Safe Model Predictive Control via Reliable Time-Series Forecasting

🕒 < 1 min

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.

Consider the UAV tracking problem illustrated in Figure 1. Here, a UAV must plan its trajectory to track a group of ground users with unknown future locations. In this example, PTS-CRC predictions can replace the unknown future locations of the users and assist in designing a UAV trajectory that minimizes energy consumption while ensuring reliability in tracking errors.

Figure 1: UAV tracking problem, an example of model predictive control in which the UAV must plan its path based on the unknown evolution of the object to be tracked. PST-CRC-based model predictive control replaces the unknown future location of the tracking target with reliable time series prediction sets.


[1] Zecchin, Matteo, Sangwoo Park, and Osvaldo Simeone. “Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control.” arXiv preprint arXiv:2310.10299, 2023.