This procedure of algorithmically tweaking control inputs, running a forward dynamic simulation, then judging how well the model/simulation achieved some objective is called dynamic optimization. It is the way that biomechanists would prefer to study human movement (see Sky Higher and Miller, 2012 for just two of many examples) and is commonly viewed as the alternative to tracking simulations. The models used for studying such motions are complex, and so it takes a long time to run a dynamic optimization (days to weeks). For cat flipping, however, an optimization takes only an hour or two. Thus, it's an ideal problem for first learning dynamic optimization.

Table of Contents

Code Organization

First, we present an overview of the 2 source files we use for the optimization. The file optimize.cpp creates an executable that (mostly just) creates the necessary objects and runs the optimization. The code block below displays the four most important lines from this file, which show how we connect the various pieces of the optimization. Look at the next code block, a skeleton of the second file, FlippinFelinesOptimizerSystem.h. It is where we define the FlippinFelinesOptimizerTool and FlippinFelinesOptimizerSystem classes.

...

As you can see, our optimizer system must be a subclass of SimTK::OptimizerSystem. We override its objectiveFunc() function, which is then called by the SimTK::Optimizer as part of its algorithm. It's in objectiveFunc() that we (1) convert the optimizer's tweaks to optimization parameters into changes in the actuation control of the model, (2) run a forward dynamic simulation of the cat's fall, and (3) evaluate the simulation by assigning a value for our objective function, f, which is what we calledon the main Flippin' Felines page. See SimTK::OptimizerSystem reference and SimTK::Optimizer reference for more information.

A: optimize.cpp: the executable that ties the pieces together

Here, we show the complete optimize.cpp. The user must provide one command-line argument, which is the name of an XML file that defines a FlippinFelinesOptimizerTool object (we'll get to this in the next section). A serialization is a representation of an object or objects in data files (which, in our case, is a plain text file in XML format). See this page for more information about C++ command-line arguments.

...

Code Block

language	cpp
title	optimize.cpp

        // Initialize parameters for the optimization as those determined
        // by our OptimizerSystem.
        SimTK::Vector initParameters = sys.initialParameters();

	// And we're off! Running the optimization
        // --------------------------------------------------------------------
        try
        {
            double f = opt.optimize(initParameters);
            // Print the optimized model so we can explore the resulting motion.
            sys.printModel(name + "_optimized.osim");
            // Print the control splines so we can explore the resulting actuation.
            sys.printPrescribedControllerFunctionSet(
                    name + "_optimized_parameters.xml");
            
            // Print out the final value of the objective function.
            std::cout << "Done with " << name << "! f = " << f << std::endl;
        }
        catch (...)
        {
            // Print the last model/controls (not optimized) so we have something
            // to look at.
            sys.printModel(name + "_last.osim");
            sys.printPrescribedControllerFunctionSet(
                    name + "_last_parameters.xml");
            std::cout << "Exception thrown; optimization not achieved." << std::endl;
            // Don't want to give the appearance of normal operation.
            throw;
        }
    }
    else
    { // Too few/many inputs, etc.
        std::cout << "\nIncorrect input provided. "
            "Must specify the name of a FlippinFelinesOptimizerTool "
            "serialization (setup/input file).\n\nExamples:\n\t"
            "optimize optimize_input_template.xml\n" << std::endl;
        return 0;
    }
    return EXIT_SUCCESS;
};

B: FlippinFelinesOptimizerTool: optimization settings and serialization

Below is a skeleton of the FlippinFelinesOptimizerTool definition. There are three main parts to the definition; we'll address each individually. Although this code is not vital for setting up optimizations, we discuss it now since the rest of our code depends on it.

...

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

namespace OpenSim {

class FlippinFelinesOptimizerTool : public Object {

OpenSim_DECLARE_CONCRETE_OBJECT(FlippinFelinesOptimizerTool, Object);

public:

    // Declare properties of the class.
    // ------------------------------------------------------------------------
    // ********** PART B.1 **********

    // Constructors
    // ------------------------------------------------------------------------
    // ********** PART B.2 **********

private:

    void setNull() { }

    void constructProperties()
    {
	// ********** PART B.3 **********
    }
};

B.1: Declare properties of the class

We create member variables using the OpenSim_DECLARE_PROPERTY and OpenSim_DECLARE_OPTIONAL_PROPERTY macros, which are described here. For simplicity, we've omitted many of the properties we have in the actual source code. For a member variable to be de/serializable, it must be delcared via this macro. Some of the properties help with general setup (e.g., results_directory, model_filename), while others modify the computation of the objective function (e.g., desired configurations and weightings). The macros define getters/setters for us; we'll see the usage of the getters in other parts of the code.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

// General properties.
OpenSim_DECLARE_PROPERTY(results_directory, std::string,
        "Directory in which to save optimization log and results");
OpenSim_DECLARE_PROPERTY(model_filename, std::string,
        "Specifies path to model file, WITH .osim extension");
OpenSim_DECLARE_PROPERTY(num_optim_spline_points, int,
        "Number of points being optimized in each spline function. "
        "Constant across all splines. If an initial_parameters_filename is "
        "provided, the functions specified in that file must have the "
        "correct number of points. We do not error-check for this.");
// Properties related to the objective function.
OpenSim_DECLARE_PROPERTY(anterior_legs_down_weight, double,
        "Adds terms to the objective to minimize final value of "
        "(roll - Pi) and related speeds");
OpenSim_DECLARE_PROPERTY(posterior_legs_down_weight, double,
        "Adds terms to the objective to minimize final value of "
        "(twist - 0) and related speeds");
OpenSim_DECLARE_PROPERTY(sagittal_symmetry_weight, double,
        "Adds a term to the objective to minimize final value of "
        "(hunch + 2 * pitch)");
// Modifying the model before optimizing.
OpenSim_DECLARE_PROPERTY(use_coordinate_limit_forces, bool,
        "TRUE: use coordinate limit forces, "
        "FALSE: ignore coordinate limit forces");
// Setting initial parameters for the optimization.
OpenSim_DECLARE_OPTIONAL_PROPERTY(initial_parameters_filename, std::string,
        "File containing FunctionSet of SimmSpline's used to initialize "
        "optimization parameters. If not provided, initial parameters are "
        "all 0.0, and this element must be DELETED from the XML file "
        "(cannot just leave it blank). The name of each function must be "
        "identical to that of the actuator it is for. x values are ignored. "
        "The time values that are actually used in the simulation are "
        "equally spaced from t = 0 to t = 1.0 s, and there should be "
        "as many points in each function as given by the num_optim_spline_points "
        "property. y values should be nondimensional and between -1 and 1 "
        "(negative values normalized by minControl if minControl is "
        "negative; otherwise the value is normalized by maxControl). "
        "NOTE that the output optimized splines are NOT NONDIMENSIONAL. "
        "Be careful, we do not do any error checking.");

B.2: Constructors

Classes are serialized by calling their print() method. To be able to deserialize a file, the associated class must have a constructor that takes in a filename and passes it to the constructor of Object. The properties in the class are overwritten by the values in the serialization by calling the method Object::updateFromXMLDocument().

...

Before we can do that though, we need to construct the properties we've declared.

B.3: Constructing the properties we've declared

These constructProperty functions, generated by the macros in part B.1, require an initial/default value for the property.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

void constructProperties()
{
    constructProperty_results_directory("results");
    constructProperty_model_filename("flippinfelines_*FILL THIS IN*.osim");
    constructProperty_num_optim_spline_points(20);
    constructProperty_anterior_legs_down_weight(1.0);
    constructProperty_posterior_legs_down_weight(1.0);
    constructProperty_sagittal_symmetry_weight(1.0);
    constructProperty_use_coordinate_limit_forces(true);
    constructProperty_initial_parameters_filename("");
}

Sample Input

Below is what the XML serialization of this class looks like. This is exactly what we meant by the optimizer_setup_file.xml above. This is a simplified version of what's generated by the executable optimize_input_template, which is generated from optimize_input_template.cpp. We've given you this file, but do not discuss it in the tutorial.

Code Block

language	html/xml
title	optimize_setup_file.xml

<?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="30000">
    <FlippinFelinesOptimizerTool>
        <!--Directory in which to save optimization log and results-->
        <results_directory>results</results_directory>
        <!--Specifies path to model file, WITH .osim extension-->
        <model_filename>flippinfelines_*FILL THIS IN*.osim</model_filename>
        <!--Number of points being optimized in each spline function. Constant across all splines. If an initial_parameters_filename is provided, the functions specified in that file must have the correct number of points. We do not error-check for this.-->
        <num_optim_spline_points>20</num_optim_spline_points>
        <!--Adds terms to the objective to minimize final value of (roll - Pi) and related speeds-->
        <anterior_legs_down_weight>1</anterior_legs_down_weight>
        <!--Adds terms to the objective to minimize final value of (twist - 0) and related speeds-->
        <posterior_legs_down_weight>1</posterior_legs_down_weight>
        <!--Adds a term to the objective to minimize final value of (hunch + 2 * pitch)-->
        <sagittal_symmetry_weight>1</sagittal_symmetry_weight>
        <!--TRUE: use coordinate limit forces, FALSE: ignore coordinate limit forces-->
        <use_coordinate_limit_forces>true</use_coordinate_limit_forces>
        <!--File containing FunctionSet of SimmSpline's used to initialize optimization parameters. If not provided, initial parameters are all 0.0, and this element must be DELETED from the XML file (cannot just leave it blank). The name of each function must be identical to that of the actuator it is for. x values are ignored. The time values that are actually used in the simulation are equally spaced from t = 0 to t = 1.0 s, and there should be as many points in each function as given by the num_optim_spline_points property. y values should be nondimensional and between -1 and 1 (negative values normalized by minControl if minControl is negative; otherwise the value is normalized by maxControl). NOTE that the output optimized splines are NOT NONDIMENSIONAL. Be careful, we do not do any error checking.-->
        <initial_parameters_filename>initial_parameters.xml</initial_parameters_filename>
    </FlippinFelinesOptimizerTool>
</OpenSimDocument>

C: FlippinFelinesOptimizerSystem

Here's a skeleton of the FlippinFelinesOptimizerSystem class. The two functions that are the most important are the constructor and objectiveFunc(). We focus on these. The definition of an optimization problem consists primarily of (1) parameters/inputs, and (2) the objective function. The parts that deal with the parameters are C.2, C.3, C.4, and C.5. Parts C.6 and C.7 deal with the objective function.

...

Note that objectiveFunc() is necessarily a const function. This is because we don't want the state of the system changing as we optimize it. For example, we wouldn't want to change relative masses of the cat while trying to find the optimum control input; the solution would be a moving target.

C.1: Parse inputs & prepare output

Here's where we load the model. If the setup file specifies that we turn off the coordinate limit forces, we turn them off. We do this in the constructor because it's not something we would need to do during each call to the objective function.

...

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h
collapse	true

// Prepare output
// --------------------------------------------------------------------

// Create a results directory for all the output files.
#if defined(_WIN32)
    _mkdir(_name.c_str());
#else
    mkdir(_name.c_str(), 0777);
#endif

// Serialize what we just deserialized, in the results directory, to keep
// everything in one organized place.
_tool.print(_name + "/" + _name + "_setup.xml");

// Create a log for the objective function value and the separate terms
// that go into it.
_optLog.open((_name + "/" + _name + "_log.txt").c_str(), std::ofstream::out);
_optLog << "Flippin Felines optimization log: " << _name << std::endl;
time_t rawtime; time(&rawtime);
_optLog << ctime(&rawtime);
_optLog << "Model file name: " << _tool.get_model_filename() << std::endl;

C.2: Add a controller to the model to control its actuators

Right now, our models have CoordinateActuator's, but we have no way of controlling them. We must add a controller to the model to do this. In general, a controller can manage any number of the model's actuators. We choose to have one controller that controls all of the cat's actuators separately. More specifically, we want a controller that will specify beforehand what the actuator signal (torque, in our case) will be throughout the simulation (NOTE: one alternative would be to use a feedback law). Such a controller is a PrescribedController. With a PrescribedController, we specify for each actuator a function that is the torque that each CoordinateActuator applies as a function of simulation time. We choose to use splines (SimmSpline) as the functions.

...

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

// Compute the number of optimization parameters for the model.
_numActuators = _cat.getActuators().getSize();
int numParameters = _numActuators * _numOptimSplinePoints;
setNumParameters(numParameters);

// Size the vector of best-yet splines.
_splinesBestYet.resize(_numActuators);

// Add a PrescribedController to the model.
using OpenSim::PrescribedController;
PrescribedController * flipController = new PrescribedController();
flipController->setName("flip_controller");

// Set the PrescribedController to control all actuators.
flipController->setActuators(_cat.getActuators());

// Add the PrescribedController to the model.
_cat.addController(flipController);

// Create SimmSpline's for each actuator and push into vector.
double indexToTimeInSeconds =
    _duration / (double)(_numOptimSplinePoints - 1);
for (int i = 0; i < _numActuators; i++)
{
    // Create a spline function for the current actuator.
    _splines.push_back(new OpenSim::SimmSpline());
    // Name this spline with the name of the current actuator.
    _splines[i]->setName(_cat.getActuators().get(i).getName());
    // Add the correct number of points to this spline.
    for (int j = 0; j < _numOptimSplinePoints; j++)
    {
        // Scale the time points appropriately.
        double thisTime = (double)j * indexToTimeInSeconds;
        _splines[i]->addPoint(thisTime, 0.0);
    }
    // Tell the controller about this function.
    flipController->prescribeControlForActuator(i, _splines[i]);
}

C.3: Set parameter limits/bounds for the optimization

We tell the optimizer that there are bounds on the values of the parameters. We've chosen to nondimensionalize our parameters, so our bounds are simple. The OptimizerSystem::setParameterLimits() function is public, so this function might be called in optimize.cpp instead.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

// Set (nondimensional) parameter limits/bounds for the actuators.
SimTK::Vector lowerLimits(getNumParameters(), -1.0);
SimTK::Vector upperLimits(getNumParameters(), 1.0);
setParameterLimits(lowerLimits, upperLimits);

C.4: Initial parameters

We wanted to make it really easy for the user to specify initial parameters for the optimization. Since these parameters end up as Y-coordinates of the spline points, we thought that SimmSpline's would be the easiest form of input. However, we need the initial parameters as a Vector. So, we wrote a method to convert the Y-coordinates from a FunctionSet of SimmSpline's into a Vector.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h
collapse	true

SimTK::Vector initialParameters()
{
    SimTK::Vector initParams(getNumParameters(), 0.0);
    if (_tool.get_initial_parameters_filename() != "")
    { // A file is specified.
        // Deserialize the initial parameters XML file.
        OpenSim::FunctionSet initFcns(_tool.get_initial_parameters_filename());
        // Write a copy of the FunctionSet to the results directory.
        initFcns.print(_name + "/" + _name + "_initial_parameters.xml");
        OpenSim::Array<std::string> initNames;
        initFcns.getNames(initNames);
        
        // This loop is set up so that the initFcns do not need to be in the same
        // order as the optimization parameters. Also, the number of initFcns
        // specified by the initial parameters file can be greater than the
        // number of actuators in the model (assuming that the initial parameters
        // file AT LEAST contains the model's actuators).
        using OpenSim::SimmSpline;
        for (int iAct = 0; iAct < _numActuators; iAct++)
        {
            // Get index of the initFcn corresponding to the actuator.
            int iFcn = initFcns.getIndex(_cat.getActuators().get(iAct).getName());
            // Get access to the spline's methods.
            SimmSpline * fcn =
                dynamic_cast<SimmSpline *>(&initFcns.get(iFcn));
            for (int iPts = 0; iPts < _numOptimSplinePoints; iPts++)
            {
                // Find the right index in the optimization parameters.
                int paramIndex = iAct * _numOptimSplinePoints + iPts;
                // Transfer y value from input to initial parameters.
                initParams[paramIndex] = fcn->getY(iPts);
            }
        }
    }
    return initParams;
}

C.5: Unpack parameters into the model

At this point, the Optimizer has given us new parameters to try. We need to use these parameters to change the Y-values of the splines. We must first dimensionalize the parameters.

...

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

// Increment the count of calls to this function.
_objectiveCalls++;

// Unpack parameters into the model (i.e., update spline points)
// --------------------------------------------------------------------
for (int iAct = 0; iAct < _numActuators; iAct++)
{
    // Max/min for all spline points for the i-th actuator.
    double minControl = _cat.getActuators().get(iAct).getMinControl();
    double maxControl = _cat.getActuators().get(iAct).getMaxControl();

    // Visit each point in this spline.
    for (int iPts = 0; iPts < _numOptimSplinePoints; iPts++)
    {
        int paramIndex = iAct * _numOptimSplinePoints + iPts;

        // Dimensionalize the control value.
        double dimControlValue;
        if  (parameters[paramIndex] < 0 && minControl < 0)
        { // parameter and minControl are both neg; must negate again.
            dimControlValue = -minControl * parameters[paramIndex];
        }
        else
        {
            dimControlValue = maxControl * parameters[paramIndex];
        }

        // Edit the spline with the dimensional control value.
        _splines[iAct]->setY(iPts, dimControlValue);
    }
}

C.6: Run a forward dynamic simulation

Now that we've updated the controls, we can run a simulation and see how the cat ends up.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

SimTK::State& initState = _cat.initSystem();

// Construct an integrator.
SimTK::RungeKuttaMersonIntegrator integrator(_cat.getMultibodySystem());
integrator.setAccuracy(1.0e-6);

// Construct a manager to run the integration.
OpenSim::Manager manager(_cat, integrator);
_cat.getMultibodySystem().realize(initState, SimTK::Stage::Acceleration);

// Integrate from initial time to final time
manager.setInitialTime(0.0);
manager.setFinalTime(_duration);
manager.integrate(initState);

C.7: Construct the objective function value

Here's the brain of the optimization. We assign to f the value of the objective function. Here, we have 3 terms in our objective function, which are described on the main Flippin' Felines page. The source code contains a few more terms; we've cut them out for simplicity.

...

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

double anteriorLegsDownTerm(
        double roll, double rollRate, double rollAccel) const
{
    if (_tool.get_anterior_legs_down_weight() != 0.0)
    {
        return processObjTerm("anterior_legs_down", _tool.get_anterior_legs_down_weight(),
                pow(roll - SimTK::Pi, 2) + _relw * pow(rollRate, 2) + _relw * pow(rollAccel, 2));
    }
    return 0.0;
}
double posteriorLegsDownTerm(
        double twist, double twistRate, double twistAccel) const
{
    if (_tool.get_posterior_legs_down_weight() != 0.0)
    {
        return processObjTerm("posterior_legs_down", _tool.get_posterior_legs_down_weight(),
                pow(twist - 0.0, 2) + _relw * pow(twistRate, 2) + _relw * pow(twistAccel, 2));
    }
    return 0.0;
} 

double sagittalSymmetryTerm(
        double hunch, double pitch, double pitchRate, double pitchAccel) const
{
    if (_tool.get_sagittal_symmetry_weight() != 0.0)
    {
        return processObjTerm("sagittal_symmetry", _tool.get_sagittal_symmetry_weight(),
                pow(hunch + 2 * pitch, 2) + _relw * pow(pitchRate, 2) + _relw * pow(pitchAccel, 2));
    }
    return 0.0;
}

C.8: Update the log and outputs

We now return to objectiveFunc(), right after the code where we've assembled f. We update the log with the new objective function value and might print an updated model/controls.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h
collapse	true

_lastCallWasBestYet = _thisCallIsBestYet;
_thisCallIsBestYet = f <= _objectiveFcnValueBestYet;
if (_thisCallIsBestYet) _objectiveFcnValueBestYet = f;
if (_objectiveCalls % _outputPeriod == 0)
    _optLog << " objfcn " << f << " objfcn_best_yet " << _objectiveFcnValueBestYet << std::endl;
// If this is the best yet (i.e., smallest objective function value),
// save a copy of the splines.
if (_thisCallIsBestYet)
{
    for (unsigned int iFcn = 0; iFcn < _splinesBestYet.size(); iFcn++)
        _splinesBestYet[iFcn] = *_splines[iFcn];
}
// If this is worse, print out the best-yet splines and current model
// (NOTE: current model is NOT "best yet", but the idea is that it will
// be close). Also print out nondimensionalized splines so that they can
// be used directly as input for a subsequent optimization.
if (_lastCallWasBestYet && !_thisCallIsBestYet)
{
    printBestYetPrescribedControllerFunctionSet(
            _name + "_best_yet_parameters.xml");
    printBestYetPrescribedControllerFunctionSet(
            _name + "_best_yet_parameters_nomdim.xml", true);
    printModel(_name + "_best_yet.osim");
}
// Print out to the terminal/console every so often.
if (_objectiveCalls % _outputPeriod == 0)
    std::cout << "Objective call # " << _objectiveCalls << std::endl;
return 0;

C.9: Miscellaneous functions

Here are some functions that we use, but that aren't too important for learning how optimization works. They are used to print either the model or control functions.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h
collapse	true

/**
 * @brief Prints the current model to the given filename.  The file will
 * be located in the directory containing the log file for this
 * optimization run.
 * */
void printModel(std::string filename) const
{
    _cat.print(_name + "/" + filename);
}
/**
 * @brief Serializes the current set of functions used for the actuators.
 * The file will be located in the directory containing the log file
 * for this optimization run.
 * */
void printPrescribedControllerFunctionSet(std::string filename) const
{
    // Create the FunctionSet that we'll then serialize.
    OpenSim::FunctionSet fset;
    fset.setSize(_splines.size());
    for (unsigned int iFcn = 0; iFcn < _splines.size(); iFcn++)
    {
        fset.insert(iFcn, *_splines[iFcn]);
    }
    fset.print(_name + "/" + filename);
}
/**
 * @brief Serializes the set of functions associated with the best-yet
 * value of the objective function. The file will be located in the
 * directory containing the log file for this optimization run.
 *
 * @param (nondimensionalize) Divide spline values by minControl for the
 * actuator, if value and minControl are negative, and by maxControl, if
 * value is positive. Once nondimensionalized, this FunctionSet can be used
 * as initial parameters.
 * */
void printBestYetPrescribedControllerFunctionSet(std::string filename,
        bool nondimensionalize=false) const
{
    using OpenSim::SimmSpline;
    // Create the FunctionSet that we will then serialize.
    OpenSim::FunctionSet fset;
    fset.setSize(_splinesBestYet.size());
    for (unsigned int iFcn = 0; iFcn < _splinesBestYet.size(); iFcn++)
    {
        fset.insert(iFcn, _splinesBestYet[iFcn]);
        if (nondimensionalize)
        { // Go through each y-value and nondimensionalize based on its sign.
            // Max/min for all spline points for the i-th actuator.
            double minControl = _cat.getActuators().get(iFcn).getMinControl();
            double maxControl = _cat.getActuators().get(iFcn).getMaxControl();
            for (int iPts = 0; iPts < _numOptimSplinePoints; iPts++)
            {
                SimmSpline * fcn = dynamic_cast<SimmSpline *>(&fset.get(iFcn));
                double dimValue = fcn->getY(iPts);
                double nonDimValue = 0;
                if (dimValue < 0 && minControl < 0)
                {
                    nonDimValue = -dimValue / minControl;
                }
                else
                {
                    nonDimValue = dimValue / maxControl;
                }
                fcn->setY(iPts, nonDimValue);
            }
        }
    }
    fset.print(_name + "/" + filename);
}

C.10: Member variables

The mutable keyword lets us modify a member variable inside const member functions, such as objectiveFunc() above.

Code Block

language	cpp
title	FlippinFelinesOptimizerSystem.h

// Reference to the FlippinFelinesOptimizerTool.
OpenSim::FlippinFelinesOptimizerTool & _tool;

// Simulation runtime (seconds).
double _duration;

// Model containing the attributes described in this class' description
mutable OpenSim::Model _cat;

// Number of actuators in the model.
int _numActuators;

// Number of points used to define splines for actuator control.
int _numOptimSplinePoints;

// Vector of splines used in the PrescribedController.
std::vector<OpenSim::SimmSpline *> _splines;

// Vector of splines that gave the best objective value yet.
mutable std::vector<OpenSim::SimmSpline> _splinesBestYet;

// Number of calls to the objective function.
mutable int _objectiveCalls;

// Name of results directory.
std::string _name;

// Log for recording data during optimization
mutable std::ofstream _optLog;

// Period for how often objective terms are printed to the log.
static const int _outputPeriod = 100;

// Best (lowest) value of the objective function, for logging.
mutable double _objectiveFcnValueBestYet;

// To aid with conservative printing of best yet actuation:
mutable bool _lastCallWasBestYet;
mutable bool _thisCallIsBestYet;

Getting started with optimization and reproducing our results

We've given you some setup/input files for the optimization to get you started reproducing our results. Enter a terminal or command prompt, navigate to the directory containing the optimize executable and the counterrotation_setup.xml file. The following will start an optimization. It should take under 2 hours to complete.

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Remember that it's the setup file that refers to the initial parameters file. This second optimization should take about the same time to complete.

Enjoy playing around with the code. We hope you've learned something useful!

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Back: Part I: Cat Modeling

Home: BIOE-ME 485 Spring 2013

Versions Compared

Old Version 43

New Version Current

Key

Code Organization

A: optimize.cpp: the executable that ties the pieces together

B: FlippinFelinesOptimizerTool: optimization settings and serialization

B.1: Declare properties of the class

B.2: Constructors

B.3: Constructing the properties we've declared

Sample Input

C: FlippinFelinesOptimizerSystem

C.1: Parse inputs & prepare output

C.2: Add a controller to the model to control its actuators

C.3: Set parameter limits/bounds for the optimization

C.4: Initial parameters

C.5: Unpack parameters into the model

C.6: Run a forward dynamic simulation

C.7: Construct the objective function value

C.8: Update the log and outputs

C.9: Miscellaneous functions

C.10: Member variables

Getting started with optimization and reproducing our results

Page Comparison

Versions Compared

Old Version 43

New Version Current

Key

Code Organization

A: optimize.cpp: the executable that ties the pieces together

B: FlippinFelinesOptimizerTool: optimization settings and serialization

B.1: Declare properties of the class

B.2: Constructors

B.3: Constructing the properties we've declared

Sample Input

C: FlippinFelinesOptimizerSystem

C.1: Parse inputs & prepare output

C.2: Add a controller to the model to control its actuators

C.3: Set parameter limits/bounds for the optimization

C.4: Initial parameters

C.5: Unpack parameters into the model

C.6: Run a forward dynamic simulation

C.7: Construct the objective function value

C.8: Update the log and outputs

C.9: Miscellaneous functions

C.10: Member variables

Getting started with optimization and reproducing our results