rocket.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 
00033 #include <acado_optimal_control.hpp>
00034 #include <acado_gnuplot.hpp>
00035 
00036 
00037 /* >>> start tutorial code >>> */
00038 int main( ){
00039 
00040     USING_NAMESPACE_ACADO
00041 
00042     // INTRODUCE THE VARIABLES:
00043     // -------------------------
00044     DifferentialState     v,s,m;
00045     Control               u    ;
00046     DifferentialEquation  f    ;
00047 
00048     const double t_start =  0.0;
00049     const double t_end   = 10.0;
00050 
00051     // DEFINE A DIFFERENTIAL EQUATION:
00052     // -------------------------------
00053 
00054     f << dot(s) == v;
00055     f << dot(v) == (u-0.02*v*v)/m;
00056     f << dot(m) == -0.01*u*u;
00057 
00058 
00059     // DEFINE AN OPTIMAL CONTROL PROBLEM:
00060     // ----------------------------------
00061     OCP ocp( t_start, t_end, 20 );
00062     ocp.minimizeLagrangeTerm( u*u );
00063     ocp.subjectTo( f );
00064 
00065         ocp.subjectTo( AT_START, s ==  0.0 );
00066     ocp.subjectTo( AT_START, v ==  0.0 );
00067     ocp.subjectTo( AT_START, m ==  1.0 );
00068     ocp.subjectTo( AT_END  , s == 10.0 );
00069     ocp.subjectTo( AT_END  , v ==  0.0 );
00070 
00071     ocp.subjectTo( -0.01 <= v <= 1.3 );
00072         
00073         ocp.subjectTo( u*u >= -1.0 );
00074 
00075 
00076     // DEFINE A PLOT WINDOW:
00077     // ---------------------
00078     GnuplotWindow window;
00079         window.addSubplot( s,"DifferentialState s" );
00080         window.addSubplot( v,"DifferentialState v" );
00081         window.addSubplot( m,"DifferentialState m" );
00082         window.addSubplot( u,"Control u" );
00083         window.addSubplot( PLOT_KKT_TOLERANCE,"KKT Tolerance" );
00084 //         window.addSubplot( 0.5 * m * v*v,"Kinetic Energy" );
00085 
00086 
00087     // DEFINE AN OPTIMIZATION ALGORITHM AND SOLVE THE OCP:
00088     // ---------------------------------------------------
00089     OptimizationAlgorithm algorithm(ocp);
00090 
00091 //      algorithm.set( INTEGRATOR_TYPE, INT_BDF );
00092 
00093 //     algorithm.set( INTEGRATOR_TOLERANCE, 1e-6 );
00094 //     algorithm.set( KKT_TOLERANCE, 1e-3 );
00095 
00096   //algorithm.set( DYNAMIC_SENSITIVITY,  FORWARD_SENSITIVITY );
00097 
00098 
00099 
00100     algorithm.set( HESSIAN_APPROXIMATION, EXACT_HESSIAN );
00101     algorithm.set( MAX_NUM_ITERATIONS, 20 );
00102     algorithm.set( KKT_TOLERANCE, 1e-10 );
00103 //      algorithm.set( MAX_NUM_INTEGRATOR_STEPS, 4 );
00104 
00105     algorithm << window;
00106     algorithm.solve();
00107 
00108 //      BlockMatrix sens;
00109 //      algorithm.getSensitivitiesX( sens );
00110 //      sens.print();
00111 
00112     return 0;
00113 }
00114 /* <<< end tutorial code <<< */
00115 
00116 
00117 
00118 //  algorithm.set( DISCRETIZATION_TYPE, MULTIPLE_SHOOTING );
00119 //  algorithm.set( DISCRETIZATION_TYPE, SINGLE_SHOOTING   );
00120 //
00121 //  algorithm.set( DYNAMIC_SENSITIVITY, FORWARD_SENSITIVITY );
00122 //  algorithm.set( DYNAMIC_SENSITIVITY, BACKWARD_SENSITIVITY );
00123 
00124 //  algorithm.set( INTEGRATOR_TYPE, INT_RK45 );
00125 //  algorithm.set( INTEGRATOR_TYPE, INT_RK78 );
00126 //  algorithm.set( INTEGRATOR_TYPE, INT_BDF );
00127 //
00128 //  algorithm.set( KKT_TOLERANCE, 1e-4 );
00129 //  algorithm.set( MAX_NUM_ITERATIONS, 20 );
00130 
00131 //  algorithm.set( PRINT_SCP_METHOD_PROFILE, YES );


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Sat Jun 8 2019 19:38:49