parameter_estimation_algorithm.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/optimization_algorithm/parameter_estimation_algorithm.hpp>
00035 
00036 
00037 
00038 BEGIN_NAMESPACE_ACADO
00039 
00040 
00041 
00042 //
00043 // PUBLIC MEMBER FUNCTIONS:
00044 //
00045 
00046 
00047 ParameterEstimationAlgorithm::ParameterEstimationAlgorithm()
00048                              :OptimizationAlgorithm(){
00049 
00050     set( HESSIAN_APPROXIMATION, GAUSS_NEWTON );
00051 }
00052 
00053 
00054 ParameterEstimationAlgorithm::ParameterEstimationAlgorithm( const OCP& ocp_ )
00055                              :OptimizationAlgorithm( ocp_ ){
00056 
00057     set( HESSIAN_APPROXIMATION, GAUSS_NEWTON );
00058 }
00059 
00060 
00061 ParameterEstimationAlgorithm::ParameterEstimationAlgorithm( const ParameterEstimationAlgorithm& arg )
00062                              :OptimizationAlgorithm( arg ){
00063 
00064 
00065 }
00066 
00067 
00068 ParameterEstimationAlgorithm::~ParameterEstimationAlgorithm( ){
00069 
00070 }
00071 
00072 
00073 
00074 ParameterEstimationAlgorithm& ParameterEstimationAlgorithm::operator=( const ParameterEstimationAlgorithm& arg ){
00075 
00076     if( this != &arg ){
00077 
00078         OptimizationAlgorithm::operator=(arg);
00079     }
00080     return *this;
00081 }
00082 
00083 
00084 returnValue ParameterEstimationAlgorithm::getParameterVarianceCovariance( DMatrix &pVar ){
00085 
00086     DMatrix              tmp;
00087     returnValue returnvalue;
00088     int              offset;
00089     int                  np;
00090     int           run1,run2;
00091 
00092     returnvalue = getVarianceCovariance( tmp );
00093     if( returnvalue != SUCCESSFUL_RETURN ) return ACADOERROR(returnvalue);
00094 
00095     if( iter.p == 0 ){
00096         pVar.init(0,0);
00097         return SUCCESSFUL_RETURN;
00098     }
00099 
00100     offset = 0;
00101 
00102     if( iter.x  != 0 ) offset += iter.x->getNumValues();
00103     if( iter.xa != 0 ) offset += iter.xa->getNumValues();
00104 
00105     np = iter.p->getNumValues();
00106     pVar.init( np, np );
00107 
00108     for( run1 = 0; run1 < np; run1++ )
00109         for( run2 = 0; run2 < np; run2++ )
00110             pVar( run1, run2 ) = tmp( offset+run1, offset+run2 );
00111 
00112     return SUCCESSFUL_RETURN;
00113 }
00114 
00115 
00116 returnValue ParameterEstimationAlgorithm::getDifferentialStateVarianceCovariance( DMatrix &xVar ){
00117 
00118     return ACADOERROR(RET_NOT_IMPLEMENTED_YET);
00119 }
00120 
00121 
00122 returnValue ParameterEstimationAlgorithm::getAlgebraicStateVarianceCovariance( DMatrix &xaVar ){
00123 
00124     return ACADOERROR(RET_NOT_IMPLEMENTED_YET);
00125 }
00126 
00127 
00128 returnValue ParameterEstimationAlgorithm::getControlCovariance( DMatrix &uVar ){
00129 
00130     return ACADOERROR(RET_NOT_IMPLEMENTED_YET);
00131 }
00132 
00133 
00134 returnValue ParameterEstimationAlgorithm::getDistubanceVarianceCovariance( DMatrix &wVar ){
00135 
00136     return ACADOERROR(RET_NOT_IMPLEMENTED_YET);
00137 }
00138 
00139 
00140 returnValue ParameterEstimationAlgorithm::getVarianceCovariance( DMatrix &var ){
00141 
00142     if( nlpSolver == 0 ) return ACADOERROR( RET_MEMBER_NOT_INITIALISED );
00143     return nlpSolver->getVarianceCovariance( var );
00144 }
00145 
00146 
00147 
00148 
00149 //
00150 // PROTECTED MEMBER FUNCTIONS:
00151 //
00152 
00153 returnValue ParameterEstimationAlgorithm::initializeNlpSolver( const OCPiterate& _userInit )
00154 {
00155         return OptimizationAlgorithm::initializeNlpSolver( _userInit );
00156 }
00157 
00158 
00159 returnValue ParameterEstimationAlgorithm::initializeObjective(  Objective* F
00160                                                                                                                 )
00161 {
00162         return SUCCESSFUL_RETURN;
00163 }
00164 
00165 
00166 
00167 
00168 
00169 
00170 CLOSE_NAMESPACE_ACADO
00171 
00172 // end of file.


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Sat Jun 8 2019 19:38:15