Delay-sensitive Joint Optimal Control and Resource Management in Multi-loop Networked Control Systems

TL;DR

This paper presents a cross-layer optimal control and resource management framework for multi-loop networked control systems with delay sensitivities, balancing control performance and communication costs in a shared, variable-latency network.

Contribution

It introduces a novel joint design of control, sampling, and resource policies considering delay sensitivities and heterogeneous latency requirements in multi-system NCSs.

Findings

Optimal policies improve control performance and resource utilization.

Higher cross-layer awareness enhances system performance.

The framework adapts to constant and time-varying network parameters.

Abstract

In the operation of networked control systems, where multiple processes share a resource-limited and time-varying cost-sensitive network, communication delay is inevitable and primarily influenced by, first, the control systems deploying intermittent sensor sampling to reduce the communication cost by restricting non-urgent transmissions, and second, the network performing resource management to minimize excessive traffic and eventually data loss. In a heterogeneous scenario, where control systems may tolerate only specific levels of sensor-to-controller latency, delay sensitivities need to be considered in the design of control and network policies to achieve the desired performance guarantees. We propose a cross-layer optimal co-design of control, sampling and resource management policies for an NCS consisting of multiple stochastic linear time-invariant systems which close their sensor-to-controller loops over a shared network. Aligned with advanced communication technology, we assume that the network offers a range of latency-varying transmission services for given prices. Local samplers decide either to pay higher cost to access a low-latency channel, or to delay sending a state sample at a reduced price. A resource manager residing in the network data-link layer arbitrates channel access and re-allocates resources if link capacities are exceeded. The performance of the local closed-loop systems is measured by a combination of linear-quadratic Gaussian cost and a suitable communication cost, and the overall objective is to minimize a defined social cost by all three policy makers. We derive optimal control, sampling and resource allocation policies under different cross-layer awareness models, including constant and time-varying parameters, and show that higher awareness generally leads to performance enhancement at the expense of higher computational complexity.