Preview Reference Governors: A Constraint Management Technique for Systems With Preview Information

TL;DR

This paper introduces the Preview Reference Governor (PRG), a constraint management method that leverages preview information to improve control performance while maintaining computational efficiency, with extensions for multi-horizon and disturbance handling.

Contribution

The paper proposes the PRG technique, extending Scalar Reference Governors to incorporate preview information, and introduces Multi-horizon PRG to address performance issues with large preview horizons.

Findings

PRG outperforms SRG in constraint enforcement with preview info.

Multi-horizon PRG improves performance for large preview horizons.

Extensions enable handling disturbances and multi-input systems.

Abstract

This paper presents a constraint management strategy based on Scalar Reference Governors (SRG) to enforce output, state, and control constraints while taking into account the preview information of the reference and/or disturbances signals. The strategy, referred to as the Preview Reference Governor (PRG), can outperform SRG while maintaining the highly-attractive computational benefits of SRG. However, as it is shown, the performance of PRG may suffer if large preview horizons are used. An extension of PRG, referred to as Multi-horizon PRG, is proposed to remedy this issue. Quantitative comparisons between SRG, PRG, and Multi-horizon PRG on a one-link robot arm example are presented to illustrate their performance and computation time. Furthermore, extensions of PRG are presented to handle systems with disturbance preview and multi-input systems. The robustness of PRG to parametric uncertainties and inaccurate preview information is also explored.