Constraint-Tightening and Stability in Stochastic Model Predictive Control

TL;DR

This paper develops a stochastic model predictive control method that balances constraint tightening for feasibility and stability, providing guarantees on performance and convergence, with demonstrated advantages over classical approaches.

Contribution

It introduces a unified stochastic MPC algorithm that explicitly manages the trade-off between feasible region size and stability guarantees, including asymptotic stability in probability.

Findings

Proposes a novel stochastic MPC algorithm with adjustable feasibility and stability trade-offs.

Proves asymptotic stability in probability under mild assumptions.

Demonstrates improved performance over classical stochastic and robust MPC in numerical tests.

Abstract

Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are considered separately, highlighting the difference between existence of a solution and feasibility of a suitable, a priori known candidate solution. Subsequently, a Stochastic Model Predictive Control algorithm which unifies previous results is derived, leaving the designer the option to balance an increased feasible region against guaranteed bounds on the asymptotic average performance and convergence time. Besides typical performance bounds, under mild assumptions, we prove asymptotic stability in probability of the minimal robust positively invariant set obtained by the unconstrained LQ-optimal controller. A numerical example, demonstrating the efficacy of the proposed approach in comparison with classical, recursively feasible Stochastic MPC and Robust MPC, is provided.