Convert unconstrained MPC controller to state-space linear system
sys = ss(MPCobj)
sys = ss(MPCobj,signals)
sys = ss(MPCobj,signals,ref_preview,md_preview)
[sys,ut] = ss(MPCobj)
ss command returns a linear controller
in the state-space form. The controller is equivalent to the traditional
(implicit) MPC controller
MPCobj when no constraints
are active. You can then use Control System Toolbox™ software for
sensitivity analysis and other diagnostic calculations.
the linear discrete-time dynamic controller
sys = ss(
x(k + 1) = Ax(k) + Bym(k)
u(k) = Cx(k) + Dym(k)
where ym is the vector
of measured outputs of the plant, and u is the
vector of manipulated variables. The sampling time of controller
Note Vector x includes the states of the observer (plant + disturbance + noise model states) and the previous manipulated variable u(k-1).
the linearized MPC controller in its full form and allows you to specify
the signals that you want to include as inputs for
sys = ss(
The full form of the MPC controller has the following structure:
x(k + 1) = Ax(k) + Bym(k) + Brr(k) + Bvv(k) + Bututarget(k) + Boff
u(k) = Cx(k) + Dym(k) + Drr(k) + Dvv(k) + Dututarget(k) + Doff
Here, r is the vector of setpoints for both measured and unmeasured plant outputs, v is the vector of measured disturbances, utarget is the vector of preferred values for manipulated variables.
signals as a single or multicharacter
string constructed using any of the following:
'r' — Output references
'v' — Measured disturbances
'o' — Offset terms
't' — Input targets
For example, to obtain a controller that maps [ym; r; v] to u, use:
sys = ss(MPCobj,'rv');
In the general case of nonzero offsets, ym (as
well as r, v, and utarget)
must be interpreted as the difference between the vector and the corresponding
offset. Offsets can be nonzero is
Vectors Boff, Doff are
constant terms. They are nonzero if and only if
nonzero (continuous-time prediction models), or
nonzero (discrete-time prediction models). In other words, when
an equilibrium state, Boff, Doff are
Only the following fields of
MPCobj are used
when computing the state-space model:
if the MPC controller has preview actions on the reference and measured
disturbance signals. If the flag
sys = ss(
then matrices Br and Dr multiply
the whole reference sequence:
x(k + 1) = Ax(k) + Bym(k) + Br[r(k);r(k + 1);...;r(k + p – 1)] +...
u(k) = Cx(k) + Dym(k) + Dr[r(k);r(k + 1);...;r(k + p– 1)] +...
Similarly if the flag
matrices Bv and Dv multiply
the whole measured disturbance sequence:
x(k + 1) = Ax(k) +...+ Bv[v(k);v(k + 1);...;v(k + p)] +...
u(k) = Cx(k) +...+ Dv[v(k);v(k + 1);...;v(k + p)] +...
[ additionally returns
the input target values for the full form of the controller.
ut is returned as a vector of doubles,
utarget(k+1); ... utarget(k+h)].
h — Maximum length of previewed
inputs, that is,
h = max(length(MPCobj.ManipulatedVariables(:).Target))
utarget — Difference between
the input target and corresponding input offsets, that is,
To improve the clarity of the example, suppress messages about working with an MPC controller.
old_status = mpcverbosity('off');
Create the plant model.
G = rss(5,2,3); G.D = 0; G = setmpcsignals(G,'mv',1,'md',2,'ud',3,'mo',1,'uo',2);
Configure the MPC controller with nonzero nominal values, weights, and input targets.
C = mpc(G,0.1); C.Model.Nominal.U = [0.7 0.8 0]; C.Model.Nominal.Y = [0.5 0.6]; C.Model.Nominal.DX = rand(5,1); C.Weights.MV = 2; C.Weights.OV = [3 4]; C.MV.Target = [0.1 0.2 0.3];
C is an unconstrained MPC controller. Specifying
C.Model.Nominal.DX as nonzero means that the nominal values are not at steady state.
C.MV.Target specifies three preview steps.
C to a state-space model.
sys = ss(C);
sys, is a seventh-order SISO state-space model. The seven states include the five plant model states, one state from the default input disturbance model, and one state from the previous move,