The control of dynamical systems is the backbone of modern technologies, ranging from industrial processes to autonomous vehicles. In many of these scenarios, systems must be controlled while satisfying a set of safety and reliability constraints concerning the unknown evolution of a target process.
In our recent work[1], we proposed Probabilistic Time Series-Conformal Risk Prediction (PTS-CRC), a novel calibration procedure that enables reliable modelling of uncertainty regarding future system states. PTS-CRC predictions can be used to solve model predictive control problems under reliability, safety, and performance constraints. [...]