catalyst_mixing_ennc.cpp
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1 /*
2  * This file is part of ACADO Toolkit.
3  *
4  * ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
5  * Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
6  * Milan Vukov, Rien Quirynen, KU Leuven.
7  * Developed within the Optimization in Engineering Center (OPTEC)
8  * under supervision of Moritz Diehl. All rights reserved.
9  *
10  * ACADO Toolkit is free software; you can redistribute it and/or
11  * modify it under the terms of the GNU Lesser General Public
12  * License as published by the Free Software Foundation; either
13  * version 3 of the License, or (at your option) any later version.
14  *
15  * ACADO Toolkit is distributed in the hope that it will be useful,
16  * but WITHOUT ANY WARRANTY; without even the implied warranty of
17  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18  * Lesser General Public License for more details.
19  *
20  * You should have received a copy of the GNU Lesser General Public
21  * License along with ACADO Toolkit; if not, write to the Free Software
22  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
23  *
24  */
25 
26 
45 // IMPLEMENTATION:
46 // ---------------
47 
49 #include <acado_gnuplot.hpp>
50 
51 
52 /* >>> start tutorial code >>> */
53 int main( ){
54 
56 
57  // INTRODUCE THE VARIABLES:
58  // -------------------------
59  DifferentialState x1,x2,x3;
60  Control u;
61 
62  DifferentialEquation f(0.0,1.0);
63 
64 
65  // DEFINE A DIFFERENTIAL EQUATION:
66  // -------------------------------
67  f << dot(x1) == -u*(x1-10.0*x2);
68  f << dot(x2) == u*(x1-10.0*x2)-(1.0-u)*x2;
69  f << dot(x3) == u/10.0;
70 
71 
72  // DEFINE AN OPTIMAL CONTROL PROBLEM:
73  // ----------------------------------
74  OCP ocp(0.0,1.0,25);
75  ocp.minimizeMayerTerm( 0, -(1.0-x1-x2));
76  ocp.minimizeMayerTerm( 1, x3 );
77 
78  ocp.subjectTo( f );
79 
80  ocp.subjectTo( AT_START, x1 == 1.0 );
81  ocp.subjectTo( AT_START, x2 == 0.0 );
82  ocp.subjectTo( AT_START, x3 == 0.0 );
83 
84  ocp.subjectTo( 0.0 <= x1 <= 1.0 );
85  ocp.subjectTo( 0.0 <= x2 <= 1.0 );
86  ocp.subjectTo( 0.0 <= x3 <= 1.0 );
87  ocp.subjectTo( 0.0 <= u <= 1.0 );
88 
89 
90  // DEFINE A MULTI-OBJECTIVE ALGORITHM AND SOLVE THE OCP:
91  // -----------------------------------------------------
92  MultiObjectiveAlgorithm algorithm(ocp);
93 
95  algorithm.set( PARETO_FRONT_DISCRETIZATION, 11 );
97  //algorithm.set( PARETO_FRONT_HOTSTART, BT_FALSE );
98  //algorithm.set( DISCRETIZATION_TYPE, SINGLE_SHOOTING );
99 
100  // Minimize individual objective function
101  algorithm.solveSingleObjective(0);
102 
103  // Minimize individual objective function
104  algorithm.solveSingleObjective(1);
105 
106  // Generate Pareto set
107  algorithm.solve();
108 
109  algorithm.getWeights("catatlyst_mixing_ennc_weights.txt");
110  algorithm.getAllDifferentialStates("catalyst_mixing_ennc_states.txt");
111  algorithm.getAllControls("catalyst_mixing_ennc_controls.txt");
112 
113  // GET THE RESULT FOR THE PARETO FRONT AND PLOT IT:
114  // ------------------------------------------------
115  VariablesGrid paretoFront;
116  algorithm.getParetoFront( paretoFront );
117 
118  GnuplotWindow window1;
119  window1.addSubplot( paretoFront, "Pareto Front", "Conversion","Catalyst", PM_POINTS );
120  window1.plot( );
121 
122 
123  // PRINT INFORMATION ABOUT THE ALGORITHM:
124  // --------------------------------------
125  algorithm.printInfo();
126 
127 
128  // SAVE INFORMATION:
129  // -----------------
130  paretoFront.print( "catalyst_mixing_ennc_pareto.txt" );
131 
132  return 0;
133 }
134 /* <<< end tutorial code <<< */
135 
returnValue print(std::ostream &stream=std::cout, const char *const name=DEFAULT_LABEL, const char *const startString=DEFAULT_START_STRING, const char *const endString=DEFAULT_END_STRING, uint width=DEFAULT_WIDTH, uint precision=DEFAULT_PRECISION, const char *const colSeparator=DEFAULT_COL_SEPARATOR, const char *const rowSeparator=DEFAULT_ROW_SEPARATOR) const
DMatrix getWeights() const
virtual returnValue plot(PlotFrequency _frequency=PLOT_IN_ANY_CASE)
#define USING_NAMESPACE_ACADO
Provides a time grid consisting of vector-valued optimization variables at each grid point...
returnValue printInfo()
returnValue subjectTo(const DifferentialEquation &differentialEquation_)
Definition: ocp.cpp:153
returnValue minimizeMayerTerm(const Expression &arg)
Definition: ocp.cpp:238
returnValue addSubplot(PlotWindowSubplot &_subplot)
returnValue set(OptionsName name, int value)
Definition: options.cpp:126
returnValue getAllControls(const char *fileName) const
int main()
returnValue getParetoFront(VariablesGrid &paretoFront) const
Data class for defining optimal control problems.
Definition: ocp.hpp:89
Expression dot(const Expression &arg)
virtual returnValue solveSingleObjective(const int &number)
User-interface to formulate and solve optimal control problems with multiple objectives.
returnValue getAllDifferentialStates(const char *fileName) const
Provides an interface to Gnuplot for plotting algorithmic outputs.
Allows to setup and evaluate differential equations (ODEs and DAEs) based on SymbolicExpressions.


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Mon Jun 10 2019 12:34:29