dae_optimization_tutorial.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 
27 
36 #include <acado_gnuplot.hpp>
37 
38 
39 int main( ){
40 
42 
43  // INTRODUCE THE VARIABLES:
44  // -------------------------
48  Control u;
50 
51  const double t_start = 0.0;
52  const double t_end = 10.0;
53 
54  // DEFINE A DIFFERENTIAL EQUATION:
55  // -------------------------------
56  f << dot(x) == -x + 0.5*x*x + u + 0.5*z;
57  f << dot(l) == x*x + 3.0*u*u ;
58  f << 0 == z + exp(z) - 1.0 + x ;
59 
60 
61  // DEFINE AN OPTIMAL CONTROL PROBLEM:
62  // ----------------------------------
63  OCP ocp( t_start, t_end, 10 );
64  ocp.minimizeMayerTerm( l );
65 
66  ocp.subjectTo( f );
67  ocp.subjectTo( AT_START, x == 1.0 );
68  ocp.subjectTo( AT_START, l == 0.0 );
69 
70  GnuplotWindow window;
71  window.addSubplot(x,"DIFFERENTIAL STATE x");
72  window.addSubplot(z,"ALGEBRAIC STATE z" );
73  window.addSubplot(u,"CONTROL u" );
74 
75 
76  // DEFINE AN OPTIMIZATION ALGORITHM AND SOLVE THE OCP:
77  // ----------------------------------------------------
78  OptimizationAlgorithm algorithm(ocp);
79 
80  algorithm.set( ABSOLUTE_TOLERANCE , 1.0e-7 );
81  algorithm.set( INTEGRATOR_TOLERANCE , 1.0e-7 );
83  //algorithm.set( GLOBALIZATION_STRATEGY, GS_FULLSTEP );
84 
85  algorithm << window;
86  algorithm.solve();
87 
88  return 0;
89 }
90 
91 
92 
User-interface to formulate and solve optimal control problems and static NLPs.
#define USING_NAMESPACE_ACADO
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
Data class for defining optimal control problems.
Definition: ocp.hpp:89
Expression dot(const Expression &arg)
const double t_end
const double t_start
IntermediateState exp(const Expression &arg)
Provides an interface to Gnuplot for plotting algorithmic outputs.
virtual returnValue solve()
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 12:34:32