Difference between revisions of "Lotka Volterra fishing problem (JModelica)"

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(Created page with "This page contains the model formulation of the MIOCP Lotka Volterra fishing problem in JModelica format. == JModelica == The model for compilation with JModelica. <source...")
 
 
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This page contains the model formulation of the MIOCP Lotka Volterra fishing problem in JModelica format.
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This page contains the model formulation of the MIOCP [[Lotka Volterra fishing problem]] in JModelica format.
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The control function was relaxed, i.e. <math> u(t)  \in [0,1] </math>.
  
  
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x1(0)=0.7;
 
x1(0)=0.7;
 
   end lotka;
 
   end lotka;
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</source>
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The Python run file.
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<source lang="python">
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#Import the function for transfering a model to CasADiInterface
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from pyjmi import transfer_optimization_problem
  
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op=transfer_optimization_problem("lotka", "lotka.mop")
  
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res=op.optimize()
  
 
</source>
 
</source>
  
 
[[Category:JModelica]]
 
[[Category:JModelica]]

Latest revision as of 16:00, 19 January 2016

This page contains the model formulation of the MIOCP Lotka Volterra fishing problem in JModelica format.

The control function was relaxed, i.e. u(t) \in [0,1] .


JModelica

The model for compilation with JModelica. 

//--------------------------------------------------------------------
//Lotka Volterra Fishing Problem
// (c) Madeleine Schroeter
//--------------------------------------------------------------------
 
 
  optimization lotka(objective = cost(finalTime), startTime = 0, finalTime = 12)
    "Steady State Solution with u=0"
 
 
    Real cost(start=0, min=0, max=25) "Integrated Deviation";
 
    // Differential state variables
    Real x0(min=0, max=20.0) "Biomass of Prey";
    Real x1(min=0, max=20.0) "Biomass of Predator";
 
    // Control functions
    input Real u(free=true, min=0,max=1);
 
    constant Real ref0 = 1.0 "Steady State Prey";
    constant Real ref1 = 1.0 "Steady State Predator";
 
 
  equation
    der(x0) =  x0 - x0*x1 - 0.4*x0*u;
    der(x1) = -x1 + x0*x1 - 0.2*x1*u;
    der(cost) = (x0 - ref0)*(x0 - ref0) + (x1 - ref1)*(x1 - ref1); // Quadratic deviation
 
  constraint
 	x0(0)=0.5;
	x1(0)=0.7;
  end lotka;


The Python run file. 

#Import the function for transfering a model to CasADiInterface
from pyjmi import transfer_optimization_problem
 
op=transfer_optimization_problem("lotka", "lotka.mop")
 
res=op.optimize()
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