Cloud-based computational model predictive control using a parallel multi-block ADMM approach

TL;DR

This paper introduces a cloud-based NMPC framework utilizing a novel parallel multi-block ADMM algorithm to address the computational challenges of solving large-scale nonconvex problems in real-time control.

Contribution

It presents an innovative parallel multi-block ADMM algorithm specifically designed for nonconvex NMPC problems, enabling more efficient real-time control in large-scale systems.

Findings

Enhanced computational efficiency for nonconvex NMPC problems

Demonstrated real-time feasibility improvements

Applicable to large-scale systems with nonlinear constraints

Abstract

Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multi-block alternating direction method of multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints.