Predictor-based compensators for networked control systems with stochastic delays and sampling intervals

The stochastic nature of time delays in networked control systems poses significant challenges for controller synthesis and the corresponding analyses, leading to conservative designs and degraded performance. Existing approaches approximate stochastic delays by fixed, worst-case values, which limits their ability to describe the behavior of distributed control implementations, including distributed computational architectures, multi-core processing, and network communication between different computational nodes. Thus, with regard to this context, this work proposes a novel modeling framework for linear multiple-input multiple-output networked control systems that represents stochastic sampling instants and delays through a stochastic linear time-varying state-space model. Based on this model, a predictor-based compensator derived from the filtered Smith predictor is developed to mitigate the effects of stochastic time delays. Using a cooperative adaptive cruise control benchmark, the proposed compensator is compared with a baseline fixed-delay predictor in terms of performance degradation, with all metrics normalized relative to the delay-free case. Numerical results indicate that the proposed method achieves a 55% reduction in the worst-case tracking error energy with respect to the baseline controller, and a 65% reduction in the worst-case control effort.