Invariance Feedback Entropy of Uncertain Control Systems
This work provides a fundamental information-theoretic limit for control under uncertainty, relevant to control theorists and engineers designing networked control systems.
The paper introduces invariance feedback entropy to quantify the minimum state information rate needed for any controller to keep a subset of the state space invariant. It proves a data rate theorem showing this entropy is a tight lower bound on the data rate of any coder-controller achieving invariance, and derives universal lower bounds for uncertain linear systems.
We introduce a novel notion of invariance feedback entropy to quantify the state information that is required by any controller that enforces a given subset of the state space to be invariant. We establish a number of elementary properties, e.g. we provide conditions that ensure that the invariance feedback entropy is finite and show for the deterministic case that we recover the well-known notion of entropy for deterministic control systems. We prove the data rate theorem, which shows that the invariance entropy is a tight lower bound of the data rate of any coder-controller that achieves invariance in the closed loop. We analyze uncertain linear control systems and derive a universal lower bound of the invariance feedback entropy. The lower bound depends on the absolute value of the determinant of the system matrix and a ratio involving the volume of the invariant set and the set of uncertainties. Furthermore, we derive a lower bound of the data rate of any static, memoryless coder-controller. Both lower bounds are intimately related and for certain cases it is possible to bound the performance loss due to the restriction to static coder-controllers by $1$ bit/time unit. We provide various examples throughout the paper to illustrate and discuss different definitions and results.