Predictor-Based Compensators for Networked Control Systems with Stochastic Delays and Sampling Intervals

TL;DR

This paper introduces a novel predictor-based compensator for networked control systems with stochastic delays, significantly improving performance and reducing variability compared to traditional methods.

Contribution

It proposes a Filtered Smith Predictor-based compensation scheme tailored for stochastic delays, enhancing control performance in networked systems.

Findings

Achieves near-ideal average closed-loop performance.

Reduces response variability significantly.

Yields 45% reduction in worst-case tracking error.

Abstract

The stochastic nature of time delays and sampling intervals in Networked Control Systems poses significant challenges for controller synthesis and analysis, often leading to conservative designs and degraded performance. This work presents a modeling approach for Linear Multiple-Input Multiple-Output Networked Control Systems and introduces a compensation scheme based on the Filtered Smith Predictor to mitigate the adverse effects of stochastic time delays on closed-loop performance. The proposed scheme is evaluated through numerical simulations of a well-established Cooperative Adaptive Cruise Control system. Results demonstrate that the compensator achieves near-ideal average closed-loop performance and significantly reduces response variability compared to a traditional Filtered Smith Predictor. Notably, it yields a 45% reduction in worst-case tracking error signal energy relative to an ideal baseline system with no time delays and constant sampling intervals.