  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.
