Mamduhi, M.H.; Champati, J.P.; Gross, J.; Johansson, K.H. Where Freshness Matters in the Control Loop: Mixed Age-of-Information and Event-Based Co-Design for Multi-Loop Networked Control Systems. J. Sens. Actuator Netw.2020, 9, 43.
Mamduhi, M.H.; Champati, J.P.; Gross, J.; Johansson, K.H. Where Freshness Matters in the Control Loop: Mixed Age-of-Information and Event-Based Co-Design for Multi-Loop Networked Control Systems. J. Sens. Actuator Netw. 2020, 9, 43.
Mamduhi, M.H.; Champati, J.P.; Gross, J.; Johansson, K.H. Where Freshness Matters in the Control Loop: Mixed Age-of-Information and Event-Based Co-Design for Multi-Loop Networked Control Systems. J. Sens. Actuator Netw.2020, 9, 43.
Mamduhi, M.H.; Champati, J.P.; Gross, J.; Johansson, K.H. Where Freshness Matters in the Control Loop: Mixed Age-of-Information and Event-Based Co-Design for Multi-Loop Networked Control Systems. J. Sens. Actuator Netw. 2020, 9, 43.
Abstract
In the design of multi-loop Networked Control Systems (NCSs) wherein each control system is characterized by heterogeneous dynamics and associated with certain set of timing specifications and constraints, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. Majority of the design approaches for sampling, scheduling and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic event-based metric. As the NCS model, we consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared-resource communication network. We investigate the co-design across the NCS, and discuss the pros and cons with AoI and ET approaches in terms of asymptotic control performance measured by linear-quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic event-triggered metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with pure randomized policies. Our theoretical analyses are successfully validated through several simulation scenarios.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
