Define MPC controller state
xmpc = mpcstate(MPCobj)
xmpc = mpcstate(MPCobj,xp,xd,xn,u,p)
xmpc = mpcstate
xmpc = mpcstate(MPCobj)
creates a controller
state object compatible with the controller object, MPCobj
, in
which all fields are set to their default values that are associated with the
controller’s nominal operating point.
xmpc = mpcstate(MPCobj,xp,xd,xn,u,p)
sets the
state fields of the controller state object to specified values. The controller may be
an implicit or explicit controller object. Use this controller state object to
initialize an MPC controller at a specific state other than the default state.
xmpc = mpcstate
returns an mpcstate
object in
which all fields are empty.
mpcstate
objects are updated by mpcmove
through the internal state observer based on the extended
prediction model. The overall state is updated from the measured output
y_{m}(k) by a linear
state observer (see State Observer).

MPC controller, specified as either a traditional MPC controller
( 

Plant model state estimates, specified as a vector with N_{xp} elements, where N_{xp} is the number of states in the plant model. 

Disturbance model state estimates, specified as a vector with N_{xd} elements, where N_{xd} is the total number of states in the input and output disturbance models. The disturbance model states are ordered such that input disturbance model states are followed by output disturbance model state estimates. 

Measurement noise model state estimates, specified as a vector with N_{xn} elements, where N_{xn} is the number of states in the measurement noise model. 

Values of the manipulated variables during the previous control interval, specified as a vector with N_{u} elements, where N_{u} is the number of manipulated variables. 

Covariance matrix for the state estimates, specified as an NbyN matrix, where N is the sum of N_{xp}, N_{xd} and N_{xn}). 

MPC state object, containing the following properties.

getEstimator
 getoutdist
 mpcmove
 setEstimator
 setindist
 setoutdist
 ss