Apps

## Apps.LinearStateSpace History

Changed line 32 from:
 File *.mpc.txt

to:
 File mpc.txt

Changed line 40 from:
 File *.mpc.a.txt

to:
 File mpc.a.txt

Changed line 47 from:
 File *.mpc.b.txt

to:
 File mpc.b.txt

Changed line 55 from:
 File *.mpc.c.txt

to:
 File mpc.c.txt

Changed line 62 from:
 File *.mpc.d.txt

to:
 File mpc.d.txt

Changed lines 14-19 from:

Model control

  Objects
mpc = lti
End Objects


End Model

to:
 Model control
Objects
mpc = lti
End Objects
End Model

Changed lines 32-38 from:

File *.mpc.txt

  sparse, continuous  ! dense/sparse, continuous/discrete
2      ! m=number of inputs
3      ! n=number of states
3      ! p=number of outputs


End File

to:
 File *.mpc.txt
sparse, continuous  ! dense/sparse, continuous/discrete
2      ! m=number of inputs
3      ! n=number of states
3      ! p=number of outputs
End File

Changed lines 40-45 from:

File *.mpc.a.txt

  1  1  0.9
2  2  0.1
3  3  0.5


End File

to:
 File *.mpc.a.txt
1  1  0.9
2  2  0.1
3  3  0.5
End File

Changed lines 47-53 from:

File *.mpc.b.txt

  1  1  1.0
2  2  1.0
3  1  0.5
3  2  0.5


End File

to:
 File *.mpc.b.txt
1  1  1.0
2  2  1.0
3  1  0.5
3  2  0.5
End File

Changed lines 55-60 from:

File *.mpc.c.txt

  1  1  0.5
2  2  1.0
3  3  2.0


End File

to:
 File *.mpc.c.txt
1  1  0.5
2  2  1.0
3  3  2.0
End File

Changed lines 62-64 from:

File *.mpc.d.txt

  1  1  0.2


End File

to:
 File *.mpc.d.txt
1  1  0.2
End File

Changed lines 10-13 from:

# new linear time-invariant object

to:

## Example Model

 ! new linear time-invariant object

Changed lines 20-31 from:

# y[k] = C * x[k] + D * u[k]

to:
 ! Model information
! continuous form
! dx/dt = A * x + B * u
!     y = C * x + D * u
!
! dimensions
! (nx1) = (nxn)*(nx1) + (nxm)*(mx1)
! (px1) = (pxn)*(nx1) + (pxm)*(mx1)
!
! discrete form
! x[k+1] = A * x[k] + B * u[k]
!   y[k] = C * x[k] + D * u[k]

Changed line 39 from:

# A matrix (row, column, value)

to:
 ! A matrix (row, column, value)

Changed line 46 from:

# B matrix (row, column, value)

to:
 ! B matrix (row, column, value)

Changed line 54 from:

# C matrix (row, column, value)

to:
 ! C matrix (row, column, value)

Changed line 61 from:

# D matrix (row, column, value)

to:
 ! D matrix (row, column, value)

Changed lines 8-64 from:
to:

# new linear time-invariant object

Model control

  Objects
mpc = lti
End Objects


End Model

# y[k] = C * x[k] + D * u[k]

File *.mpc.txt

  sparse, continuous  ! dense/sparse, continuous/discrete
2      ! m=number of inputs
3      ! n=number of states
3      ! p=number of outputs


End File

# A matrix (row, column, value)

File *.mpc.a.txt

  1  1  0.9
2  2  0.1
3  3  0.5


End File

# B matrix (row, column, value)

File *.mpc.b.txt

  1  1  1.0
2  2  1.0
3  1  0.5
3  2  0.5


End File

# C matrix (row, column, value)

File *.mpc.c.txt

  1  1  0.5
2  2  1.0
3  3  2.0


End File

# D matrix (row, column, value)

File *.mpc.d.txt

  1  1  0.2


End File

November 04, 2008, at 07:54 PM by 158.35.225.231 -
Changed line 6 from:

These models are typically in the finite impulse response form or linear state space form. Either model form can be converted to an APMonitor for a linear MPC upgrade. Once the linear MPC model is converted, nonlinear elements can be added to avoid multiple model switching, gain scheduling, or other ad hoc measures commonly employed because of linear MPC restrictions.

to:

These models are typically in the finite impulse response form or linear state space form. Either model form can be converted to an APMonitor for a linear MPC upgrade. Once in APMonitor form, nonlinear elements can be added to avoid multiple model switching, gain scheduling, or other ad hoc measures commonly employed because of linear MPC restrictions.

November 04, 2008, at 07:54 PM by 158.35.225.231 -
Changed line 6 from:

These models are typically in the finite impulse response form or linear state space form. Either model form can be converted to an APMonitor for a linear MPC upgrade. Once the linear MPC model is converted, nonlinear elements can be added to avoid multiple model switching, gain scheduling, or other ad hoc measures commonly employed because of linear MPC shortcomings.

to:

These models are typically in the finite impulse response form or linear state space form. Either model form can be converted to an APMonitor for a linear MPC upgrade. Once the linear MPC model is converted, nonlinear elements can be added to avoid multiple model switching, gain scheduling, or other ad hoc measures commonly employed because of linear MPC restrictions.

November 04, 2008, at 07:54 PM by 158.35.225.231 -
Changed lines 4-6 from:

Linear model predictive controllers are based on models in the finite impulse response form or linear state space form. Either model form can be converted to a form that APMonitor uses for estimation and control.

to:

Model Predictive Control, or MPC, is an advanced method of process control that has been in use in the process industries such as chemical plants and oil refineries since the 1980s. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification.

These models are typically in the finite impulse response form or linear state space form. Either model form can be converted to an APMonitor for a linear MPC upgrade. Once the linear MPC model is converted, nonlinear elements can be added to avoid multiple model switching, gain scheduling, or other ad hoc measures commonly employed because of linear MPC shortcomings.

November 04, 2008, at 07:38 PM by 158.35.225.231 -
Changed lines 1-2 from:

to:

## Linear Model Predictive Control

Linear model predictive controllers are based on models in the finite impulse response form or linear state space form. Either model form can be converted to a form that APMonitor uses for estimation and control.

November 04, 2008, at 04:06 PM by 158.35.225.231 -
Changed line 5 from:
to:
November 04, 2008, at 04:06 PM by 158.35.225.231 -
Changed lines 3-5 from:
to:
November 04, 2008, at 03:49 AM by 98.199.241.177 -