simtk.org

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import org.opensim.modeling.*;
% Construct the MocoInverse tool.
inverse = MocoInverse();

% Construct a ModelProcessor and set it on the tool. The default
% muscles in the model are replaced with optimization-friendly
% DeGrooteFregly2016Muscles, and adjustments are made to the default muscle
% parameters.
modelProcessor = ModelProcessor('DDH_0Run01_postRRA.osim');
modelProcessor.append(ModOpAddExternalLoads('DDH_0Run01_RRA_GRF.xml'));

modelProcessor.append(ModOpReplaceMusclesWithDeGrooteFregly2016());
% Only valid for DeGrooteFregly2016Muscles.
modelProcessor.append(ModOpIgnorePassiveFiberForcesDGF());
% modelProcessor.append(ModOpIgnoreTendonCompliance());
modelProcessor.append(ModOpUseImplicitTendonComplianceDynamicsDGF());
% modelProcessor.append(ModOpTendonComplianceDynamicsModeDGF('opimplicit'));
% Only valid for DeGrooteFregly2016Muscles.
modelProcessor.append(ModOpScaleActiveFiberForceCurveWidthDGF(1.5));
modelProcessor.append(ModOpAddReserves(10, 100,true));

jointNames = StdVectorString();
jointNames.add('mtp_l');
jointNames.add('mtp_r');

modelProcessor.append(ModOpReplaceJointsWithWelds(jointNames));
inverse.setModel(modelProcessor);



% Construct a TableProcessor of the coordinate data and pass it to the
% inverse tool. TableProcessors can be used in the same way as
% ModelProcessors by appending TableOperators to modify the base table.
% A TableProcessor with no operators, as we have here, simply returns the
% base table.
inverse.setKinematics(TableProcessor('DDH_SS_postRRA_states.sto'));
% inverse.append_output_paths('.*fiber_length');
% Initial time, final time, and mesh interval.
inverse.set_initial_time(1.29);
inverse.set_final_time(2.01);
inverse.set_mesh_interval(0.02);

% By default, Moco gives an error if the kinematics contains extra columns.
% Here, we tell Moco to allow (and ignore) those extra columns.
inverse.set_kinematics_allow_extra_columns(true);
inverse.set_minimize_sum_squared_activations(true);
% constrains muscle residuals/error
inverse.set_constraint_tolerance(1e-05);
% when change from an iteration is smaller than tolerance, iterations terminated
inverse.set_convergence_tolerance(1e-04);

% solution = inverse.solve();
% solution.getMocoSolution().write('MocoInverse_0Run01_solution.sto');

% get MocoStudy
study = inverse.initialize();
problem = study.updProblem();

EMG = importdata('EMG.mat');
emgReference = osimTableFromStruct(EMG.DDH);

% Part 3b: Create a MocoControlTrackingGoal, set its weight, and provide
% the EMG data as the tracking reference. We also need to specify the 
% reference labels for the four muscles whose EMG we will track.
tracking = MocoControlTrackingGoal('emg_tracking');
tracking.setWeight(5);
tracking.setReference(TableProcessor(emgReference));
tracking.setReferenceLabel('/forceset/bflh140_r', 'RBFLH');
tracking.setReferenceLabel('/forceset/ercspn_r', 'RES');
tracking.setReferenceLabel('/forceset/gasmed_r', 'RGASMED');
tracking.setReferenceLabel('/forceset/glmax2_r', 'RGMAX');
tracking.setReferenceLabel('/forceset/glmed2_r', 'RGMED');
tracking.setReferenceLabel('/forceset/recfem_r', 'RRECFEM');
tracking.setReferenceLabel('/forceset/tfl_r', 'RTFL');
tracking.setReferenceLabel('/forceset/vaslat140_r', 'RVASLAT');


% Part 3c: The EMG signals in the tracking are all normalized to have
% a maximum value of 1, but the magnitudes of the excitations from the 
% effort minimization solution suggest that these signals should be
% rescaled. Use addScaleFactor() to add a MocoParameter to the problem that
% will scale the reference data for the muscles in the tracking cost.


% Part 3d: Add the tracking goal to the problem.
problem.addGoal(tracking)

% Part 3e: Update the MocoCasADiSolver with the updated MocoProblem using 
% resetProblem().
solver = MocoCasADiSolver.safeDownCast(study.updSolver());
solver.resetProblem(problem);

% Part 3f: Tell MocoCasADiSolver that the MocoParameters we added to the 
% problem via addScaleFactor() above do not require initSystem() calls on
% the model. This provides a large speed-up.
solver.set_parameters_require_initsystem(false);

% Solve the problem and write the solution to a Storage file.
solution = study.solve();
solution.write('trackingSolution.sto');

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solution.insertStatesTrajectory(kinematicsTable);

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study = inverse.initialize();
problem = study.updProblem();
model = problem.getModel();