Efficiently solving the harmonic model predictive control formulation

TL;DR

This paper introduces an efficient method for solving harmonic model predictive control problems with second order cone constraints, enabling real-time implementation suitable for embedded systems.

Contribution

It presents a novel operator splitting approach for HMPC that reduces computation time, making it comparable to linear MPC solvers.

Findings

The proposed solver outperforms existing methods in speed.

HMPC can be implemented in embedded systems.

The method maintains control performance with reduced computation.

Abstract

Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation which displays several benefits over other MPC formulations, especially when using a small prediction horizon. These benefits, however, come at the expense of an optimization problem that is no longer the typical quadratic programming problem derived from most linear MPC formulations due to the inclusion of a particular class of second order cone constraints. This article presents a method for efficiently dealing with these constraints in operator splitting methods, leading to a computation time for solving HMPC in line with state of the art solvers for linear MPC. We show how to apply this result to the alternating direction method of multipliers algorithm, presenting a solver which we compare against other solvers from the literature, including solvers for other linear MPC formulations. The results show that the proposed solver, and by extension the HMPC formulation, is suitable for its implementation in embedded systems.