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Difference between revisions of "Flexible Space Structures"

m (Some fixes and more explanation.)
m (→‎Data: Minor fixes)
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==Data==
 
==Data==
   
The following Matlab code assembles the above described <math>A</math>, <math>B</math> and <math>C</math> matrix for a given number of modes <math>K</math>.
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The following Matlab code assembles the above described <math>A</math>, <math>B</math> and <math>C</math> matrix for a given number of modes <math>K</math>, actuators (inputs) <math>M</math> and sensors (outputs) <math>Q</math>.
   
 
<div class="thumbinner" style="width:540px;text-align:left;">
 
<div class="thumbinner" style="width:540px;text-align:left;">
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rand('seed',1009);
 
rand('seed',1009);
 
xi = rand(1,K)*0.001; % Sample damping ratio
 
xi = rand(1,K)*0.001; % Sample damping ratio
omega = rand(1,K)*100; % Sample natural frequencies
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omega = rand(1,K)*100.0; % Sample natural frequencies
   
 
A_k = cellfun(@(p) sparse([-2.0*p(1)*p(2),-p(2);p(2),0]), ...
 
A_k = cellfun(@(p) sparse([-2.0*p(1)*p(2),-p(2);p(2),0]), ...
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</source>
 
</source>
 
</div>
 
</div>
 
   
 
==Reference==
 
==Reference==

Revision as of 10:12, 12 May 2017

Under Construction.png Note: This page has not been verified by our editors.

Description

The flexible space structure benchmark[1] is a procedural modal model which represents structural dynamics with a selectable number actuators and sensors. This model is used for truss structures in space environments i.e. the COFS-1 (Control of Flexible Structures) mast flight experiment.

Model

In modal form the flexible space structure model for K modes, M actuators and Q sensors is of second order and given by:

\ddot{\nu}(t) = (2 \xi \circ \omega) \circ \dot{\nu}(t) + (\omega \circ \omega) \circ \nu = Bu(t)
y(t) = C_r\dot{\nu}(t) + C_d\nu(t)

with the parameters \xi \in \mathbb{R}_{>0}^K (damping ratio), \omega \in \mathbb{R}_{>0}^K (natural frequency) and using the Hadamard product \circ. The first order representation follows for x(t) = (\dot{\nu}(t), \omega_1\nu_1, \dots, \omega_K\nu_K) by:

\dot{x}(t) = Ax(t) + Bu(t)
y(t) = Cx(t)

with the matrices:

A := \begin{pmatrix} A_1 & & \\ & \ddots & \\ & & A_K \end{pmatrix}, \; B := \begin{pmatrix} B_1 \\ \vdots \\ B_K \end{pmatrix}, \; C := \begin{pmatrix} C_1 & \dots & C_K \end{pmatrix},

and their components:

A_k := \begin{pmatrix} -2\xi_k\omega_k & -\omega_k \\ \omega_k & 0 \end{pmatrix}, \; B_k := \begin{pmatrix} b_k \\ 0 \end{pmatrix}, \; C_k := \begin{pmatrix} c_{rk} & \frac{c_{dk}}{\omega_k} \end{pmatrix},

where b_k \in \mathbb{R}^{1 \times M} and c_{rk}, c_{dk} \in \mathbb{R}^{Q \times 1}.


Benchmark Specifics

For this benchmark the system matrix is block diagonal and thus chosen to be sparse. The parameters \xi and math>\omega</math> are sampled from a uniform random distributions \mathcal{U}_{[0,\frac{1}{1000}]}^K and \mathcal{U}_{[0,100]}^K respectively. The components of the input matrix b_k are sampled form a uniform random distribution \mathcal{U}_{[0,1]}, while the output matrix C is sampled from a uniform random distribution \mathcal{U}_{[0,10]} completely w.l.o.g, since if the components of C_d are random their scaling can be ignored.


Data

The following Matlab code assembles the above described A, B and C matrix for a given number of modes K, actuators (inputs) M and sensors (outputs) Q.

function [A,B,C] = fss(K,M,Q)

    rand('seed',1009);
    xi = rand(1,K)*0.001;	% Sample damping ratio
    omega = rand(1,K)*100.0;	% Sample natural frequencies

    A_k = cellfun(@(p) sparse([-2.0*p(1)*p(2),-p(2);p(2),0]), ...
                  num2cell([xi;omega],1),'UniformOutput',0);

    A = blkdiag(A_k{:});

    B = kron(rand(K,M),[1;0]);

    C = 10.0*rand(Q,2*K);
end

Reference

  1. W. Gawronski and T. Williams, "Model Reduction for Flexible Space Structures", Journal of Guidance 14(1): 68--76, 1991

Contact

Christian Himpe