# Inverse Lyapunov Procedure

## Contents |

## Description

The **Inverse Lyapunov Procedure** (ilp) is a synthetic random linear system generator.
It is based on reversing the Balanced Truncation procedure and was developed in ^{[1]}, where a description of the algorithm is given.
In aggregate form, for randomly generated controllability and observability gramians, a balancing transformation is computed.
The balanced gramian is the basis for an associated state-space system, which is determined by solving a Lyapunov equation and then unbalanced.
A central point is the solution of the Lyapunov equations for the system matrix instead of the gramian matrix.
This is feasable due to the symmetric (semi-)positive definiteness of the gramians and the requirement for a stable system, yet with a non-unique solution.

## Implementation

An efficient approach to solving the Lyapunov equation is provided by empirical gramians.

## Usage

Use the following matlab code to generate a random system as described above:

function [A B C] = ilp(J,N,O,s,r) % ilp (inverse lyapunov procedure) % by Christian Himpe, 2013-2014 ( http://gramian.de ) % released under BSD 2-Clause License ( http://gramian.de/#license ) %* if(exist('emgr')~=2) disp('emgr framework is required. Download at http://gramian.de/emgr.m'); return; end if(nargin==5) rand('seed',r); randn('seed',r); end; % Gramian Eigenvalues WC = exp( rand(N,1) ); WO = exp( rand(N,1) ); % Gramian Eigenvectors [P S Q] = svd(randn(N)); % Balancing Transformation WC = P*diag(WC)*P'; WO = Q*diag(WO)*Q'; [U D V] = svd(WC*WO); % Input and Output B = randn(N,J); if(nargin>=4 && s~=0), C = B'; else, C = randn(O,N); end % Scale Output Matrix BB = sum(B.*B,2); % = diag(B*B') CC = sum(C.*C,1)'; % = diag(C'*C) SC = sqrt(BB./CC)'; C = bsxfun(@times,C,SC); % Solve System Matrix f = @(x,u,p) -D*x+B*u; g = @(x,u,p) C*x; A = -emgr(f,g,[J N O],[0 0.01 1],'c') - (1e-13)*eye(N); % Unbalance System A = V*A*U'; B = V*B; C = C*U';

The function call requires three parameters; the number of inputs , of states and outputs . Optionally, a symmetric system can be enforced with the parameter . For reproducibility, the random number generator seed can be controlled by the parameter . The return value consists of three matrices; the system matrix , the input matrix and the output matrix .

[A,B,C] = ilp(J,N,O,s,r);

**ilp** is compatible with MATLAB and OCTAVE and the matlab code can be downloaded from: ilp.m.
The Empirical Gramian Framework can be obtained at http://gramian.de.

## References

- ↑ S.C. Smith, J. Fisher, "On generating random systems: a gramian approach", Proceedings of the American Control Conference, 2003.