Calculation of dead time and time constant for non linear system

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Ahmed Abdelfattah
回答： Ashim 2017-9-22
Hello ,
I am simulating a non linear system on simulink . I used the scope to show the output and saved the output signal with time to the workspace . Is there any function to calculate the dead time and time constant given the output with time ?

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回答（3 个）

surya
surya 2012-3-9
Calculation of dead time is a bit debatable aspect, you might want to look at a data based approach, like system identification.. System delay calculation. Might not be very accurate.. but gives an approximate idea.
Rajiv Singh
Rajiv Singh 2012-3-16
Delays are not necessarily separable from system dynamics such as effect of poles and zeros on the response. However, in many cases, DELAYEST in System Identification Toolbox works fine to deliver delays as number of multiples of data sample time. See also http://www.mathworks.com/products/sysid/demos.html?file=/products/demos/shipping/ident/iddemo3.html
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Ashim
Ashim 2017-9-22
you can easily perform the time constant analysis on the output, if you know the objective non-linear function
options = optimoptions(...); % select the options that match your data
objfcn = @(y,t) y(1) + y(2)*(1-exp(y(3)*t)) - yobs; % objective function for time constant where y2 is usually the differential. use Taylor series to construct such an equation
y0 = [y10, y20, y30]; initial guesses
lb = [10, 10, 1]...; %lower bound
ub = [100, 100, 10]; % upper bound
[haty, resnorm, res, output, exitflag] = lsqnonlin(objfcn, y0, lb,
ub, options); non-linear least squares fitting

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Ashim
Ashim 2017-9-22
you can easily perform the time constant analysis on the output, if you know the objective non-linear function
options = optimoptions(...); % select the options that match your data
objfcn = @(y,t) y(1) + y(2)*(1-exp(y(3)*t)) - yobs; % objective function for time constant where y2 is usually the differential. use Taylor series to construct such an equation
y0 = [y10, y20, y30]; initial guesses
lb = [10, 10, 1]...; %lower bound
ub = [100, 100, 10]; % upper bound
[haty, resnorm, res, output, exitflag] = lsqnonlin(objfcn, y0, lb,
ub, options); non-linear least squares fitting

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