Decentralized Event-Triggered Control for Multiple Networks Subject to Stochastic Delays and Poisson Pulsing Attacks

Core Concepts

This paper proposes a decentralized time-regularized (Zeno-free) event-triggered control strategy for networked control systems subject to stochastic network delays and Poisson pulsing denial-of-service (Pp-DoS) attacks. The proposed approach ensures stochastic stability and Zeno-freeness of the closed-loop system by integrating the effects of stochastic delays and Pp-DoS attacks into the event-triggered control design.

Abstract

The paper considers a networked control system with multiple decentralized networks operating asynchronously and independently. The networks are subject to two sources of randomness: stochastic network delays and Poisson pulsing denial-of-service (Pp-DoS) attacks. Key highlights: A novel stochastic hybrid model is established to capture the dynamics of the networked control system under stochastic delays and Pp-DoS attacks. Decentralized time-regularized (Zeno-free) event-triggered control strategies are designed to generate transmission instants for each network. The strategies are resilient to stochastic delays and Pp-DoS attacks. The proposed approach ensures stochastic stability and Zeno-freeness of the closed-loop system by integrating the effects of stochastic delays and Pp-DoS attacks into the event-triggered control design. The effectiveness of the proposed approach is demonstrated through the robust global attitude stabilization of flexible combined rotorcraft-like aerial vehicles.

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Key Insights Distilled From

Decentralized Zeno-Free Event-Triggered Control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks

by Dandan Zhang... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2401.14750.pdf

Deeper Inquiries

How can the proposed decentralized event-triggered control strategies be extended to handle more complex network topologies or communication protocols

The proposed decentralized event-triggered control strategies can be extended to handle more complex network topologies or communication protocols by incorporating adaptive mechanisms based on the specific requirements of the network. For different network topologies, the triggering conditions can be adjusted to account for varying communication delays and packet loss rates. Additionally, the event-triggered strategies can be designed to adapt to different communication protocols by considering the medium access control mechanisms and the data transmission requirements of each protocol. To handle more complex network topologies, the triggering conditions can be modified to take into account the network structure, the number of nodes, and the communication patterns. For example, in a multi-hop network, the triggering conditions can be designed to optimize the transmission schedule based on the network topology and the data routing paths. Furthermore, for different communication protocols, the event-triggered strategies can be tailored to meet the specific requirements of each protocol. For example, for protocols with contention-based access, the triggering conditions can be adjusted to minimize collisions and maximize the utilization of the communication channel. Overall, by customizing the event-triggered strategies to suit the characteristics of the network topology and communication protocols, the decentralized control system can effectively adapt to a wide range of network configurations and communication scenarios.

What are the potential trade-offs between the minimum inter-transmission times, maximum allowable delays, and the frequency of Pp-DoS attacks that need to be considered in the design

In the design of decentralized event-triggered control strategies, there are several potential trade-offs that need to be considered between the minimum inter-transmission times, maximum allowable delays, and the frequency of Pp-DoS attacks. Minimum Inter-Transmission Times: Increasing the minimum inter-transmission times can reduce the frequency of transmissions, leading to lower communication overhead and reduced network congestion. However, longer inter-transmission times may result in slower response times and decreased system performance. Maximum Allowable Delays: Allowing for longer delays can provide more flexibility in the communication process and accommodate variations in network conditions. However, excessive delays can lead to data loss, increased latency, and potential instability in the control system. Frequency of Pp-DoS Attacks: Managing the frequency of Pp-DoS attacks is crucial for ensuring the reliability and security of the networked control system. Higher attack frequencies can disrupt communication and compromise system operation, while lower attack frequencies may still pose a threat if not adequately addressed. Balancing these trade-offs involves optimizing the event-triggered strategies to achieve a compromise that ensures efficient communication, timely updates, and robust resilience to attacks and delays. By carefully adjusting the parameters related to inter-transmission times, delays, and attack frequencies, the decentralized control system can maintain stability, performance, and security in the face of varying network conditions.

How can the insights from this work on stochastic hybrid systems be applied to other domains beyond networked control systems, such as in the analysis of cyber-physical systems or multi-agent systems

The insights from this work on stochastic hybrid systems can be applied to other domains beyond networked control systems, such as in the analysis of cyber-physical systems or multi-agent systems. Cyber-Physical Systems (CPS): The principles of decentralized event-triggered control and stochastic hybrid modeling can be utilized in the design and analysis of CPS, where physical processes are integrated with computational elements. By incorporating event-triggered strategies and considering stochastic uncertainties, CPS can achieve efficient communication, real-time control, and robustness to disturbances. Multi-Agent Systems (MAS): In MAS, multiple autonomous agents interact to achieve common goals. By applying stochastic hybrid tools, MAS can benefit from adaptive event-triggered control strategies that optimize communication and coordination among agents. The modeling of uncertainties and random events can enhance the resilience and performance of MAS in dynamic environments. By leveraging the concepts of stochastic hybrid systems, researchers and practitioners can enhance the design and operation of complex systems in various domains, ensuring reliability, efficiency, and adaptability in the face of uncertainty and variability.