Conformal Prediction-Based MPC for Stochastic Linear Systems
Provides a computationally efficient, distribution-free method for stochastic MPC with joint chance constraints, addressing a key bottleneck in control under uncertainty.
Proposed a stochastic MPC framework for linear systems with joint-in-time chance constraints under unknown disturbance distributions, using conformal prediction to construct finite-sample confidence regions with minimal computation, ensuring recursive feasibility and constraint satisfaction. Numerical examples show effectiveness over existing methods.
We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches.