A Consistency Constraint-Based Approach to Coupled State Constraints in Distributed Model Predictive Control

TL;DR

This paper introduces a distributed model predictive control method for decoupled systems with state and input constraints, using consistency constraints and iterative reference trajectory improvements to ensure system stability and convex optimization feasibility.

Contribution

It proposes a novel DMPC scheme that handles non-convex state constraints and coupling constraints through consistency constraints and iterative reference updates.

Findings

Effective in maintaining state constraints in simulations

Allows convex optimization with non-convex constraints

Requires only neighbor-to-neighbor communication

Abstract

In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints. In the proposed control scheme, neighbor-to-neighbor communication suffices and all subsystems solve their local optimization problem in parallel. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the respective 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 iteratively. Besides, the presented approach allows the formulation of convex optimization problems even in the presence of non-convex state constraints. The algorithm's effectiveness is demonstrated with a simulation.