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 run_tracking_experiment(useSaved=False, plotResults=False, saveResults=False):
import game
# Game frame rate
expStepTime = 1.0/frameRateExperiment
expNumSteps = int(float(finalTime)/expStepTime)
expNumFrames = expNumSteps + 1
if useSaved:
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 "useSaved is set to True, but no saved data is found in Resources/Temp.\nFirst use run_tracking_experiment to generate new data.\n"
else:
# Make forcing functions
frame_exp = range(0, expNumFrames, 1)
t_exp = [expStepTime*frame for frame in frame_exp]
print "Generating reference signal.\n"
r_exp = tools.generate_fmu_signal(referenceModel, moFile, t_exp, referenceParams, expStepTime, saveDir=os.environ['MANTRA_TEMP'], odeSolver=odeSolver, printVerbose=printVerbose)
print "Generating disturbance input.\n"
w_exp = tools.generate_fmu_signal(disturbanceModel, moFile, t_exp, disturbanceParams, expStepTime, saveDir=os.environ['MANTRA_TEMP'], odeSolver=odeSolver, printVerbose=printVerbose)
# Assign variables into 'game' module from TrackingTask
game.backgroundVisible = backgroundVisible
game.previewTime = previewTime
game.stepTime = expStepTime
game.frameRate = frameRateExperiment
game.frame_hist = frame_exp
game.odeSolver = odeSolver
game.t_hist = t_exp
game.r_hist = r_exp
game.w_hist = w_exp
game.u_hist = len(t_exp)*[0]
game.y_hist = len(t_exp)*[0]
game.u_current = 0 # persistent variable needed for keyboard input
# Run tracking experiment
(u_exp, y_exp) = game.run_tracking_experiment(elementModel=elementModel, modelicaFile=os.environ['MANTRA_MOFILE'])
# Save results
tools.write_data_csv(dirName=os.environ['MANTRA_TEMP'], fileName=taskName+'_exp.csv', dataCols=(t_exp, r_exp, y_exp, w_exp, u_exp))
if saveResults:
functionName = 'RunTrackingExperiment'
timeStamp = time.strftime("%Y.%m.%d-%H.%M.%S", time.localtime())
fileName = eval(saveFormat)
tools.write_data_csv(dirName=os.environ['MANTRA_DATA'], fileName=fileName+'.csv', dataCols=(t_exp, r_exp, y_exp, w_exp, u_exp))
if plotResults:
tools.plot_variable_trajectories(t_exp, ((r_exp, y_exp, 'Reference State', 'Measured State'), (w_exp, u_exp, 'Disturbance Input', 'Control Input')), "Experimental Tracking Task")
# 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()