Invariance entropy for uncertain control systems

TL;DR

This paper introduces a new concept of invariance entropy for uncertain control systems, extending previous deterministic notions, and provides formulas and bounds for its calculation under various conditions.

Contribution

It generalizes invariance entropy to uncertain systems, relates it to existing entropy measures, and derives explicit formulas and bounds for practical computation.

Findings

Invariance entropy extends to uncertain control systems.

Bounds for entropy are derived using spectral radii.

Explicit formulas are provided for finite controlled invariant sets.

Abstract

We introduce a notion of invariance entropy for uncertain control systems, which is, roughly speaking, the exponential growth rate of "branches" of "trees" that are formed by controls and are necessary to achieve invariance of controlled invariant subsets of the state space. This entropy extends the invariance entropy for deterministic control systems introduced by Colonius and Kawan (2009). We show that invariance feedback entropy, proposed by Tomar, Rungger, and Zamani (2020), is bounded from below by our invariance entropy. We generalize the formula for the calculation of entropy of invariant partitions obtained by Tomar, Kawan, and Zamani (2020) to quasi-invariant-partitions. Moreover, we also derive lower and upper bounds for entropy of a quasi-invariant-partition by spectral radii of its adjacency matrix and weighted adjacency matrix. With some reasonable assumptions, we obtain explicit formulas for computing invariance entropy for uncertain control systems and invariance feedback entropy for finite controlled invariant sets.