def __init__(self,
Kp=1,
Ti=0,
Td=0,
dt=1,
N=float('inf'),
alpha=None,
beta=None,
**opt):
"""
Arguments:
----------
Kp : `float`
Proportional gain.
Default ``Kp``=1.
Ti : `float`
Integral time constant.
Default ``Ti``=0.
Td : `float`
Derivative time constant.
Default ``Td``=0.
dt : `float`
Sampling period.
Default ``dt``=1
N : `float`
Derivative filter coefficient
Default ``N``=float('inf')
alpha : `float`
Alpha coefficient for two-degree of freedom controller.
Default ``alpha``=None
beta : `float`
Alpha coefficient for two-degree of freedom controller.
Default ``alpha``=None
"""
self.P_block = Models.Gain(Kp=Kp, dt=dt, **opt) if Kp else None
self.I_block = Models.Integral(Ki=Kp /
Ti, dt=dt, **opt) if Ti else None
self.D_block = None
self.P2_block = None
self.D2_block = None
if Td:
self.D_block = Models.Derivative(Kd=Td * Kp, dt=dt, **opt) if N == float('inf') \
else Models.StateSpace(ABCD=[[[-N / Td]], [[1]], [[- N ** 2 / Td]], [[N]]], dt=dt, K=Kp, **opt)
if alpha: self.P2_block = Models.Gain(Kp=Kp * alpha, **opt)
if beta:
self.D2_block = Models.Derivative(Kd=Kp * Td * beta, dt=dt, **opt) if N == float('inf') \
else Models.StateSpace(ABCD=[[[-N / Td]], [[1]], [[- N ** 2 / Td]], [[N]]], dt=dt, K=Kp * beta, **opt)
super().__init__(in_len=1, name='PID_Parallel', **opt)
def __init__(self, Kp=0, Ti=0, Td=0, dt=1, N=float('inf'), **opt):
"""
Arguments:
----------
Kp : `float`
Proportional gain.
Default ``Kp``=1.
Ti : `float`
Integral time constant.
Default ``Ti``=0.
Td : `float`
Derivative time constant.
Default ``Td``=0.
dt : `float`
Sampling period.
Default ``dt``=1
N : `float`
Derivative filter coefficient
Default ``N``=float('inf')
"""
self.P_block = Models.Gain(Kp=Kp, dt=dt, **opt) if Kp else None
self.I_block = Models.Integral(Ki=1 / Ti, dt=dt, **opt) if Ti else None
self.D_block = None
if Td:
self.D_block = Models.Derivative(Kd=Td, dt=dt, **opt) if N == float('inf') \
else Models.StateSpace(ABCD=[[[-N / Td]], [[1]], [[- N ** 2 / Td]], [[N]]], dt=dt, **opt)
super().__init__(in_len=1, name='PID_Serial', **opt)