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. The maximal set is characterized as the greatest fixed point of the robust predecessor operator, leading to three equivalent set iterations for its computation. Under uniform exponential stability and bounded disturbances, the Kuratowski upper limit of the disturbance-reachable sets yields a necessary and sufficient condition for the maximal set to be nonempty under closed state constraints. Sufficient conditions for its finite determination are also established, together with an explicit bound on the determination index. For polyhedral state constraints, bounded disturbances, and finite or polytopic matrix families, exact half-space implementations of the three iterations are derived. Although these algorithms generate the same set sequence, complexity analysis and numerical experiments reveal substantial differences in their computational performance.