State Estimation of Continuous-Time Dynamical Systems With Uncertain Inputs With Bounded Variation: Entropy, Bit Rates, and Relation With Switched Systems

In this article, we extend the notion of
estimation entropy
of autonomous dynamical systems proposed by Liberzon and Mitra to nonlinear dynamical systems with uncertain inputs with bounded variation. We call this new notion the
$\varepsilon$
-
estimation entropy
of the system and show that it lower bounds the bit rate needed for state estimation.
$\varepsilon$
-estimation entropy represents the exponential rate of the increase of the minimal number of functions that are adequate for
$\varepsilon$
-approximating any trajectory of the system. We show that alternative entropy definitions using spanning or separating trajectories bound ours from both sides. On the other hand, we show that other commonly used definitions of entropy, for example, the ones in the work of Liberzon and Mitra (2018), diverge to infinity. Thus, they are potentially not suitable for systems with uncertain inputs. We derive an upper bound on
$\varepsilon$
-estimation entropy and estimation bit rates, and evaluate it for two examples. We present a state estimation algorithm that constructs a function that approximates a given trajectory up to an
$\varepsilon$
error, given time-sampled and quantized measurements of state and input. We investigate the relation between
$\varepsilon$
-estimation entropy and a previous notion for switched nonlinear systems and derive a new upper bound for the latter, showing the generality of our results on systems with uncertain inputs.