Apps

Linear State Space

Apps.LinearStateSpace History

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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:
!!! Example Model


! new linear time-invariant object
to:
!! Example Model


 ! new linear time-invariant object
Changed lines 20-31 from:
! 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]
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:
Attach:lti_step_response.png
to:
Attach:lti_step_response.png

!!! Example Model


! new linear time-invariant object
Model control
  Objects
    mpc = lti
  End Objects
End Model

! 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]
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 12:54 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 %blue%A%red%P%black%Monitor 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 %blue%A%red%P%black%Monitor for a linear MPC upgrade.  Once in %blue%A%red%P%black%Monitor 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 12:54 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 %blue%A%red%P%black%Monitor 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 %blue%A%red%P%black%Monitor 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 12:54 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 %blue%A%red%P%black%Monitor 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 %blue%A%red%P%black%Monitor 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 12:38 PM by 158.35.225.231 -
Changed lines 1-2 from:
!! Linear State Space Model
to:
!! Linear Model Predictive Control
Added lines 3-4:

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 %blue%A%red%P%black%Monitor uses for estimation and control.
November 04, 2008, at 09:06 AM by 158.35.225.231 -
Changed line 5 from:
Attach:linear_ss.png
to:
Attach:lti_step_response.png
November 04, 2008, at 09:06 AM by 158.35.225.231 -
Changed lines 3-5 from:
* %list list-page% [[Attach:fir.apm | Linear State Space]]
to:
* %list list-page% [[Attach:fir.apm | Linear State Space]]

Attach:linear_ss.png
November 03, 2008, at 08:49 PM by 98.199.241.177 -
Added lines 1-3:
!! Linear State Space Model

* %list list-page% [[Attach:fir.apm | Linear State Space]]