examples/code_generation/mpc_mhe/getting_started.cpp

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/*
 *    This file is part of ACADO Toolkit.
 *
 *    ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
 *    Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
 *    Milan Vukov, Rien Quirynen, KU Leuven.
 *    Developed within the Optimization in Engineering Center (OPTEC)
 *    under supervision of Moritz Diehl. All rights reserved.
 *
 *    ACADO Toolkit is free software; you can redistribute it and/or
 *    modify it under the terms of the GNU Lesser General Public
 *    License as published by the Free Software Foundation; either
 *    version 3 of the License, or (at your option) any later version.
 *
 *    ACADO Toolkit is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 *    Lesser General Public License for more details.
 *
 *    You should have received a copy of the GNU Lesser General Public
 *    License along with ACADO Toolkit; if not, write to the Free Software
 *    Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
 *
 */

#include <acado_code_generation.hpp>

int main( )
{
        USING_NAMESPACE_ACADO

        // Variables:
        DifferentialState   p    ;  // the trolley position
        DifferentialState   v    ;  // the trolley velocity 
        DifferentialState   phi  ;  // the excitation angle
        DifferentialState   omega;  // the angular velocity
        Control             a    ;  // the acc. of the trolley

        const double     g = 9.81;  // the gravitational constant 
        const double     b = 0.20;  // the friction coefficient

        // Model equations:
        DifferentialEquation f; 

        f << dot( p ) == v;
        f << dot( v ) == a;
        f << dot( phi ) == omega;
        f << dot( omega ) == -g * sin(phi) - a * cos(phi) - b * omega;

        // Reference functions and weighting matrices:
        Function h, hN;
        h << p << v << phi << omega << a;
        hN << p << v << phi << omega;

        // Provide defined weighting matrices:
        DMatrix W = eye<double>( h.getDim() );
        DMatrix WN = eye<double>( hN.getDim() );
        WN *= 5;

        // Or provide sparsity patterns for the weighting matrices
//      BMatrix W = eye<bool>( h.getDim() );
//      BMatrix WN = eye<bool>( hN.getDim() );

        //
        // Optimal Control Problem
        //
        OCP ocp(0.0, 3.0, 10);

        ocp.subjectTo( f );

        ocp.minimizeLSQ(W, h);
        ocp.minimizeLSQEndTerm(WN, hN);

        ocp.subjectTo( -1.0 <= a <= 1.0 );
        ocp.subjectTo( -0.5 <= v <= 1.5 );

        // Export the code:
        OCPexport mpc( ocp );

        mpc.set( HESSIAN_APPROXIMATION,       GAUSS_NEWTON    );
        mpc.set( DISCRETIZATION_TYPE,         SINGLE_SHOOTING );
        mpc.set( INTEGRATOR_TYPE,             INT_RK4         );
        mpc.set( NUM_INTEGRATOR_STEPS,        30              );

        mpc.set( QP_SOLVER,                   QP_QPOASES      );
//      mpc.set( HOTSTART_QP,                 YES             );
//      mpc.set( LEVENBERG_MARQUARDT,         1.0e-4          );
        mpc.set( GENERATE_TEST_FILE,          YES             );
        mpc.set( GENERATE_MAKE_FILE,          YES             );
        mpc.set( GENERATE_MATLAB_INTERFACE,   YES             );
        mpc.set( GENERATE_SIMULINK_INTERFACE, YES             );

//      mpc.set( USE_SINGLE_PRECISION,        YES             );

        if (mpc.exportCode( "getting_started_export" ) != SUCCESSFUL_RETURN)
                exit( EXIT_FAILURE );

        mpc.printDimensionsQP( );

        return EXIT_SUCCESS;
}