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.TH obscont 1 "April 1993" "Scilab Group" "Scilab Function"
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obscont - observer based controller
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: \fVsyslin\fR list (nominal plant) in state-space form, continuous
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: real matrix, (full state) controller gain
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: real matrix, filter gain
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: \fVsyslin\fR list (controller)
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: \fVsyslin\fR list (extended controller)
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\fVobscont\fR returns the observer-based controller associated with a
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nominal plant \fVP\fR with matrices \fV[A,B,C,D]\fR (\fVsyslin\fR list).
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The full-state control gain is \fVKc\fR and the filter gain is \fVKf\fR.
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These gains can be computed, for example, by pole placement.
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\fVA+B*Kc\fR and \fVA+Kf*C\fR are (usually) assumed stable.
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\fVK\fR is a state-space representation of the
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compensator \fV K: y->u \fR in:
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\fV xdot = A x + B u, y=C x + D u, zdot= (A + Kf C)z -Kf y +B u, u=Kc z\fR
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\fVK\fR is a linear system (\fVsyslin\fR list) with matrices given by:
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\fVK=[A+B*Kc+Kf*C+Kf*D*Kc,Kf,-Kc]\fR.
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The closed loop feedback system \fV Cl: v ->y \fR with
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(negative) feedback \fVK\fR (i.e. \fVy = P u, u = v - K y\fR, or \fVxdot
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= A x + B u, y = C x + D u, zdot = (A + Kf C) z - Kf y + B u, u = v -F z\fR)
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is given by \fVCl = P/.(-K)\fR
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The poles of \fVCl\fR (\fV spec(cl('A')) \fR) are located at the eigenvalues of \fVA+B*Kc\fR
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Invoked with two output arguments \fVobscont\fR returns a
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(square) linear system \fVK\fR which parametrizes all the stabilizing
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Let \fVQ\fR an arbitrary stable linear system of dimension \fVr(2)\fRx\fVr(1)\fR
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i.e. number of inputs x number of outputs in \fVP\fR.
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Then any stabilizing controller \fVK\fR for \fVP\fR can be expressed as
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\fVK=lft(J,r,Q)\fR. The controller which corresponds to \fVQ=0\fR is
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\fVK=J(1:nu,1:ny)\fR (this \fVK\fR is returned by \fVK=obscont(P,Kc,Kf)\fR).
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\fVr\fR is \fVsize(P)\fR i.e the vector [number of outputs, number of inputs];
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ny=2;nu=3;nx=4;P=ssrand(ny,nu,nx);[A,B,C,D]=abcd(P);
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Kc=-ppol(A,B,[-1,-1,-1,-1]); //Controller gain
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Kf=-ppol(A',C',[-2,-2,-2,-2]);Kf=Kf'; //Observer gain
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cl=P/.(-obscont(P,Kc,Kf));spec(cl('A')) //closed loop system
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[J,r]=obscont(P,Kc,Kf);
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Q=ssrand(nu,ny,3);Q('A')=Q('A')-(maxi(real(spec(Q('A'))))+0.5)*eye(Q('A'))
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//Q is a stable parameter
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spec(h_cl(P,K)) // closed-loop A matrix (should be stable);
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ppol, lqg, lqr, lqe, h_inf, lft, syslin, feedback, observer