examples/controller/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_toolkit.hpp>
#include <acado_gnuplot.hpp>


int main( )
{
    USING_NAMESPACE_ACADO


    // INTRODUCE THE VARIABLES:
    // -------------------------
        DifferentialState xB; //Body Position
        DifferentialState xW; //Wheel Position
        DifferentialState vB; //Body Velocity
        DifferentialState vW; //Wheel Velocity

        Control F;
        Disturbance R;

        double mB = 350.0;
        double mW = 50.0;
        double kS = 20000.0;
        double kT = 200000.0;


    // DEFINE A DIFFERENTIAL EQUATION:
    // -------------------------------
    DifferentialEquation f;

        f << dot(xB) == vB;
        f << dot(xW) == vW;
        f << dot(vB) == ( -kS*xB + kS*xW + F ) / mB;
        f << dot(vW) == (  kS*xB - (kT+kS)*xW + kT*R - F ) / mW;


    // DEFINE LEAST SQUARE FUNCTION:
    // -----------------------------
    Function h;

    h << xB;
    h << xW;
        h << vB;
    h << vW;
        h << F;

    DMatrix Q(5,5); // LSQ coefficient matrix
        Q(0,0) = 10.0;
        Q(1,1) = 10.0;
        Q(2,2) = 1.0;
        Q(3,3) = 1.0;
        Q(4,4) = 1.0e-8;

    DVector r(5); // Reference
    r.setAll( 0.0 );


    // DEFINE AN OPTIMAL CONTROL PROBLEM:
    // ----------------------------------
    const double tStart = 0.0;
    const double tEnd   = 1.0;

    OCP ocp( tStart, tEnd, 20 );

    ocp.minimizeLSQ( Q, h, r );

        ocp.subjectTo( f );

        ocp.subjectTo( -200.0 <= F <= 200.0 );
        ocp.subjectTo( R == 0.0 );


    // SETTING UP THE REAL-TIME ALGORITHM:
    // -----------------------------------
        RealTimeAlgorithm alg( ocp,0.025 );
        alg.set( MAX_NUM_ITERATIONS, 1 );
        alg.set( PLOT_RESOLUTION, MEDIUM );

        GnuplotWindow window;
          window.addSubplot( xB, "Body Position [m]" );
          window.addSubplot( xW, "Wheel Position [m]" );
          window.addSubplot( vB, "Body Velocity [m/s]" );
          window.addSubplot( vW, "Wheel Velocity [m/s]" );
          window.addSubplot( F,  "Damping Force [N]" );
          window.addSubplot( R,  "Road Excitation [m]" );

        alg << window;


    // SETUP CONTROLLER AND PERFORM A STEP:
    // ------------------------------------
        StaticReferenceTrajectory zeroReference( "ref.txt" );

        Controller controller( alg,zeroReference );

        DVector y( 4 );
        y.setZero( );
        y(0) = 0.01;

        if (controller.init( 0.0,y ) != SUCCESSFUL_RETURN)
                exit( 1 );
        if (controller.step( 0.0,y ) != SUCCESSFUL_RETURN)
                exit( 1 );

    return EXIT_SUCCESS;
}