Predictor-based networked control under uncertain transmission delays

TL;DR

This paper develops predictor-based control strategies for networked control systems with uncertain, large time-varying delays, demonstrating improved stability and reduced errors through adaptive predictor models and event-triggered control.

Contribution

It introduces a novel analysis of delay uncertainties in predictor-based control and proposes adaptive event-triggered controllers tailored to delay conditions.

Findings

Predictor-based control can stabilize systems with large uncertain delays.

Adaptive predictor models reduce control errors under varying delays.

The approach is effective for systems like inverted pendulums with network-induced delays.

Abstract

We consider state-feedback predictor-based control of networked control systems with large time-varying communication delays. We show that even a small controller-to-actuators delay uncertainty may lead to a non-small residual error in a networked control system and reveal how to analyze such systems. Then we design an event-triggered predictor-based controller with sampled measurements and demonstrate that, depending on the delay uncertainty, one should choose various predictor models to reduce the error due to triggering. For the systems with a network only from a controller to actuators, we take advantage of the continuously available measurements by using a continuous-time predictor and employing a recently proposed switching approach to event-triggered control. By an example of an inverted pendulum on a cart we demonstrate that the proposed approach is extremely efficient when the uncertain time-varying network-induced delays are too large for the system to be stabilizable without a predictor.