Harmonic model predictive control for tracking sinusoidal references and its application to trajectory tracking

TL;DR

This paper extends harmonic model predictive control (HMPC) to effectively track sinusoidal and periodic references, enhancing performance and domain of attraction without increasing computational complexity related to the reference period.

Contribution

The paper introduces an extension of HMPC for sinusoidal reference tracking, maintaining low complexity and improved performance for arbitrary trajectories.

Findings

HMPC effectively tracks sinusoidal references.

The approach maintains low computational complexity.

Closed-loop results show improved tracking performance.

Abstract

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extension of the HMPC formulation to track periodic harmonic/sinusoidal references and discusses its use for tracking arbitrary trajectories. The proposed formulation inherits the benefits of its predecessor, namely its good performance and large domain of attraction when using small prediction horizons, and that the complexity of its optimization problem does not depend on the period of the reference. We show closed-loop results discussing its performance and comparing it to other MPC formulations.