Parallelized Robust Distributed Model Predictive Control in the Presence of Coupled State Constraints

TL;DR

This paper introduces a parallelized, robust distributed model predictive control scheme for nonlinear systems with coupled state constraints, enabling convex optimization and robustness against uncertainties through neighbor communication and iterative trajectory refinement.

Contribution

It presents a novel parallelized DMPC approach that handles coupled constraints and non-convex states, with convex reformulation and robustness via tubes, demonstrated through simulations.

Findings

Effective in handling coupled and non-convex constraints

Enables convex optimization for linear systems with non-convex states

Robust against bounded uncertainties using tube-based methods

Abstract

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the subsystem is guaranteed to stay in. Reference trajectories and consistency constraints are known to neighboring subsystems. Contrary to other relevant approaches, the reference trajectories are improved consecutively. The presented approach allows the formulation of convex optimization problems for systems with linear dynamics even in the presence of non-convex state constraints. Additionally, we employ tubes in order to ensure the controller's robustness against bounded uncertainties. In the end, we briefly comment on an iterative extension of the DMPC scheme. The effectiveness of the proposed DMPC scheme and its iterative extension are demonstrated with simulations.