Robust Control Toolbox™

Quadratic stability of polytopic or affine parameter-dependent systems

Syntax

• [tau,P] = quadstab(ps,options) 
  

Description

For affine parameter-dependent systems

E(p) = A(p)x, p(t) = (p1(t), . . ., pn(t))

or polytopic systems

E(t) = A(t)x, (A, E) Co{(A1, E1), . . ., (An, En)},

quadstab seeks a fixed Lyapunov function V(x) = x^TPx with P > 0 that establishes quadratic stability. The affine or polytopic model is described by ps (see psys).

The task performed by quadstab is selected by options(1):

• if options(1)=0 (default), quadstab assesses quadratic stability by solving the LMI problem

• Minimize over Q = QT such that

A^TQE + EQA^T < I for all admissible values of (A, E)

                   Q > I

• The global minimum of this problem is returned in tau and the system is quadratically stable if tau < 0

• if options(1)=1, quadstab computes the largest portion of the specified parameter range where quadratic stability holds (only available for affine models). Specifically, if each parameter pi varies in the interval

pi [pi0 - i, pi0 + i],

• quadstab computes the largest > 0 such that quadratic stability holds over the parameter box

pi [pi0 - i, pi0 + i]

• This "quadratic stability margin" is returned in tau and ps is quadratically stable if tau 1.

Given the solution Qopt of the LMI optimization, the Lyapunov matrix P is given by P = . This matrix is returned in P.

Other control parameters can be accessed through options(2) and options(3):

• if options(2)=0 (default), quadstab runs in fast mode, using the least expensive sufficient conditions. Set options(2)=1 to use the least conservative conditions
• options(3) is a bound on the condition number of the Lyapunov matrix P. The default is 109.

See Also
pdlstab     Robust stability of polytopic or affine parameter-dependent systems (P-system)

decay       Quadratic decay rate of polytopic or affine P-systems

quadperf    Compute the quadratic H performance of a polytopic or parameter-dependent system

psys        Specification of uncertain state-space models

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