The Maximal Robust Positively Invariant Set for Linear Difference Inclusions

Abstract

This article develops a systematic characterization and exact computational framework for the maximal robust positively invariant set of linear difference inclusions subject to hard state constraints. The set is characterized as the greatest fixed point of the robust predecessor operator, leading to three equivalent decreasing set iterations---the standard, self-restricted, and incremental iterations---and exact stopping tests. Under uniform exponential stability of the matrix family and bounded disturbances, the limiting disturbance-reachable set is characterized and used to derive conditions for nonemptiness and finite determination. When the state constraint set is polyhedral, the three iterations admit exact half-space implementations for bounded disturbances and finite or polytopic matrix families. Although these implementations generate the same set sequence, they distribute computational effort differently. The complexity analysis and numerical study make these differences explicit and reveal how the underlying problem structure informs the choice of implementation.