LMI Applications

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 c^Tx subject to

A(x) < 0

(2)

plays an important role in LMI-based design. Finally, the generalized eigenvalue minimization problem

Minimize λ subject to

\begin{array}{l} A (x) < λ B (x) \\ B (x) > 0 \\ C (x) > 0 \end{array}

(3)

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 [9]. 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 [6], [7], 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:

Robust stability of systems with LTI uncertainty (µ-analysis) ([24], [21], [27])
Robust stability in the face of sector-bounded nonlinearities (Popov criterion) ([22], [28], [13], [16])
Quadratic stability of differential inclusions ([15], [8])
Lyapunov stability of parameter-dependent systems ([12])
Input/state/output properties of LTI systems (invariant ellipsoids, decay rate, etc.) ([9])
Multi-model/multi-objective state feedback design ([4], [17], [3], [9], [10])
Robust pole placement
Optimal LQG control ([9])
Robust H^∞ control ([11], [14])
Multi-objective H^∞ synthesis ([18], [23], [10], [18])
Design of robust gain-scheduled controllers ([5], [2])
Control of stochastic systems ([9])
Weighted interpolation problems ([9])

To hint at the principles underlying LMI design, let's review the LMI formulations of a few typical design objectives.

Stability

The stability of the dynamic system

$\dot{x} = A x$

is equivalent to the feasibility of the following problem:

Find P = P^T such that A^T P + P A < 0, P > I.

This can be generalized to linear differential inclusions (LDI)

$\dot{x} = A (t) x$

where A(t) varies in the convex envelope of a set of LTI models:

$A (t) \in C o {A_{1}, \dots, A_{n}} = {\sum_{i = 1}^{n} a_{i} A_{i} : a_{i} \geq 0, \sum_{i = 1}^{N} a_{i} = 1} .$

A sufficient condition for the asymptotic stability of this LDI is the feasibility of

Find P = P^T such that $A_{i}^{T} P + P A_{i} < 0, P > I$ .

RMS Gain

The random-mean-squares (RMS) gain of a stable LTI system

${\begin{matrix} \dot{x} = A x + B u \\ y = C x + D u \end{matrix}$

is the largest input/output gain over all bounded inputs u(t). This gain is the global minimum of the following linear objective minimization problem [1], [25], [26].

Minimize γ over X = X^T and γ such that

$(\begin{matrix} A^{T} X + X A & X B & C^{T} \\ B^{T} X & - γ I & D^{T} \\ C & D & - γ I \end{matrix}) < 0$

and

$X > 0.$

LQG Performance

For a stable LTI system

$G {\begin{matrix} \dot{x} = A x + B w \\ y = C x \end{matrix}$

where w is a white noise disturbance with unit covariance, the LQG or H₂ performance ∥G∥₂ is defined by

$\begin{matrix} {‖ G ‖}_{2}^{2} : = \lim_{T \to \infty} E {\frac{1}{T} \int_{0}^{T} y^{T} (t) y (t) d t} \\ = \frac{1}{2 π} \int_{- \infty}^{\infty} G^{H} (j ω) G (j ω) d ω . \end{matrix}$

It can be shown that

${‖ G ‖}_{2}^{2} = \inf {Trace ({CPC}^{T}) : A P + P A^{T} + B B^{T} < 0} .$

Hence ${‖ G ‖}_{2}^{2}$ is the global minimum of the LMI problem. Minimize Trace (Q) over the symmetric matrices P,Q such that

$A P + P A^{T} + B B^{T} < 0$

and

$(\begin{matrix} Q & C P \\ P C^{T} & P \end{matrix}) > 0.$

Again this is a linear objective minimization problem since the objective Trace (Q) is linear in the decision variables (free entries of P,Q).