Stochastic MPC with Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints

TL;DR

This paper develops a stochastic MPC framework with dynamic feedback gain selection and discounted probabilistic constraints, ensuring recursive feasibility, stability, and reduced conservativeness in controlling linear systems with disturbances.

Contribution

It introduces a novel online feedback gain selection method and a discounted chance constraint formulation for MPC, improving feasibility and performance without disturbance boundedness.

Findings

Guaranteed recursive feasibility and stability.

Reduced conservativeness through dynamic gain selection.

Expanded initial condition feasible set.

Abstract

This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating a dynamic feedback gain to minimise a quadratic cost function subject to a single chance constraint. The feedback gain is selected online and we provide two selection methods based on minimising upper bounds on predicted costs. The chance constraint is defined as a discounted sum of violation probabilities on an infinite horizon. By penalising violation probabilities close to the initial time and assigning violation probabilities in the far future with vanishingly small weights, this form of constraints allows for an MPC law with guarantees of recursive feasibility without a boundedness assumption on the disturbance. A computationally convenient MPC optimisation problem is formulated using Chebyshev's inequality and we introduce an online constraint-tightening technique to ensure recursive feasibility. The closed loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition. With dynamic feedback gain selection, the closed loop cost is reduced and conservativeness of Chebyshev's inequality is mitigated. Also, a larger feasible set of initial conditions can be obtained. Numerical simulations are given to show these results.