EKF#
- class EKF(model)[source]#
Bases:
Estimator
Extended Kalman Filter. Setup this class and use
EKF.make_step()
during runtime to obtain the currently estimated states given the measurementsy0
.Warning
Not currently implemented.
Methods#
make_step#
- make_step(self, y0)#
Main method during runtime. Pass the most recent measurement and retrieve the estimated state.
reset_history#
- reset_history(self)#
Reset the history of the estimator
- Return type:
None
Attributes#
t0#
- EKF.t0#
Current time marker of the class. Use this property to set of query the time.
Set with
int
,float
,numpy.ndarray
orcasadi.DM
type.
u0#
- EKF.u0#
Initial input and current iterate. This is the numerical structure holding the information about the current input in the class. The property can be indexed according to the model definition.
Example:
model = do_mpc.model.Model('continuous') model.set_variable('_u','heating', shape=(4,1)) ... mhe = do_mpc.estimator.MHE(model) # or mpc = do_mpc.estimator.MPC(model) # Get or set current value of variable: mpc.u0['heating', 0] # 0th element of variable mpc.u0['heating'] # all elements of variable mpc.u0['heating', 0:2] # 0th and 1st element
Useful CasADi symbolic structure methods:
.shape
.keys()
.labels()
x0#
- EKF.x0#
Initial state and current iterate. This is the numerical structure holding the information about the current states in the class. The property can be indexed according to the model definition.
Example:
model = do_mpc.model.Model('continuous') model.set_variable('_x','temperature', shape=(4,1)) ... mhe = do_mpc.estimator.MHE(model) # or mpc = do_mpc.estimator.MPC(model) # Get or set current value of variable: mpc.x0['temperature', 0] # 0th element of variable mpc.x0['temperature'] # all elements of variable mpc.x0['temperature', 0:2] # 0th and 1st element
Useful CasADi symbolic structure methods:
.shape
.keys()
.labels()
z0#
- EKF.z0#
Initial algebraic state and current iterate. This is the numerical structure holding the information about the current algebraic states in the class. The property can be indexed according to the model definition.
Example:
model = do_mpc.model.Model('continuous') model.set_variable('_z','temperature', shape=(4,1)) ... mhe = do_mpc.estimator.MHE(model) # or mpc = do_mpc.estimator.MPC(model) # Get or set current value of variable: mpc.z0['temperature', 0] # 0th element of variable mpc.z0['temperature'] # all elements of variable mpc.z0['temperature', 0:2] # 0th and 1st element
Useful CasADi symbolic structure methods:
.shape
.keys()
.labels()