User`s guide
Table Of Contents
- Preface
 - Quick Start
 - LTI Models
- Introduction
 - Creating LTI Models
 - LTI Properties
 - Model Conversion
 - Time Delays
 - Simulink Block for LTI Systems
 - References
 
 - Operations on LTI Models
 - Arrays of LTI Models
 - Model Analysis Tools
 - The LTI Viewer
- Introduction
 - Getting Started Using the LTI Viewer: An Example
 - The LTI Viewer Menus
 - The Right-Click Menus
 - The LTI Viewer Tools Menu
 - Simulink LTI Viewer
 
 - Control Design Tools
 - The Root Locus Design GUI
- Introduction
 - A Servomechanism Example
 - Controller Design Using the Root Locus Design GUI
 - Additional Root Locus Design GUI Features
 - References
 
 - Design Case Studies
 - Reliable Computations
 - Reference
- Category Tables
 - acker
 - append
 - augstate
 - balreal
 - bode
 - c2d
 - canon
 - care
 - chgunits
 - connect
 - covar
 - ctrb
 - ctrbf
 - d2c
 - d2d
 - damp
 - dare
 - dcgain
 - delay2z
 - dlqr
 - dlyap
 - drmodel, drss
 - dsort
 - dss
 - dssdata
 - esort
 - estim
 - evalfr
 - feedback
 - filt
 - frd
 - frdata
 - freqresp
 - gensig
 - get
 - gram
 - hasdelay
 - impulse
 - initial
 - inv
 - isct, isdt
 - isempty
 - isproper
 - issiso
 - kalman
 - kalmd
 - lft
 - lqgreg
 - lqr
 - lqrd
 - lqry
 - lsim
 - ltiview
 - lyap
 - margin
 - minreal
 - modred
 - ndims
 - ngrid
 - nichols
 - norm
 - nyquist
 - obsv
 - obsvf
 - ord2
 - pade
 - parallel
 - place
 - pole
 - pzmap
 - reg
 - reshape
 - rlocfind
 - rlocus
 - rltool
 - rmodel, rss
 - series
 - set
 - sgrid
 - sigma
 - size
 - sminreal
 - ss
 - ss2ss
 - ssbal
 - ssdata
 - stack
 - step
 - tf
 - tfdata
 - totaldelay
 - zero
 - zgrid
 - zpk
 - zpkdata
 
 - Index
 

kalmd
11-112
11kalmd
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 t ime Ts for the continuous-time plant
with process noise and measurement noise satis fying
The estimator
kest is derived a s follows. The continuous plant sys is first
discretized using zero-order hold with sample time
Ts (see c2d entry), and the
continuous noisecovariance matrices and arereplacedby theirdiscrete
equivalents
The integral is computed using the matrix exponential formulas in [2]. A
discrete-timeestimatoristhen designedforthe discretized plantandnoise.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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