Gaussian-Process based Model Predictive Control [IN PROGRESS]
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
The Gaussian Process model GP is then fed with data (X,Y+w) collected online, such that:
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) |
|---|---|
 |
 |
Control of an Inverted Pendulum with deffect motor
To run the Inverted Pendulum please execute
main_invertedPendulum.m