scalar2_ws.cpp
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00001 /*
00002  *    This file is part of ACADO Toolkit.
00003  *
00004  *    ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
00005  *    Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
00006  *    Milan Vukov, Rien Quirynen, KU Leuven.
00007  *    Developed within the Optimization in Engineering Center (OPTEC)
00008  *    under supervision of Moritz Diehl. All rights reserved.
00009  *
00010  *    ACADO Toolkit is free software; you can redistribute it and/or
00011  *    modify it under the terms of the GNU Lesser General Public
00012  *    License as published by the Free Software Foundation; either
00013  *    version 3 of the License, or (at your option) any later version.
00014  *
00015  *    ACADO Toolkit is distributed in the hope that it will be useful,
00016  *    but WITHOUT ANY WARRANTY; without even the implied warranty of
00017  *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
00018  *    Lesser General Public License for more details.
00019  *
00020  *    You should have received a copy of the GNU Lesser General Public
00021  *    License along with ACADO Toolkit; if not, write to the Free Software
00022  *    Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
00023  *
00024  */
00025 
00026 
00034 #include <acado_optimal_control.hpp>
00035 #include <acado_gnuplot.hpp>
00036 
00037 
00038 /* >>> start tutorial code >>> */
00039 int main( ){
00040 
00041     USING_NAMESPACE_ACADO
00042 
00043 
00044     // INTRODUCE THE VARIABLES:
00045     // ------------------------
00046     Parameter y1,y2;
00047 
00048 
00049     // DEFINE AN OPTIMIZATION PROBLEM:
00050     // -------------------------------
00051     NLP nlp;
00052     nlp.minimize( 0, y1 );
00053     nlp.minimize( 1, y2 );
00054 
00055     nlp.subjectTo( 0.0 <= y1 <= 5.0 );
00056     nlp.subjectTo( 0.0 <= y2 <= 5.2 );
00057     nlp.subjectTo( 0.0 <= y2 - 5.0*exp(-y1) - 2.0*exp(-0.5*(y1-3.0)*(y1-3.0)) );
00058 
00059 
00060     // DEFINE A MULTI-OBJECTIVE ALGORITHM AND SOLVE THE NLP:
00061     // -----------------------------------------------------
00062     MultiObjectiveAlgorithm algorithm(nlp);
00063 
00064     algorithm.set( PARETO_FRONT_GENERATION, PFG_WEIGHTED_SUM );
00065     algorithm.set( PARETO_FRONT_DISCRETIZATION, 41 );
00066     algorithm.set( KKT_TOLERANCE, 1e-12 );
00067 
00068     // Generate Pareto set 
00069     algorithm.solve();
00070 
00071     algorithm.getWeights("scalar2_ws_weights.txt");
00072 
00073 
00074     // GET THE RESULT FOR THE PARETO FRONT AND PLOT IT:
00075     // ------------------------------------------------
00076     VariablesGrid paretoFront;
00077     algorithm.getParetoFront( paretoFront );
00078 
00079     GnuplotWindow window1;
00080     window1.addSubplot( paretoFront, "Pareto Front y1 vs y2", "y1","y2", PM_POINTS );
00081     window1.plot( );
00082 
00083     paretoFront.print();
00084 
00085 
00086     // FILTER THE PARETO FRONT AND PLOT IT:
00087     // ------------------------------------
00088     algorithm.getParetoFrontWithFilter( paretoFront );
00089 
00090     GnuplotWindow window2;
00091     window2.addSubplot( paretoFront, "Pareto Front (with filter) y1 vs y2", "y1","y2", PM_POINTS );
00092     window2.plot( );
00093 
00094     paretoFront.print();
00095 
00096 
00097     // PRINT INFORMATION ABOUT THE ALGORITHM:
00098     // --------------------------------------
00099     algorithm.printInfo();
00100 
00101     return 0;
00102 }
00103 /* <<< end tutorial code <<< */
00104 


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Thu Aug 27 2015 11:59:55