0. Title
- Cautious Model Predictive Control using Gaussian Process Regression
1. Authors
- Lukas Hewing
2. Abstract
- There is also an MPC approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a Gaussian Process.
- It 's a principled way of formulating the chance constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control.
3. Motivation
- To safely enhance performance of the system.
4. Contributions(Findings)
- While combining GP dynamics with the nominal system, only a part of the dynamics can be learned. Through this, real-time control is possible by reducing computational complexity.
5. Methodology
- the design of an MPC controller for system using a GP approximation $d$ of the unknown function
6. Measurements
- sampling times, performance, safety
7. Limitations(If it's not written, think about it)
-
8. Potential Gap
-
