Instant distributed MPC with reference governor

Abstract

Model predictive control is a popular choice for systems that must satisfy prescribed constraints on states and control inputs. Although much progress has been made in distributed model predictive control, existing algorithms tend to be computationally expensive. This limits their use in systems with fast dynamics. In this paper, we propose a new distributed model predictive control algorithm that we term as instant distributed model predictive control (iDMPC). The proposed algorithm employs a realisation of the primal-dual algorithm in the controller dynamics for fast computation. We show that the closed-loop system trajectories with the proposed iDMPC algorithm converge asymptotically to a desired reference. To ensure the satisfaction of the state constraints during the transient, we also include an explicit reference governor in the feedback loop.

Keywords:

• Distributed model predictive control
• suboptimal control
• reference governor
• dissipativity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work of Vijay Gupta and Martin Figura was partially supported by NSF Award 2300355, ARO Award 203708PU and AFOSR award 204152PU. The work of Masaki Inoue was partially supported by Grant-in-Aid for Scientific Research (B), No. ∼20H02173 from JSPS.