def test_get_results_this_sample(self):
"""
Test that get_results_this_sample returns the optimization result for
this optimization.
"""
op = transfer_to_casadi_interface(
"CSTR.CSTR_MPC",
self.cstr_file_path,
compiler_options={"state_initial_equations": True})
op.set('_start_c', float(self.c_0_A))
op.set('_start_T', float(self.T_0_A))
# Set options collocation
n_e = 50
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['IPOPT_options']['print_level'] = 0
# Define some MPC-options
sample_period = 3
horizon = 50
seed = 7
cvc = {'T': 1e6}
# Define blocking factors
bl_list = [1] * horizon
factors = {'Tc': bl_list}
bf = BlockingFactors(factors=factors)
opt_opts['blocking_factors'] = bf
# Create MPC-object
MPC_object = MPC(op,
opt_opts,
sample_period,
horizon,
constr_viol_costs=cvc,
noise_seed=seed)
MPC_object.update_state()
u_k1 = MPC_object.sample()
result1 = MPC_object.get_results_this_sample()
N.testing.assert_equal(0, result1['time'][0])
N.testing.assert_equal(sample_period * horizon, result1['time'][-1])
MPC_object.update_state()
u_k2 = MPC_object.sample()
result2 = MPC_object.get_results_this_sample()
N.testing.assert_equal(sample_period, result2['time'][0])
N.testing.assert_equal(sample_period * (horizon + 1),
result2['time'][-1])
def test_du_bounds(self):
"""
Test with blocking_factor du_bounds.
"""
op = transfer_to_casadi_interface(
"CSTR.CSTR_MPC",
self.cstr_file_path,
compiler_options={"state_initial_equations": True})
op.set('_start_c', float(self.c_0_A))
op.set('_start_T', float(self.T_0_A))
# Set options collocation
n_e = 50
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['IPOPT_options']['print_level'] = 0
# Define some MPC-options
sample_period = 3
horizon = 50
seed = 7
# Define blocking factors
bl_list = [1] * horizon
factors = {'Tc': bl_list}
du_bounds = {'Tc': 5}
bf = BlockingFactors(factors=factors, du_bounds=du_bounds)
opt_opts['blocking_factors'] = bf
# Create MPC-object
MPC_object = MPC(op,
opt_opts,
sample_period,
horizon,
noise_seed=seed,
initial_guess='trajectory')
MPC_object.update_state()
u_k1 = MPC_object.sample()
res = MPC_object.get_results_this_sample()
Tc = res['Tc']
prev_value = Tc[0]
largest_delta = 0
for value in Tc:
delta = value - prev_value
if delta > largest_delta:
largest_delta = delta
prev_value = value
assert largest_delta < 5, "Value {} is not less than {}".format(
largest_delta, 5)
def test_auto_bl_factors(self):
"""
Test blocking factors generated in the mpc-class.
"""
op = transfer_to_casadi_interface(
"CSTR.CSTR_MPC",
self.cstr_file_path,
compiler_options={"state_initial_equations": True})
# Set options collocation
n_e = 50
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['IPOPT_options']['print_level'] = 0
# Define some MPC-options
sample_period = 3
horizon = 50
# Create MPC-object
MPC_object_auto = MPC(op, opt_opts, sample_period, horizon)
MPC_object_auto.update_state()
MPC_object_auto.sample()
res_auto = MPC_object_auto.get_results_this_sample()
opt_opts_auto = op.optimize_options()
opt_opts_auto['n_e'] = n_e
opt_opts_auto['IPOPT_options']['print_level'] = 0
bf_list = [1] * horizon
factors = {'Tc': bf_list}
bf = BlockingFactors(factors)
opt_opts_auto['blocking_factors'] = bf
MPC_object = MPC(op, opt_opts_auto, sample_period, horizon)
MPC_object.update_state()
MPC_object.sample()
res = MPC_object.get_results_this_sample()
# Assert that res_auto['Tc'] and res['Tc'] are equal
N.testing.assert_array_equal(res_auto['Tc'], res['Tc'])
def test_infeasible_return_input(self):
"""
Test that the input returned from an unsuccessful optimization is the
next input in the last successful optimization.
"""
op = transfer_to_casadi_interface(
"CSTR.CSTR_MPC",
self.cstr_file_path,
compiler_options={"state_initial_equations": True})
# Set options collocation
n_e = 50
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['IPOPT_options']['print_level'] = 0
# Define some MPC-options
sample_period = 3
horizon = 50
seed = 7
cvc = {'T': 1e6}
# Define blocking factors
bl_list = [1] * horizon
factors = {'Tc': bl_list}
bf = BlockingFactors(factors=factors)
opt_opts['blocking_factors'] = bf
# Create MPC-object
MPC_object = MPC(op,
opt_opts,
sample_period,
horizon,
constr_viol_costs=cvc,
noise_seed=seed,
create_comp_result=False,
initial_guess='trajectory')
# NOTE: THIS NOT WORKING WITH initial_guess='shift'!!
MPC_object.update_state({
'_start_c': 587.47543496,
'_start_T': 345.64619542
})
u_k1 = MPC_object.sample()
result1 = MPC_object.get_results_this_sample()
# Optimize with infeasible problem
MPC_object.update_state({'_start_c': 900, '_start_T': 400})
u_k2 = MPC_object.sample()
# Assert that problem was infeasible and that the returned input is
# the next input from the last succesful optimization
N.testing.assert_(
'Infeasible_Problem_Detected',
MPC_object.collocator.solver_object.getStat('return_status'))
N.testing.assert_almost_equal(u_k2[1](0)[0],
result1['Tc'][4],
decimal=10)
# Assert that the returned resultfile is that of the last succesful
# optimization
result2 = MPC_object.get_results_this_sample()
assert result1 == result2, "UNEQUAL VALUES. result1={}\nresult2={}".format(
result1, result2)
# Assert that problem was infeasible yet again and that the returned
# input is the next (third) input from the last succesful optimization
MPC_object.update_state({'_start_c': 900, '_start_T': 400})
u_k3 = MPC_object.sample()
N.testing.assert_(
'Infeasible_Problem_Detected',
MPC_object.collocator.solver_object.getStat('return_status'))
N.testing.assert_almost_equal(u_k3[1](0)[0],
result1['Tc'][7],
decimal=10)
class RealTimeMPCBase(RealTimeBase):
"""
A base class for performing real time MPC on a process. Note that
this is an abstract class; to use it, you need to extend it with
the functions send_control_signal and get_values.
"""
def __init__(self,
file_path,
opt_name,
dt,
t_hor,
t_final,
start_values,
par_values,
output_names,
input_names,
par_changes=ParameterChanges(),
mpc_options={},
constr_viol_costs={},
noise=0):
"""
Create a real time MPC object.
Parameters::
file_path --
The path of the .mop file containing the model to be used for
the MPC solver.
opt_name --
The name of the optimization in the file specified by file_path
to be used by the MPC solver.
dt --
The time to wait in between each sample.
t_hor --
The horizon time for the MPC solver. Must be an even multiple
of dt.
t_final --
The total time to run the real time MPC.
start_values --
A dictionary containing the initial state values for the process.
par_values --
A dictionary containing parameter values to be set in the model.
output_names --
A list of the names of all of the output variables used in the
model.
input_names --
A list of the names of all of the input variables used in the
model.
par_changes --
A ParameterChanges object containing parameter changes and the
times they should be applied.
Default: An empty ParameterChanges object
mpc_options --
A dictionary of options to be used for the MPC solver.
constr_viol_costs --
Constraint violation costs used by the MPC solver. See the
documentation of the MPC class for more information.
noise --
Standard deviation of the noise to add to the input signals.
Default: 0
"""
super(RealTimeMPCBase,
self).__init__(dt, t_final, start_values, output_names,
input_names, par_changes, noise)
horizon = int(t_hor / dt)
n_e = horizon
self._setup_MPC_solver(file_path, opt_name, dt, horizon, n_e,
par_values, constr_viol_costs, mpc_options)
self.range_ = []
for i, name in enumerate(self.inputs):
var = self.solver.op.getVariable(name)
self.range_.append(
(var.getMin().getValue(), var.getMax().getValue()))
def _setup_MPC_solver(self,
file_path,
opt_name,
dt,
horizon,
n_e,
par_values,
constr_viol_costs={},
mpc_options={}):
op = transfer_optimization_problem(
opt_name,
file_path,
compiler_options={'state_initial_equations': True})
op.set(par_values.keys(), par_values.values())
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['n_cp'] = 2
opt_opts['IPOPT_options']['tol'] = 1e-10
opt_opts['IPOPT_options']['print_time'] = False
if 'IPOPT_options' in mpc_options:
opt_opts['IPOPT_options'].update(mpc_options['IPOPT_options'])
for key in mpc_options:
if key != 'IPOPT_options':
opt_opts[key] = mpc_options[key]
self.solver = MPC(op,
opt_opts,
dt,
horizon,
constr_viol_costs=constr_viol_costs)
def enable_codegen(self, name=None):
"""
Enables use of generated C code for the MPC solver.
Generates and compiles code for the NLP, gradient of f, Jacobian of g,
and Hessian of the Lagrangian of g Function objects, and then replaces
the solver object in the solver's collocator with a new one that makes
use of the compiled functions as ExternalFunction objects.
Parameters::
name --
A string that if it is not None, loads existing files
nlp_[name].so, grad_f_[name].so, jac_g_[name].so and
hess_lag_[name].so as ExternalFunction objects to be used
by the solver rather than generating new code.
Default: None
"""
self.solver.collocator.enable_codegen(name)
def enable_integral_action(self, mu, M, error_names=None, u_e=None):
"""
Enables integral action for the inputs.
If integral action is enabled, in each step the input error is
calculated and used to update the estimation of the error. By
default, the input error is calculated as the matrix M times the
difference between the current state vector as predicted by the
solver in the last time step and as measured from the process
in the current time step; however, this can be changed by
overriding the estimate_input_error method.
The low-pass filter used to update the estimate is
[new estimate] = mu*[old estimate] + (1-mu)*[current estimate]
Parameters::
mu --
Controls the convergence rate of the error estimate.
See above.
M --
A matrix used for calculating the input error from the state
error. See above.
error_names --
A list containing the names of the model variables for
the input errors. If set to None, it is assumed be the
same as the list of input variables with the prefix '_e'
appended to each one.
Default: None
u_e --
A list containing a set of values to be applied as
stationary errors to the input signals. Used for
simulating a stationary error where there otherwise wouldn't
be one. If set to None, no stationary error is applied.
Default: None.
"""
self._ia = True
self.mu = mu
self.M = M
if error_names == None:
self.errors = [name + '_e' for name in self.inputs]
else:
self.errors = error_names
if u_e == None:
self.u_e = N.zeros(len(self.inputs))
else:
self.u_e = N.array(u_e)
self.u_e_e = N.zeros(len(self.inputs))
def run(self, save=False):
"""
Run the real time MPC controller defined by the object.
Parameters::
save --
Determines whether or not to save data after running. If set
to False, the same data can still be saved manually by using
the save_results function.
Default: False
Returns::
The results and statistics from the run.
"""
if self._already_run:
raise RuntimeError('run can only be called once')
self.e_e = []
n_outputs = len(self.outputs)
n_inputs = len(self.inputs)
x_k = self.start_values.copy()
x_k_last = x_k.copy()
time1 = time.clock()
time2 = time.time()
time3 = 0
for k in range(self.n_steps):
new_pars = self.par_changes.get_new_pars(k * self.dt)
if new_pars != None:
self.solver.op.set(new_pars.keys(), new_pars.values())
self.solver.update_state(x_k)
u_k = self.solver.sample()
u_k = self._apply_noise(u_k, std_dev=self.noise)
if self._ia:
u_k_e = self._apply_error(u_k)
if time3 != 0:
solve_time = time.time() - time3
self.solve_times.append(solve_time)
if solve_time > self.dt * 0.2:
print 'WARNING: Control signal late by', solve_time, 's'
if self._ia:
self.send_control_signal(u_k_e)
else:
self.send_control_signal(u_k)
if k == 0:
next_time = time.time() + self.dt
m_k = self.wait_and_get_measurements(next_time)
next_time = time.time() + self.dt
time3 = time.time()
x_k = self.estimate_states(m_k, x_k_last)
x_k_last = x_k.copy()
if self._ia:
self._update_error_estimate(x_k)
for i in range(n_outputs):
self.results[self.outputs[i]].append(x_k['_start_' +
self.outputs[i]])
for i in range(n_inputs):
self.results[self.inputs[i]].append(u_k[1](0)[i])
self.stats.append(self.solver.collocator.solver_object.getStats())
self.ptime = time.clock() - time1
self.rtime = time.time() - time2
print 'Processor time:', self.ptime, 's'
print 'Real time:', self.rtime, 's'
if save:
self.save_results()
self._already_run = True
return self.results, self.stats
def _apply_noise(self, u_k, std_dev=0.0):
if std_dev == 0:
return u_k
inputs = u_k[1](0)
for n in range(len(inputs)):
noise = N.random.normal(0.0, std_dev)
inputs[n] += noise
inputs[n] = max(self.range_[n][0], min(self.range_[n][1],
inputs[n]))
return (u_k[0], lambda t: inputs)
def _apply_error(self, u_k):
u_k_e = []
for i in range(len(self.errors)):
u_k_e.append(
max(self.range_[i][0],
min(self.range_[i][1], u_k[1](0)[i] + self.u_e[i])))
return (u_k[0], lambda t: N.array(u_k_e))
def _update_error_estimate(self, x_k):
e_k = self._calculate_error(x_k)
u_e_e_next = self.estimate_input_error(e_k)
self.u_e_e = (1 - self.mu) * self.u_e_e + self.mu * (u_e_e_next +
self.u_e_e)
for i in range(len(self.errors)):
self.solver.set(self.errors[i], self.u_e_e[i])
self.e_e.append(self.u_e_e)
def estimate_input_error(self, e_k):
"""
Estimates the input error given the state error.
Parameters::
e_k --
The state error vector.
Returns::
The input error vector.
"""
return self.M.dot(e_k)
def _calculate_error(self, x_k):
x_k_a = N.array([x_k['_start_' + name] for name in self.outputs])
res = self.solver.get_results_this_sample()
x_k_e = N.array(
[res[name][self.solver.options['n_cp']] for name in self.outputs])
return x_k_a - x_k_e
def print_stats(self):
"""
Print statistics from the run.
The times printed are the sums of the corresponding
statistics from the NLP solver over all time steps.
"""
if not self._already_run:
raise RuntimeError(
'Stats can only be printed after run() has been called')
stat_names = [
't_callback_fun', 't_callback_prepare', 't_eval_f', 't_eval_g',
't_eval_grad_f', 't_eval_h', 't_eval_jac_g', 't_mainloop'
]
total_times = {}
for name in stat_names:
total_times[name] = 0.
for stat in self.stats:
for name in stat_names:
total_times[name] += stat[name]
t_total = total_times['t_mainloop']
print 'Total times:'
for name in stat_names:
print("%19s: %6.4f s (%7.3f%%)" %
(name, total_times[name], total_times[name] / t_total * 100))
def save_results(self, filename=None):
"""
Pickle and save data from the run to a file name either passed
as an argument or entered by the user. If an empty string is
entered as the file name, no data is saved.
Parameters::
filename --
The file name to save as. If it is not provided, the
user will be prompted to input it.
Default: None
"""
if not self._already_run:
raise RuntimeError(
'Results can only be saved after run() has been called')
result_dict = {}
result_dict['results'] = self.results
result_dict['stats'] = self.stats
result_dict['ptime'] = self.ptime
result_dict['rtime'] = self.rtime
result_dict['noise'] = self.noise
result_dict['late_times'] = self.late_times
result_dict['wait_times'] = self.wait_times
result_dict['solve_times'] = self.solve_times
save_to_file(result_dict, filename)
def test_du_quad_pen(self):
"""
Test with blocking_factor du_quad_pen.
"""
op = transfer_to_casadi_interface(
"CSTR.CSTR_MPC",
self.cstr_file_path,
compiler_options={"state_initial_equations": True})
op.set('_start_c', float(self.c_0_A))
op.set('_start_T', float(self.T_0_A))
# Set options collocation
n_e = 50
opt_opts = op.optimize_options()
opt_opts['n_e'] = n_e
opt_opts['IPOPT_options']['print_level'] = 0
# Define some MPC-options
sample_period = 3
horizon = 50
seed = 7
# Define blocking factors
bl_list = [1] * horizon
factors = {'Tc': bl_list}
bf = BlockingFactors(factors=factors)
opt_opts['blocking_factors'] = bf
# Create MPC-object without du_quad_pen
MPC_object = MPC(op,
opt_opts,
sample_period,
horizon,
noise_seed=seed,
initial_guess='trajectory')
MPC_object.update_state()
MPC_object.sample()
res = MPC_object.get_results_this_sample()
# Create MPC-object with du_quad_pen
opt_opts_quad = op.optimize_options()
opt_opts_quad['n_e'] = n_e
opt_opts_quad['IPOPT_options']['print_level'] = 0
bf_list = [1] * horizon
factors = {'Tc': bf_list}
du_quad_pen = {'Tc': 100}
bf = BlockingFactors(factors, du_quad_pen=du_quad_pen)
opt_opts_quad['blocking_factors'] = bf
MPC_object_quad = MPC(op,
opt_opts_quad,
sample_period,
horizon,
noise_seed=seed,
initial_guess='trajectory')
MPC_object_quad.update_state()
MPC_object_quad.sample()
res_quad = MPC_object_quad.get_results_this_sample()
Tc = res['Tc']
prev_value = Tc[0]
largest_delta = 0
for value in Tc:
delta = value - prev_value
if delta > largest_delta:
largest_delta = delta
prev_value = value
Tc = res_quad['Tc']
prev_value = Tc[0]
largest_delta_quad = 0
for value in Tc:
delta = value - prev_value
if delta > largest_delta_quad:
largest_delta_quad = delta
prev_value = value
N.testing.assert_(largest_delta_quad < largest_delta)
