Code covered by the BSD License
-
[a,b,nze]=kab2ab(kab,na,ma,nb...
KAB2AB: Function to reconstruct a,b from kron(a,b)
-
[f,k,l,sigp,sigs,spr,pt,st,ph...
DELROTIN: Delta Deterministic Parameter Reduced-Order Time-Invariant iNfinite horizon LQG
-
[f,k,l,sigp,sigs,spr,pt,st,ph...
DPROTIN: Deterministic Parameter Discrete-time Reduced-Order Time-Invariant
-
[f,k,l,sigp,sigs,spr,pt,st,ph...
SPDROTIN: Stochastic Parameter Delta Reduced-Order Time Invariant iNfinite horizon LQG compensation.
-
[f,k,l,sigp,sigs,spr,pt,st,ph...
SPDFOTIN: Stochastic Parameter Delta Full-Order Time Invariant iNfinite horizon LQG compensation.
-
[f,k,l,sigp,sigs,spr,pt,st,ph...
SPROTIN: Stochastic Parameter Reduced-Order Time Invariant iNfinite horizon LQG compensation.
-
[ggipsd]=gginv(psd,r,tol)
GGINV: Group generalized inverse of matrix psd
-
[kba]=kab2kba(kab,na,ma,nb,mb...
-
[mre]=maxreig(a,delta)
MAXREIG: Stability measure for continuous-time systems and
-
[nx,ny,nu,q,mc,v,me]=abcchk(a...
ABCCHK: Check and generate dimensions
-
[nx,ny,nu,q,mc,v,me]=pgcchk(p...
PGCCHK: Check and generate dimensions
-
[nxs,nus,nys]=varchk(nx,nu,ny...
VARCHK: Check dimensions mean square and variance matrices
-
[osc,endc,conver,bb,nocon,tps...
RCCHK: Check reduced-order algorithm convergence
-
[osc,ldif,sdif]=detosc(osc,tr...
DETOSC : detect oscillation during iteration
-
[p,g,c,v,w,q,r,delta,nc,pp,gg...
SPDFUTV: Function specifying a reduced order finite horizon
-
[p,g,c,v,w,q,r]=crlqg(nx,nu,n...
CRLQG.M: Continuous-time random LQG problem generation.
-
[p,g,pp,gg,pg,pva,gva,pgva,qi...
DELDORTIS1: Equivalent Delta Optimal Control Problem for Stochastic Parameter Systems
-
[p,g,pp,gg,pg,pva,gva,pgva,qi...
EDORTIS1: Equivalent Discrete Optimal Control Problem for Stochastic Parameter Systems
-
[ph,p12,p2]=iniphsh(ic,nx,nc)
INIPHSH : Function generates initial values ph, sh
-
[pkab]=permkron(a,b,nx,nc);
PERMKRON: Permuted Kronecker product of a and b associated to
-
[pt,ph,st,sh,tps,psi1,psi2,dt...
SPDFOEQ: one iteration of the Stochastic Parameter
-
[pt,ph,st,sh,tps,psi1,psi2,ta...
SPDROEQ: one iteration of the Stochastic Parameter
-
[pt,ph,st,sh,tps,psi1,psi2,ta...
DELROEQ :One iteration of the Delta Deterministic Parameter
-
[pt,ph,st,sh,tps,psi1,psi2]=d...
DPROEQ: one iteration of the Deterministic Parameter
-
[pt,ph,st,sh,tps]=sproeq(pt,p...
SPROEQ: one iteration of the Stochastic Parameter
-
[pt,ph]=spdroeqe(pm,gm,cm,pms...
SPDROEQE one iteration of the Stochastic Parameter
-
[ptn]=intps(pt,N1,N2)
-
[sigp,sigs,ko,lo]=spdcomco(pm...
SPDCOMCO: Stochastic Parameter Delta COMpensator COst.
-
[st,sh]=spdroeqc(pm,gm,cm,pms...
SDPROEQC: one iteration of the Delta Stochastic Parameter
-
[tau,ga,ha,ma]=gmhfac(ph,sh,n...
-
[xfkl,sigp,sigs,ptt,stt,pht,s...
SPDROTV: Stochastic Parameter Delta Reduced Order Time Varying LQG compensation.
-
dellyap(a,c,delta)
DELLYAP: Delta Lyapunov equation solution.
-
pinvr(A,rtol,fr)
PINVR pseudo inverse with rank detection.
-
pinvrd(A,tol)
PINVRD pseudo inverse with rank deficiency detection.
-
plconv(trt,trr,epsl,endloop,t...
PLCONV: Plot convergence results
-
sr=sperad(mat);
SPERAD: spectral radius of a square matrix
-
ex1delta.m
-
ex2delta.m
-
ex3delta.m
-
ex4delta.m
-
readme.m
-
View all files
Continuous and discrete time optimal reduced order output feedback
by Gerard Van Willigenburg
17 Jan 2011
(Updated 15 Aug 2011)
Software associated with : International Journal of Control, 83, 12, 2546-2563, 2010
|
Watch this File
|
| File Information |
| Description |
Using the delta operator, the strengthened discrete-time optimal projection equations for optimal reduced-order compensation of systems with white stochastic parameters are formulated in the delta domain. The delta domain unifies discrete time and continuous time. Moreover, when formulated in this domain, the efficiency and numerical conditioning of algorithms improves when the sampling rate is high. Exploiting the unification, important theoretical results, algorithms and compensatability tests concerning finite and infinite horizon optimal compensation of systems with white stochastic parameters are carried over from discrete time to continuous time. Among others, we consider the finite-horizon time-varying compensation problem for systems with white stochastic parameters and the property mean-square compensatability (ms-compensatability) that determines whether a system with white stochastic parameters can be stabilised by means of a compensator. In continuous time, both of these appear to be new. This also holds for the associated numerical algorithms and tests to verify ms-compensatability. They are illustrated with three numerical examples that reveal several interesting theoretical and numerical issues. A fourth example illustrates the improvement of both the efficiency and numerical conditioning of the algorithms. This is of vital practical importance for digital control system design when the sampling rate is high. |
| Required Products |
Control System Toolbox
|
| MATLAB release |
MATLAB 7.5 (R2007b)
|
|
Tags for This File
|
| Everyone's Tags |
|
| Tags I've Applied |
|
| Add New Tags |
Please login to tag files.
|
| Updates |
| 15 Aug 2011 |
Closed loop spectral radius computation in sprotin.m, spdrotin.m and spdfotin.m has been corrected. The code now also works properly for problems with pgva or pcva unequal to zero (these are zero in the paper examples). |
|
Contact us at files@mathworks.com