Finding a solution x to the LMI system
|A(x) < 0||(1)|
is called the feasibility problem. Minimizing a convex objective under LMI constraints is also a convex problem. In particular, the linear objective minimization problem:
Minimize cTx subject to
|A(x) < 0||(2)|
plays an important role in LMI-based design. Finally, the generalized eigenvalue minimization problem
Minimize λ subject to
is quasi-convex and can be solved by similar techniques. It owes its name to the fact that is related to the largest generalized eigenvalue of the pencil (A(x),B(x)).
Many control problems and design specifications have LMI formulations . This is especially true for Lyapunov-based analysis and design, but also for optimal LQG control, H∞ control, covariance control, etc. Further applications of LMIs arise in estimation, identification, optimal design, structural design , , matrix scaling problems, and so on. The main strength of LMI formulations is the ability to combine various design constraints or objectives in a numerically tractable manner.
A nonexhaustive list of problems addressed by LMI techniques includes the following:
Lyapunov stability of parameter-dependent systems ()
Input/state/output properties of LTI systems (invariant ellipsoids, decay rate, etc.) ()
Robust pole placement
Optimal LQG control ()
Control of stochastic systems ()
Weighted interpolation problems ()
To hint at the principles underlying LMI design, let's review the LMI formulations of a few typical design objectives.
The stability of the dynamic system
is equivalent to the feasibility of the following problem:
Find P = PT such that AT P + P A < 0, P > I.
This can be generalized to linear differential inclusions (LDI)
where A(t) varies in the convex envelope of a set of LTI models:
A sufficient condition for the asymptotic stability of this LDI is the feasibility of
Find P = PT such that .
The random-mean-squares (RMS) gain of a stable LTI system
Minimize γ over X = XT and γ such that
For a stable LTI system
where w is a white noise disturbance with unit covariance, the LQG or H2 performance ∥G∥2 is defined by
It can be shown that
Hence is the global minimum of the LMI problem. Minimize Trace (Q) over the symmetric matrices P,Q such that
Again this is a linear objective minimization problem since the objective Trace (Q) is linear in the decision variables (free entries of P,Q).