# why lsim return empty states?

3 vues (au cours des 30 derniers jours)
Mahmoud Elzouka le 28 Juil 2023
Commenté : Paul le 28 Juil 2023
I have build a sparse state space system, and solved using lsim. According to the documentation, the third output represent the state trajectory.
When I run this example, the third output is empty.
Is there anything wrong I am doing? how can I get the states with time?
Thanks!
% inputs defining sparse state space system size
N_inputs = 41;
N_outputs = 240;
N_states = 2400;
N_time_steps = 10;
% defining the sparse state space system
G = sprand(N_states, N_states, 0.01);
B = sprand(N_states, N_inputs, 0.01);
E = sprand(N_outputs, N_states, 0.01);
D = 0;
C = sprand(N_states, N_states, 0.01);
sys = sparss(G, B, E, D, C);
% solving
t = linspace(0,1,N_time_steps);
[y,tOut,x] = lsim( ...
sys, ...
rand(N_inputs, length(t)), ...
t);
% check the size of each output
disp(size(y))
10 240
disp(size(tOut))
10 1
disp(size(x)) % why this is empty? how can I force lsim to return it?
0 0
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### Réponse acceptée

Paul le 28 Juil 2023
Modifié(e) : Paul le 28 Juil 2023
According to the lsim doc page, the output x is returned when the input sys is a state-space model. The doc page further distinguishes between the input sys being a sparss model and an ss model. So I think that state trajectories are just returned as empty for sparss input. You can also see a comment that says exactly this if you drill down far enough into lsim in the debugger.
dbtype(fullfile(matlabroot,'toolbox/shared/controllib/engine/+ltipack/@spssdata/lsim.m'),'1:8')
1 function [y,x] = lsim(D,u,t,x0,~) 2 % Linear response simulation of state-space model with real coefficients. 3 % RE: Assume U is Ns x Nu and T has Ns samples. 4 5 % Author: P. Gahinet 6 % Copyright 2020 The MathWorks, Inc. 7 [ny,nu] = iosize(D); 8 x = []; % never compute X for sparse
In theory, at least, we can augment the output equation to include the states.
rng(100)
N_inputs = 41;
N_outputs = 240;
N_states = 2400;
N_time_steps = 10;
% defining the sparse state space system
G = sprand(N_states, N_states, 0.01);
B = sprand(N_states, N_inputs, 0.01);
E = sprand(N_outputs, N_states, 0.01);
D = 0;
C = sprand(N_states, N_states, 0.01);
% augment
E = [E; sparse(eye(N_states))];
sys = sparss(G, B, E, D, C);
% solving
t = linspace(0,1,N_time_steps);
[y,tOut,x] = lsim( ...
sys, ...
rand(N_inputs, length(t)), ...
t);
% collect states and outputs
x = y(:,N_outputs+1:end);
y = y(:,1:N_outputs);
size(y)
ans = 1×2
10 240
size(x)
ans = 1×2
10 2400
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Paul le 28 Juil 2023
The doc could definitely be more clear in this regard. If you feel motivated, you can click on a star rating at the bottom of the doc page, after which I think that a window will pop-up where you can provide feedback to the doc writers.

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