Project for the course "Statistical Learning and Stochastic Control" at University of Stuttgart
For detailed information about the project, please refer to the Presentation and Report.
Supported Matlab Version >= R2019a
Control of a Race Vehicle with unkown complex dynamics
To run the Race Car example execute:
main_singletrack.m
A Gaussian process is used to learn unmodeled dynamics
$$x_{k+1} = f_d(x_k,u_k) + B_d * ( GP(z_k) + w ) , where z_k = [Bz_x*xk ; Bz_u*uk] is the vector of selected features f_d is the dicrete nominal model w ~ N(0,\sigma_n) is the process WG noise GP is the Gaussian Process model reponsible for learning the unmodeled dynamics$$The Gaussian Process model GP is then fed with data (X,Y+w) collected online, such that:
$$X = [x_k,u_k] Y + w = pinv(B_d) * ( x_{k+1} - f_d(x_k,u_k) )$$and it is trained (hyperparameter optimization) by maximizing the log Likelihood p(Y|X,theta), where theta is the vector of hyperparameters.
Results
| NMPC controller with unmodelled dynamics | Learning-Based NMPC controller (with trained Gaussian Process) |
|---|---|
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Control of an Inverted Pendulum with deffect motor
To run the Inverted Pendulum please execute
main_invertedPendulum.m