Stochastic Model Predictive Control for tracking of distributed linear systems with additive uncertainty

Abstract

In this paper, we propose a chance constrained stochastic model predictive control scheme for reference tracking of distributed linear time-invariant systems with additive stochastic uncertainty. The chance constraints are reformulated analytically based on mean-variance information, where we design suitable Probabilistic Reachable Sets for constraint tightening. Furthermore, the chance constraints are proven to be satisfied in closed-loop operation. The design of an invariant set for tracking complements the controller and ensures convergence to arbitrary admissible reference points, while a conditional initialization scheme provides the fundamental property of recursive feasibility. The paper closes with a numerical example, highlighting the convergence to changing output references and empirical constraint satisfaction.

Extended version of our ECC 2021 paper

References in corpus (2)

• A stochastic output-feedback MPC scheme for distributed systems
• Stochastic Model Predictive Control for tracking of distributed linear systems with additive uncertainty

Cited by in corpus (1)

• Stochastic Model Predictive Control for tracking of distributed linear systems with additive uncertainty