def __init__(self, reg, model, delay, **opt):
"""
Arguments:
reg : `Models.Block`
Controller block.
model : `Models.Block`
Model of a process/plant.
opt : `dict`
``Models.Block`` options.
"""
self.controller = reg
self.model = model
self.delay = Models.Delay(d=delay, **
opt) if delay is not None else None
super().__init__(in_len=1, output_no_mem=True, **opt)
def __init__(self, reg, model, delay, filter=None, **opt):
"""
Arguments:
---------
reg : `Models.Block`
Regulator represantation.
model : `Models.Block`
Model of a process.
delay : `int`
Dead time. Number of samples to be delayed.
filter : `Models.Block`, optional
Filtering block.
opt : `dict`, optional
`Model.Block` options.
"""
self.controller = reg
self.model = model
self.model_delay = Models.Delay(d=delay, **opt)
self.filter = filter
super().__init__(model='Smith-Predictor',
shape=(2, 1, 0),
model_func=None,
**opt)
"""Simulation parameters""" N = 100 # Simulation time's end dt = 0.01 # Sampling time max_len = int(N / dt) # Simulation's vectors' length time = np.arange(0, N, dt) # Time vector u = np.ones((1, max_len)) # Input vector u=1 input_on_time = 20 # Start time of input u[0, 0:int(input_on_time / dt)] = 0 # Input u = 0 if t < input_on_time """ Open loop - step response """ # Process P(s) = 12/(12s+1)e^-2 to state-space A, B, C, D = [[-1 / 12]], [[1]], [[1]], [[0]] d = int(2 / dt) Plant_openloop0 = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) Delay_openloop0 = Models.Delay(d=d, dt=dt, len=max_len) """ ****** *** IMC 1 - model with delay ****** """ # Process P(s) = 12/(12s+1)e^-2s to state-space Plant1 = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) Delay1 = Models.Delay(d=d, dt=dt, len=max_len) # Model Pm(s) = 12/(12s+1)e^-2s Plant_model1 = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) # Regulator Q(s) = 12s+1/6s+12 Ar1, Br1, Cr1, Dr1 = [[-2]], [[1]], [[-23 / 6]], [[2]] Reg1 = Models.StateSpace(ABCD=[Ar1, Br1, Cr1, Dr1], dt=dt, len=max_len) # IMC
"""Simulation parameters""" N = 50 # Simulation time's end dt = 0.1 # Sampling time max_len = int(N / dt) # Simulation's vectors' length time = np.arange(0, N, dt) # Time vector u = np.ones((1, max_len)) # Input vector u=1 v = -1*np.ones((1, max_len)) # Disturbance vector v=-1, for t>=20 v_start = int(20 / dt) v[0, :v_start] = 0 """Open loop - step response""" # Process model P(s) = 1/(1s+1)e^-5 to state-space A, B, C, D = [[-1]], [[1]], [[1]], [[0]] d = int(5 / dt) Plant_openloop = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) Delay_openloop = Models.Delay(d=d, dt=dt, len=max_len) """Smith Predictor""" # Process model P(s) = 1/(1s+1)e^-5 to state-space Plant = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) Delay = Models.Delay(d=d, dt=dt, len=max_len) # Model Pmo(s) = 1/(1s+1) Model = Models.StateSpace(ABCD=[A, B, C, D], dt=dt, len=max_len) # Regulator C(s) = (1s + 1)/(0.5s) Ar, Br, Cr, Dr = [[0]], [[1]], [[2]], [[2]] Reg = Models.StateSpace(ABCD=[Ar, Br, Cr, Dr], dt=dt, len=max_len) # Output memory Mem = Models.Memory(in_len=1, dt=dt, len=max_len) # SmithPredictor structure SmithPred = SP.SmithPredictor(reg=Reg, model=Model, delay=d, len=max_len)