I have some questions about your thesis, but I couldn't find your email. Would you mind sending it to me?

System

Windows 10
Python 3.7.0
SciPy 1.4.1

Problem

When I run python car_example.py, I get the traceback below. I understand that the issue may raise from different SciPy version. Any Idea how to solve it? An addition of the requirements.txt for stating which version of Scipy to use this with would be good, if a solution is not available. According to LAPACK documentation, the third argument is for precision so I first thought it was a SciPy bug but the function works as intended when used separately.

Traceback

** On entry to DGEBAL parameter number  3 had an illegal value
Traceback (most recent call last):
  File "car_example.py", line 223, in <module>
    methods = ['TA', 'ME'], num_cols=1, xnames=xnames)
  File "./..\gp_mpc\gp_class.py", line 775, in predict_compare
    K, S, E = lqr(A, B, Q, R)
  File "./..\gp_mpc\mpc_class.py", line 972, in lqr
    P = np.array(scipy.linalg.solve_discrete_are(A, B, Q, R))
  File "C:\Users\Admin\Anaconda3\envs\pirl\lib\site-packages\scipy\linalg\_solvers.py", line 675, in solve_discrete_are
    _, (sca, _) = matrix_balance(M, separate=1, permute=0)
  File "C:\Users\Admin\Anaconda3\envs\pirl\lib\site-packages\scipy\linalg\basic.py", line 1588, in matrix_balance
    ''.format(-info))
ValueError: xGEBAL exited with the internal error "illegal value in argument number 3.". See LAPACK documentation for the xGEBAL error codes.

Hi, thanks for sharing the code. I have a question for the function predict_compare in file gp_class.py. In the following figure, when I use if feedback:, the code goes wrong(At t = 0.00759683, mxstep steps taken before reaching tout.). How to solve this problem? Thank you very much!

Hi Helge-Andre,

Thank you very much for sharing your framework, it's very inspiring.

I am particular to try your hybrid implementation where GP estimating modeling errors. I noticed in your code , you said f_hybrid option is not finished implemented. I was wondering what do you mean here? Can I still use your framework to reproduce Hewing2017? Thanks.

Regards,
Jie

Hi, I read the code carefully. I am a little confused about training data. I check the training data. It seems that the X, Y generated by generate_training_data function doesn't have too much difference(see the following figure: X[:30, :3]- Y[:30, :3], the data is got from car_example.py file). Why do you generate the training data like that? If I replace it with (X, X+ random noise), doesn't it make any difference ? Thank you very much!

Hi Helge-Andre, Thank you very much for sharing your framework, it's very inspiring.
I am particular to try your hybrid GP (GP model for dynamic equations, and RK4 for kinematic equations). I noticed in your code , you said "Missing kinematic states" (mpc_class.py line289) in the step of Hybrid output covariance matrix.
Will this affect the results?

For the car_example in main(and its equivalent in develop), the dynamics for second state in the dxdt are missing a delta in the last term
Code:
2*Cr*(x[1] - lf*x[2]) / (x[0] + eps))

Thesis: