def tune_controller_experiment(useSaved=False, plotResults=False, saveResults=False):
functionName = 'TuneControllerExperiment'
# Allow user to select parameters to tune
params2tune = tools.select_tuned_parameters(controllerParams, controllerStr, tuneAnyParam) # returns list of param names to tune
# Run experiment if not using previously saved data
if os.path.isfile(os.path.join(os.environ['MANTRA_TEMP'], taskName+'_exp.csv')):
(t_exp, r_exp, y_exp, w_exp, u_exp) = tools.read_data_csv(dirName=os.environ['MANTRA_TEMP'], fileName=taskName+'_exp.csv')
else:
print "No saved data is found in Resources/Temp.\nFirst use run_tracking_experiment to generate new data.\n"
# Assemble arguments and call function
inputData = (t_exp, r_exp, y_exp, w_exp, u_exp)
taskInfo = (controllerName, taskModel, os.environ['MANTRA_MOFILE'])
controllerInfo = (controllerParams, params2tune)
controllerParams_optimal = tools.tune_manual_controller(taskInfo, controllerInfo, saveDir=os.environ['MANTRA_TEMP'], inputData=inputData, printVerbose=printVerbose)
if plotResults:
(t_sim, r_sim, y_sim, w_sim, u_sim) = tools.read_data_csv(dirName=os.environ['MANTRA_TEMP'], fileName=taskName+'_sim.csv')
(t_exp, r_exp, y_exp, w_exp, u_exp) = tools.read_data_csv(dirName=os.environ['MANTRA_TEMP'], fileName=taskName+'_exp.csv')
y_sim_interp = numpy.interp(t_exp, t_sim, y_sim)
u_sim_interp = numpy.interp(t_exp, t_sim, u_sim)
tools.plot_variable_trajectories(t_exp, ((r_exp, y_exp, y_sim_interp, 'Reference State', 'Experimental Measured State', 'Simulated Measured State'), (w_exp, u_exp, u_sim_interp, 'Disturbance Input', 'Experimental Control Input', 'Simulated Control Input')), "Experimental vs. Simulated Controller")
print "Controller parameters chosen:"
print params2tune
print "Optimal parameter values:"
print controllerParams_optimal
# Stop OMC Server
fmi.stop_openmodelica_server()
def tune_controller_simulation(plotResults=False, saveResults=False):
functionName = 'TuneControllerSimulation'
# Allow user to select parameters to tune
params2tune = tools.select_tuned_parameters(controllerParams, controllerStr, tuneAnyParam) # returns list of param names to tune
# Assemble arguments and call function
taskInfo = (controllerName, taskModel, os.environ['MANTRA_MOFILE'])
controllerInfo = (controllerParams, params2tune)
controllerParams_optimal = tools.tune_manual_controller(taskInfo, controllerInfo, saveDir=os.environ['MANTRA_TEMP'], inputData=False, printVerbose=printVerbose)
if plotResults:
(t_sim, r_sim, y_sim, w_sim, u_sim) = tools.read_data_csv(dirName=os.environ['MANTRA_TEMP'], fileName=taskName+'_sim.csv')
tools.plot_variable_trajectories(t_sim, ((r_sim, y_sim, 'Reference State', 'Measured State'), (w_sim, u_sim, 'Disturbance Input', 'Control Input')), "Simulated Tracking Task")
print "Controller parameters chosen:"
print params2tune
print "Optimal parameter values:"
print controllerParams_optimal
# Stop OMC Server
fmi.stop_openmodelica_server()
