# Control Systems/Matrix Operations

## Contents

## Laws of Matrix Algebra[edit]

Matrices must be compatible sizes in order for an operation to be valid:

- Addition
- Matrices must have the same dimensions (same number of rows, same number of columns). Matrix addition is commutative:

- Multiplication
- Matrices must have the same inner dimensions (the number of columns of the first matrix must equal the number of rows in the second matrix). For instance, if matrix A is
*n*×*m*, and matrix B is*m*×*k*, then we can multiply: - Where C is an
*n*×*k*matrix. Matrix multiplication is not commutative: - Because it is not commutative, the differentiation must be made between "multiplication on the left", and "multiplication on the right".
- Division
- There is no such thing as division in matrix algebra, although multiplication of the matrix inverse performs the same basic function. To find an inverse, a matrix must be nonsingular, and must have a non-zero determinant.

## Transpose Matrix[edit]

The transpose of a matrix, denoted by:

is the matrix where the rows and columns of X are interchanged. In some instances, the transpose of a matrix is denoted by:

This shorthand notation is used when the superscript T applied to a large number of matrices in a single equation, and the notation would become too crowded otherwise. When this notation is used in the book, derivatives will be denoted explicitly with:

## Determinant[edit]

The determinant of a matrix it is a scalar value. It is denoted similarly to absolute-value in scalars:

A matrix has an inverse if the matrix is square, and if the determinant of the matrix is non-zero.

## Inverse[edit]

The inverse of a matrix A, which we will denote here by "B" is any matrix that satisfies the following equation:

Matrices that have such a companion are known as "invertible" matrices, or "non-singular" matrices. Matrices which do not have an inverse that satisfies this equation are called "singular" or "non-invertable".

An inverse can be computed in a number of different ways:

- Append the matrix A with the Identity matrix of the same size. Use row-reductions to make the left side of the matrice an identity. The right side of the appended matrix will then be the inverse:
- The inverse matrix is given by the adjoint matrix divided by the determinant. The adjoint matrix is the transpose of the cofactor matrix.
- The inverse can be calculated from the Cayley-Hamilton Theorem.

## Eigenvalues[edit]

The eigenvalues of a matrix, denoted by the Greek letter lambda λ, are the solutions to the characteristic equation of the matrix:

Eigenvalues only exist for square matrices. Non-square matrices do not have eigenvalues. If the matrix X is a real matrix, the eigenvalues will either be all real, or else there will be complex conjugate pairs.

## Eigenvectors[edit]

The eigenvectors of a matrix are the nullspace solutions of the characteristic equation:

There are is least one distinct eigenvector for every distinct eigenvalue. Multiples of an eigenvector are also themselves eigenvectors. However, eigenvalues that are not linearly independent are called "non-distinct" eigenvectors, and can be ignored.

## Left-Eigenvectors[edit]

Left Eigenvectors are the right-hand nullspace solutions to the characteristic equation:

These are also the rows of the inverse transition matrix.

## Generalized Eigenvectors[edit]

In the case of repeated eigenvalues, there may not be a complete set of *n* distinct eigenvectors (right or left eigenvectors) associated with those eigenvalues. Generalized eigenvectors can be generated as follows:

Because generalized eigenvectors are formed in relation to another eigenvector or generalize eigenvectors, they constitute an ordered set, and should not be used outside of this order.

## Transformation Matrix[edit]

The transformation matrix is the matrix of all the eigenvectors, or the ordered sets of generalized eigenvectors:

The inverse transition matrix is the matrix of the left-eigenvectors:

A matrix can be diagonalized by multiplying by the transition matrix:

Or:

If the matrix has an incomplete set of eigenvectors, and therefore a set of generalized eigenvectors, the matrix cannot be diagonalized, but can be converted into Jordan canonical form:

## MATLAB[edit]

The MATLAB programming environment was specially designed for matrix algebra and manipulation. The following is a brief refresher about how to manipulate matrices in MATLAB:

- Addition
- To add two matrices together, use a plus sign ("+"):

C = A + B;

- Multiplication
- To multiply two matrices together use an asterisk ("*"):

C = A * B;

- If your matrices are not the correct dimensions, MATLAB will issue an error.
- Transpose
- To find the transpose of a matrix, use the apostrophe (" ' "):

C = A';

- Determinant
- To find the determinant, use the
**det**function:

d = det(A);

- Inverse
- To find the inverse of a matrix, use the function
**inv**:

C = inv(A);

- Eigenvalues and Eigenvectors
- To find the eigenvalues and eigenvectors of a matrix, use the
**eig**command:

[E, V] = eig(A);

- Where E is a square matrix with the eigenvalues of A in the diagonal entries, and V is the matrix comprised of the corresponding eigenvectors. If the eigenvalues are not distinct, the eigenvectors will be repeated. MATLAB will not calculate the generalized eigenvectors.
- Left Eigenvectors
- To find the left eigenvectors, assuming there is a complete set of distinct right-eigenvectors, we can take the inverse of the eigenvector matrix:

[E, V] = eig(A); C = inv(V);

The rows of C will be the left-eigenvectors of the matrix A.

For more information about MATLAB, see the wikibook MATLAB Programming.