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Optimal control action

`u = mpcmove(MPCobj,x,ym,r,v)`

[u,Info] = mpcmove(MPCobj,x,ym,r,v)

[u,Info] = mpcmove(MPCobj,x,ym,r,v,Options)

computes
the optimal manipulated variable moves, `u`

= mpcmove(`MPCobj`

,`x`

,`ym`

,`r`

,`v`

)*u*(*k*),
at the current time. *u*(*k*) is
calculated given the current estimated extended state, *x*(*k*),
the measured plant outputs, *y _{m}*(

`mpcmove`

repeatedly
to simulate closed-loop model predictive control.`[`

returns
additional information regarding the model predictive controller in
the second output argument `u`

,`Info`

] = mpcmove(`MPCobj`

,`x`

,`ym`

,`r`

,`v`

)`Info`

.

`[`

overrides
default constraints and weights settings in `u`

,`Info`

] = mpcmove(`MPCobj`

,`x`

,`ym`

,`r`

,`v`

,`Options`

)`MPCobj`

with
the values specified by `Options`

, an `mpcmoveopt`

object. Use `Options`

to
provide run-time adjustment in constraints and weights during the
closed-loop simulation.

Use

`sim`

for plant mismatch and noise simulation when not using run-time constraints or weight changes.Use theMPC Designer app to interactively design and simulate model predictive controllers.

Use the MPC Controller block in Simulink and for code generation.

`getEstimator`

| `mpc`

| `mpcmoveopt`

| `mpcstate`

| `review`

| `setEstimator`

| `sim`

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