Average Communication Rate for Event-Triggered Stochastic Control Systems

TL;DR

This paper introduces analytical and numerical methods to accurately compute the average communication rate in event-triggered stochastic control systems, addressing nonlinearity and non-stationarity issues overlooked by previous simplified models.

Contribution

It develops a recursive model for precise ACR prediction in NET-SCS, improving upon prior Gaussian-based approximations and validating the approach through theoretical and experimental analysis.

Findings

Proposed methods accurately predict ACR in NET-SCS.

Significant deviation identified in conventional Gaussian approximation.

Validated improvements through experimental studies.

Abstract

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the nonlinear statistics propagation of the network communication status brought up by deterministic thresholds makes the precise computation of ACR difficult. Previous work used to over-simplify the computation using a Gaussian distribution without incorporating this nonlinearity, leading to sacrificed precision. This paper proposes both analytical and numerical approaches to predict the exact ACR for a NET-SCS using a recursive model. We use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation.