nmpc_solver_setup.cpp

/*
 *    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
 *
 */

 /**
 *    \file   examples/code_generation/getting_started.cpp
 *    \author Hans Joachim Ferreau, Boris Houska
 *    \date   2011-2013
 */

#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( "OCPexport" ) != SUCCESSFUL_RETURN)
        exit( EXIT_FAILURE );

    mpc.printDimensionsQP( );

    return EXIT_SUCCESS;
}