An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control

TL;DR

This paper reviews sensitivity-based distributed optimization and model predictive control, highlighting their development, application to static and dynamic problems, and introducing a real-time distributed MPC validated on a water tank system.

Contribution

It provides a comprehensive overview of sensitivity-based methods and introduces a real-time distributed MPC validated through experimental testing.

Findings

Effective coordination via local computations and neighbor communication

Successful real-time distributed MPC implementation on a coupled water tank system

Historical development overview of sensitivity-based optimization methods

Abstract

This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and primal decomposition to achieve coordination between the subsystems while requiring only local computations with neighbor-to-neighbor communication. We give a brief historical synopsis of its development and apply it to both static and dynamic optimization problems. Furthermore, a real-time capable distributed model predictive controller is proposed which is experimentally validated on a coupled watertank system.