Specify uncertain state-space models or convert LTI model to uncertain state-space model


usys = uss(a,b,c,d)
usys = uss(a,b,c,d,Ts)
usys = uss(d)
usys = uss(a,b,c,d,Property,Value,...)
usys = uss(a,b,c,d,Ts,Property,Value,...)
usys = uss(sys)


uss creates uncertain state-space models (uss objects) or to convert LTI models to the uss class.

usys = uss(a,b,c,d) creates a continuous-time uncertain state-space object. The matrices a, b, c and d can be umat and/or double and/or uncertain atoms. These are the 4 matrices associated with the linear differential equation model to describe the system.

usys = uss(a,b,c,d,Ts) creates a discrete-time uncertain state-space object with sample time Ts.

usys = uss(d) specifies a static gain matrix and is equivalent to usys = uss([],[],[],d).

Any of these syntaxes can be followed by property name/property value pairs.

usys = uss(a,b,c,d,'P1',V1,'P2',V2,...) set the properties P1, P2, ... to the values V1, V2, ...

usys = uss(sys) converts an arbitrary ss, tf or zpk model sys to an uncertain state-space object without uncertainties. Both usys.NominalValue and simplify(usys,'class') are the same as ss(sys).


You can first create two uncertain atoms and use them to create two uncertain matrices. These four matrices can be packed together to form a 1-output, 1-input, 2-state continuous-time uncertain state-space system.

p1 = ureal('p1',5,'Range',[2 6]); 
p2 = ureal('p2',3,'Plusminus',0.4); 
A = [-p1 0;p2 -p1]; 
B = [0;p2]; 
C = [1 1]; 
usys = uss(A,B,C,0); 

In the second example, you can convert a not-uncertain tf model to an uncertain state-space model without uncertainties. You can verify the equality of the nominal value of the usys object and simplified representation to the original system.

G = tf([1 2 3],[1 2 3 4]); 
usys = uss(G) 
USS: 3 States, 1 Output, 1 Input, Continuous System 
ans = 
ans = 

See Also


Was this topic helpful?