Specifications
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- Introduction
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- Function Reference
- Functions by Category
- acker
- allmargin
- append
- augstate
- balreal
- bode
- bodemag
- c2d
- canon
- care
- chgunits
- connect
- covar
- ctrb
- ctrbf
- d2c
- d2d
- damp
- dare
- dcgain
- delay2z
- dlqr
- dlyap
- drss
- dsort
- dss
- dssdata
- esort
- estim
- evalfr
- feedback
- filt
- frd
- frdata
- freqresp
- gensig
- get
- gram
- hasdelay
- impulse
- initial
- interp
- inv
- isct, isdt
- isempty
- isproper
- issiso
- kalman
- kalmd
- lft
- lqgreg
- lqr
- lqrd
- lqry
- lsim
- ltimodels
- ltiprops
- ltiview
- lyap
- margin
- minreal
- modred
- ndims
- ngrid
- nichols
- norm
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- obsv
- obsvf
- ord2
- pade
- parallel
- place
- pole
- pzmap
- reg
- reshape
- rlocus
- rss
- series
- set
- sgrid
- sigma
- sisotool
- size
- sminreal
- ss
- ss2ss
- ssbal
- ssdata
- stack
- step
- tf
- tfdata
- totaldelay
- zero
- zgrid
- zpk
- zpkdata
- Index

kalmd
16-113
16kalmd
Purpose Design discrete Kalman estimator for continuous plant
Syntax [kest,L,P,M,Z] = kalmd(sys,Qn,Rn,Ts)
Description kalmd designs a discrete-time Kalman estimator that has response
characteristics similar to a continuous-time estimator designed with
kalman.
This command is useful to derive a discrete estimator for digital
implementation after a satisfactory continuous estimator has been designed.
[kest,L,P,M,Z] = kalmd(sys,Qn,Rn,Ts) produces a discrete Kalman
estimator
kest with sample time Ts for the continuous-time plant
with process noise and measurement noise satisfying
The estimator
kest is derived as follows. The continuous plant sys is first
discretized using zero-order hold with sample time
Ts (see c2d entry), and the
continuous noisecovariancematrices and arereplacedbytheirdiscrete
equivalents
The integral is computed using the matrix exponential formulas in [2]. A
discrete-timeestimator isthendesignedforthediscretizedplantandnoise. See
kalman for details on discrete-time Kalman estimation.
kalmd also returns the estimator gains L and M, and the discrete error
covariance matrices
P and Z (see kalman for details).
Limitations The discretized problem data should satisfy the requirements for kalman.
See Also kalman Design Kalman estimator
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