Casadi MHE optimation

[pegah darabi]

Hello friends.

I have used Casadi for the moving horizon estimation optimization method and there is an error in the solver:

'gradient' only defined for scalar outputs: Use 'jacobian' instead.

 Casadi is completely new to me and I do not know how I can solve this error. If anyone has experience in this field please help me.

[Joris Gillis]

Dear Pegah,

This indicates that your optimization objective is not scalar.

Please check its dimensions with `size(.)` in Matlab or `*.shape` in Python.

Best regards,

  Joris

[pegah darabi]

Hi Joris, 

Thanks for answering so quickly. I did not understand what you mean. I wrote the program in MATLAB. Which size of variable should I check? My optimization constrains are three equations. And I want to estimate the modes of my control system. 

should I send my Code?
Best regards,

pegah

[Joris Gillis]

An objective is an expression that should be minimized by the solver, by means of adapting the decision variables (unknowns) in the problem. In an estimation problem, and objective is typically a sum of squared terms. If you omit the sum, you end up with a non-scalar expression.

Best,

  Joris

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[Andrea Tuveri]

Hi Joris, 

I have a similar problem when I try to implement direct collocation for the MHE. Here you find the code ( https://gist.github.com/detu92/59b4f5554b6543d7f4c2bd574aef5c03 ).

I create the solver outside of the loop and then call it when measurements are available. Everything works fine when I give a constant state noise (line 78), that then enters in the objective in line 218.

Considering that for my process  constant state noise would be a very strong assumption, I wanted to have it variable and I tought about defining it as in line 142-143 and then give it to the solver as a variable in line 250. However, when I do so, I get the same error that Pegah mentioned:

"ex.is_scalar()" failed:

'gradient' only defined for scalar outputs: Use
'jacobian' instead.

I am very new to CasAdi, so I am probably doing something wrong. Is there a way you can help me with solving that? :)

Best regards,

Andrea

[Marc-Simon Schäfer]

Hi Andrea,

you can do this in a time-variable way by appending W in your parameter vector in line 250. Something like

'p', vertcat(Y_in, U_in, X0_var, P_mat_in, W)

Later in your online-code just fill your updated W in the parameter vector.

Best regards,

Marc-Simon

[Andrea Tuveri]

Hei Marc-Simon,

Thanks for the replay. 

Yes this is what I previously did, but I have got the same error message as Pegah mentioned before in this conversation 

('"ex.is_scalar()" failed:

'gradient' only defined for scalar outputs: Use

'jacobian' instead).

Berst regards,

Andrea

[Marc-Simon Schäfer]

Hi Andrea,

as Joris mention above: check your objective function if it is a scalar. Especially if line 218 results in a scalar expression. Looks like [1 x n ] * [n x n].

Best regards

[Andrea Tuveri]

Hei Marc-Simon,

Thanks a lot again! Yes, there was a mistake from my side in the dimension of W, (line 142). Now everything works.

Best,

Andrea