Safe Controller Optimization for Quadrotors with Gaussian Processes

Understand

One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters.

• Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance.
• To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used.
• However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen.