Data-driven Distributed and Localized Model Predictive Control

Abstract

Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized directly from trajectory data. Our method, data-driven Distributed and Localized Model Predictive Control (D 3 LMPC), builds upon the data-driven System Level Synthesis (SLS) framework, which allows one to parameterize closed-loop system responses directly from collected open-loop trajectories. The resulting model-predictive controller can be implemented with distributed computation and only local information sharing. By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system. Moreover, we show that our algorithm enjoys theoretical guarantees for recursive feasibility and asymptotic stability. Finally, we also demonstrate the optimality and scalability of our algorithm in a simulation experiment. * Denotes equal contribution.

Online Adversarial Stabilization of Unknown Networked Systems

“…We remark that the pseudo-inverse condition in Lemma 7 is equivalently to Assumption 5, as observed in Alonso et al (2021); Yu et al (2020); Anderson and Matni (2017).…”

“…Among these, Fattahi et al (2020) demonstrates a first use case of SLS theory in learning-based distributed controller learning. However, most prior work that considers distributed learning and control schemes use the stochastic noise or no-noise model, assume a known stabilizing distributed controller is given, and cannot handle general communication delay during learning, e.g., Li et al (2021b); Alonso et al (2021); Jing et al (2021); Alemzadeh and Mesbahi (2019); Talebi et al (2021a); Alemzadeh et al (2021). Ho and Doyle (2019) presents an adaptive SLS controller but requires a known stabilizing controller and does not have guaranteed stability for large uncertainties.…”

Data-driven distributed MPC of dynamically coupled linear systems

“…We consider the same example as in Alonso et al (2021): a system comprising a chain of 64 subsystems with dynamics…”

“…Further references may be found in the survey Markovsky and Dörfler (2021). Recently, extensions to data-driven distributed MPC for dynamically coupled systems with stability guarantees Allibhoy and Cortés (2021); Alonso et al (2021) have been made, which require state measurements and state coupling. Both employ iterative distributed optimisation, requiring a multitude of communication at each time step.…”