: eval(y[4],tf) = 0;
 
option solver ...;
 
solve;

Other Descriptions

Other descriptions of this problem are available in

References

There were no citations found in the article.