Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes
This work addresses safety-critical control for unknown systems, which is incremental as it builds on existing methods by integrating learning with formal guarantees.
The paper tackles the problem of ensuring safety in unknown nonlinear systems by combining Gaussian processes for learning dynamics with control barrier functions for controller synthesis, resulting in a safe controller with a rigorous lower bound on safety probability.
This paper focuses on the controller synthesis for unknown, nonlinear systems while ensuring safety constraints. Our approach consists of two steps, a learning step that uses Gaussian processes and a controller synthesis step that is based on control barrier functions. In the learning step, we use a data-driven approach utilizing Gaussian processes to learn the unknown control affine nonlinear dynamics together with a statistical bound on the accuracy of the learned model. In the second controller synthesis steps, we develop a systematic approach to compute control barrier functions that explicitly take into consideration the uncertainty of the learned model. The control barrier function not only results in a safe controller by construction but also provides a rigorous lower bound on the probability of satisfaction of the safety specification. Finally, we illustrate the effectiveness of the proposed results by synthesizing a safety controller for a jet engine example.