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 signal. 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. Finally, novel extensions of PRG are presented to handle systems with uncertain preview information, systems with disturbance preview, and systems with multiple reference signals.

本文提出了一种基于标量参考调速器（SRG）的约束管理策略，以在考虑参考信号的预览信息的同时强制执行输出，状态和控制约束。该策略被称为预览参考调速器（PRG），可以在保持SRG高度吸引人的计算优势的同时，胜过SRG。但是，如图所示，如果使用较大的预览范围，PRG的性能可能会受到影响。为了解决此问题，建议将PRG的扩展称为多对视PRG。提出了在单链接机械臂示例上SRG，PRG和多水平PRG之间的定量比较，以说明它们的性能和计算时间。最后，提出了PRG的新颖扩展，以处理具有不确定预览信息的系统，具有干扰预览的系统，