Delay-Aware Multi-Layer Multi-Rate Model Predictive Control for Vehicle Platooning Under Message-Rate Congestion Control

Ibrahim, Amr; Goswami, Dip; Li, Hong; Basten, Twan

doi:10.1109/access.2022.3169577

Cited by 5 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the paper assumes that the dynamics of the plant are considered to be accurately known, the states are measurable, and the communication delay is bounded, which limits the applicability of this method. From a control perspective, robust controllers [103], [104] and MPC [105] are well-known to be robust against packet loss and delay in NCSs. However, the performance of these methods usually depends on the dynamic model accuracy and availability, which may strongly vary for real systems.…”

Section: A Communication Errorsmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

View full text Add to dashboard Cite

Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

show abstract

Section: A Communication Errorsmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

View full text Add to dashboard Cite

show abstract

“…An event- or scenario-based adaptation of control mechanisms is also considered in [ 26 ], where a multi-layer model predictive control for platooning is proposed, which makes use of the Dynamic Congestion Control (DCC) mechanism to adapt the messaging rate. However, the proposed adaptation relies only on a set of fixed configurations, which are selected based on the estimated channel load.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

Sroka

Ström

Svensson

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles’ motion, might be challenging in a case of heavy road traffic. Thus, in this paper, we propose to reduce the number of intra-platoon transmissions required to facilitate the safe autonomous control of vehicle mobility, by analyzing the impact of cars’ behaviors (in terms of acceleration changes) on the evolution of the inter-vehicle distance errors within the platoon. We derive formulas representing the relation between the platoon leader’s acceleration changes and the evolution of the distance error, velocity difference, and the accelerations for the first pair of vehicles. Furthermore, we propose a heuristic algorithm for selection of the intra-platoon messaging period for each platoon vehicle that minimizes the use of radio resources subject to the safety constraint, represented as the fraction of the total time when emergency braking is activated. The presented simulation results indicate that the proposed approach is capable of ensuring safe platoon operation and simultaneously providing a significant reduction in the use of resources, compared with conventional fixed-period transmission.

show abstract