Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

The LMI Lab is a high-performance package for solving general
LMI problems. It blends simple tools for the specification and manipulation
of LMIs with powerful LMI solvers for three generic LMI problems.
Thanks to a structure-oriented representation of LMIs, the various
LMI constraints can be described in their natural block-matrix form.
Similarly, the optimization variables are specified directly as *matrix
variables* with some given structure. Once an LMI problem
is specified, it can be solved numerically by calling the appropriate
LMI solver. The three solvers `feasp`

, `mincx`

, and `gevp`

constitute
the computational engine of the LMI portion of Robust Control Toolbox™ software.
Their high performance is achieved through C-MEX implementation and
by taking advantage of the particular structure of each LMI.

The LMI Lab offers tools to

Specify LMI systems either symbolically with the LMI Editor or incrementally with the

`lmivar`

and`lmiterm`

commandsRetrieve information about existing systems of LMIs

Modify existing systems of LMIs

Solve the three generic LMI problems (feasibility problem, linear objective minimization, and generalized eigenvalue minimization)

Validate results

This chapter gives a tutorial introduction to the LMI Lab as well as more advanced tips for making the most out of its potential.

Any linear matrix inequality can be expressed in the canonical form

*L*(*x*) = *L*_{0} + *x*_{1}*L*_{1} +
. . . + *x*_{N}*L**_{N}* <
0

where

*L*_{0},*L*_{1}, . . . ,*L*are given symmetric matrices_{N}= (*x**x*_{1}, . . . ,*x*)_{N}∊^{T}*R*is the vector of scalar variables to be determined. We refer to^{N}*x*_{1}, . . . ,*x*as the_{N}*decision variables*. The names "design variables" and "optimization variables" are also found in the literature.

Even though this canonical expression is generic, LMIs rarely arise in this form in control applications. Consider for instance the Lyapunov inequality

$${A}^{T}X+XA<0$$ | (4-1) |

where

$$A=\left(\begin{array}{cc}-1& 2\\ 0& -2\end{array}\right)$$

and the variable

$$X=\left(\begin{array}{cc}{x}_{1}& {x}_{2}\\ {x}_{2}& {x}_{3}\end{array}\right)$$

is a symmetric matrix. Here the decision variables are the
free entries *x*_{1}, *x*_{2}, *x*_{3} of *X* and
the canonical form of this LMI reads

$${x}_{1}\left(\begin{array}{cc}-2& 2\\ 2& 0\end{array}\right)+{x}_{2}\left(\begin{array}{cc}0& -3\\ -3& 4\end{array}\right)+{x}_{3}\left(\begin{array}{cc}0& 0\\ 0& -4\end{array}\right)<0.$$ | (4-2) |

Clearly this expression is less intuitive and transparent than Equation 4-1. Moreover, the
number of matrices involved in Equation 4-2 grows roughly as *n*^{2} /2
if *n* is the size of the *A* matrix.
Hence, the canonical form is very inefficient from a storage viewpoint
since it requires storing `o`

(*n*^{2} /2)
matrices of size *n* when the single *n*-by-*n* matrix *A* would
be sufficient. Finally, working with the canonical form is also detrimental
to the efficiency of the LMI solvers. For these various reasons, the
LMI Lab uses a *structured representation* of LMIs.
For instance, the expression *A*^{T}*X* + *XA* in
the Lyapunov inequality Equation 4-1 is explicitly described as a function
of the matrix variable *X*, and only the *A* matrix
is stored.

In general, LMIs assume a block matrix form where each block
is an affine combination of the matrix variables. As a fairly typical
illustration, consider the following LMI drawn from *H*^{∞} theory

$${N}^{T}\left(\begin{array}{ccc}{A}^{T}X+XA& X{C}^{T}& B\\ CX& -\gamma I& D\\ {B}^{T}& {D}^{T}& -\gamma I\end{array}\right)N<0$$ | (4-3) |

where * A*,

*N*is called the*outer factor*, and the block matrix$$L\left(X,\gamma \right)=\left(\begin{array}{ccc}{A}^{T}X+XA& X{C}^{T}& B\\ CX& -\gamma I& D\\ {B}^{T}& {D}^{T}& -\gamma I\end{array}\right)$$

is called the

*inner factor*. The outer factor*needs not be square*and is*often absent*.*X*and γ are the*matrix variables*of the problem. Note that scalars are considered as 1-by-1 matrices.The inner factor

*L*(*X,*γ) is a symmetric*block matrix*, its block structure being characterized by the sizes of its diagonal blocks. By symmetry,*L*(*X,*γ) is entirely specified by the blocks on or above the diagonal.Each block of

*L*(*X,*γ) is an affine expression in the matrix variables*X*and γ. This expression can be broken down into a sum of elementary*terms*. For instance, the block (1,1) contains two elementary terms:*A*^{T}*X*and*XA*.Terms are either

*constant*or*variable*. Constant terms are fixed matrices like*B*and*D*above. Variable terms involve one of the matrix variables, like*XA*,*XC*, and –γ^{T}*I*above.

The LMI (Equation 4-3) is specified by the list of terms in each block, as is any LMI regardless of its complexity.

As for the matrix variables *X* and γ,
they are characterized by their dimensions and structure. Common structures
include rectangular unstructured, symmetric, skew-symmetric, and scalar.
More sophisticated structures are sometimes encountered in control
problems. For instance, the matrix variable *X* could
be constrained to the block-diagonal structure:

$$X=\left(\begin{array}{ccc}{x}_{1}& 0& 0\\ 0& {x}_{2}& {x}_{3}\\ 0& {x}_{3}& {x}_{4}\end{array}\right).$$

Another possibility is the symmetric Toeplitz structure:

$$X=\left(\begin{array}{ccc}{x}_{1}& {x}_{2}& {x}_{3}\\ {x}_{2}& {x}_{1}& {x}_{2}\\ {x}_{3}& {x}_{2}& {x}_{1}\end{array}\right).$$

Summing up, structured LMI problems are specified by declaring the matrix variables and describing the term content of each LMI. This term-oriented description is systematic and accurately reflects the specific structure of the LMI constraints. There is no built-in limitation on the number of LMIs that you can specify or on the number of blocks and terms in any given LMI. LMI systems of arbitrary complexity can therefore, be defined in the LMI Lab.

The LMI Lab offers tools to specify, manipulate, and numerically solve LMIs. Its main purpose is to

Allow for straightforward description of LMIs in their natural block-matrix form

Provide easy access to the LMI solvers (optimization codes)

Facilitate result validation and problem modification

The structure-oriented description of a given LMI system is
stored as a single vector called the *internal representation* and
generically denoted by `LMISYS`

in the sequel. This
vector encodes the structure and dimensions of the LMIs and matrix
variables, a description of all LMI terms, and the related numerical
data. It must be stressed that you need not attempt to read or understand
the content of `LMISYS`

since all manipulations involving
this internal representation can be performed in a transparent manner
with LMI-Lab tools.

The LMI Lab supports the following functionalities:

LMI systems can be either specified as symbolic matrix expressions
with the interactive graphical user interface `lmiedit`

,
or assembled incrementally with the two commands `lmivar`

and `lmiterm`

.
The first option is more intuitive and transparent while the second
option is more powerful and flexible.

The interactive function `lmiinfo`

answers
qualitative queries about LMI systems created with `lmiedit`

or `lmivar`

and `lmiterm`

. You can also use `lmiedit`

to visualize the LMI system produced
by a particular sequence of `lmivar`

/`lmiterm`

commands.

General-purpose LMI solvers are provided for the three generic
LMI problems defined in LMI Applications. These solvers can handle very
general LMI systems and matrix variable structures. They return a
feasible or optimal vector of decision variables *x**.
The corresponding values $${X}_{1}^{*},\dots ,{X}_{K}^{*}$$ of
the matrix variables are given by the function `dec2mat`

.

The solution *x** produced by the LMI solvers
is easily validated with the functions `evallmi`

and `showlmi`

. This allows a fast check and/or
analysis of the results. With `evallmi`

,
all variable terms in the LMI system are evaluated for the value *x**
of the decision variables. The left and right sides of each LMI then
become constant matrices that can be displayed with `showlmi`

.

An existing system of LMIs can be modified in two ways:

An LMI can be removed from the system with

`dellmi`

.A matrix variable

*X*can be deleted using`delmvar`

. It can also be instantiated, that is, set to some given matrix value. This operation is performed by`setmvar`

and allows, for example, to fix some variables and solve the LMI problem with respect to the remaining ones.

Was this topic helpful?