Collaborative Safety-Critical Control for Networked Dynamic Systems with Coupled Dynamics

แนวคิดหลัก

This paper presents a framework for collaborative safety-critical control of networked dynamic systems with coupled dynamics, where the safety of each node depends on the states of its 1-hop neighbors. The authors define a collaborative node-level control barrier function (cNCBF) that incorporates the 1-hop neighborhood dynamics into the evaluation of node-level safety, and construct a distributed algorithm that enables neighboring nodes to collaboratively ensure safety.

บทคัดย่อ

The paper introduces a general networked model with coupled dynamics and local control, and discusses the relationship between node-level safety definitions and local neighborhood dynamics. The authors define a node-level barrier function (NBF), node-level control barrier function (NCBF), and collaborative node-level barrier function (cNCBF), and provide conditions under which sets defined by these functions will be forward invariant.
The authors then use collaborative node-level barrier functions to construct a distributed algorithm for the safe control of collaborating network agents. The algorithm involves rounds of communication between nodes, where each node processes safety requests from its outgoing neighbors and determines needed compromises for its incoming neighbors. The authors provide conditions under which the algorithm is guaranteed to converge to a viable set of safe control actions for all agents or a terminally infeasible state for at least one agent.
The paper also introduces the notion of non-compliance of network neighbors as a metric of robustness for collaborative safety, with respect to a given network state and chosen barrier function hyper-parameters. The results are illustrated using a networked susceptible-infected-susceptible (SIS) model.

สถิติ

As modern systems become ever more connected with complex dynamic coupling relationships, the development of safe control methods for such networked systems becomes paramount.
The computational complexity for even relatively simple networked models can become exponentially intractable as these networks grow.
Distributed and decentralized controllers are developed to scale with network growth, especially when network agents are considered to be independent actors with individual objectives.

คำพูด

"While much of the foundational work on safety for dynamical systems via set invariance was performed by Nagumo [1942], Blanchini [1999], the study of safety-critical control has seen a significant resurgence in recent years, due largely to the introduction and refinement of control barrier functions (CBFs) [Ames et al., 2016, 2019]."
"One common strategy used to tackle the high dimensionality and complexity of networked systems is to break down these potentially large systems into smaller, and therefore more readily solvable, components and provide methods for composing a solution for the entire system."

ข้อมูลเชิงลึกที่สำคัญจาก

Collaborative Safety-Critical Control for Networked Dynamic Systems

by Broo... ที่ arxiv.org 05-02-2024

https://arxiv.org/pdf/2310.03289.pdf

สอบถามเพิ่มเติม

How can the proposed collaborative safety-critical control framework be extended to handle time-varying network topologies or stochastic network dynamics

The proposed collaborative safety-critical control framework can be extended to handle time-varying network topologies or stochastic network dynamics by incorporating adaptive algorithms and real-time communication protocols.

Adaptive Algorithms: Introducing adaptive control algorithms that can adjust control strategies based on changing network topologies or dynamics. These algorithms can continuously monitor the network conditions and adapt the control actions accordingly.

Real-time Communication: Implementing real-time communication protocols between nodes to exchange information about the evolving network topology or stochastic events. This communication can enable nodes to coordinate their actions and adjust their control strategies collaboratively.

Predictive Modeling: Utilizing predictive modeling techniques to anticipate changes in network topologies or dynamics. By forecasting potential variations, nodes can proactively adjust their control strategies to ensure safety in dynamic environments.

Stochastic Control: Incorporating stochastic control methods that can handle uncertainties in network dynamics. By modeling the stochastic nature of the system, nodes can optimize their control actions to account for probabilistic variations in the network.

What are the potential limitations or drawbacks of the assumption that neighboring nodes have weakly non-interfering safety constraints, and how could this assumption be relaxed

The assumption that neighboring nodes have weakly non-interfering safety constraints may have limitations in scenarios where the interactions between nodes are more complex or interconnected. To relax this assumption and address potential drawbacks, the following approaches can be considered:

Interconnected Constraints: Allow for more intricate relationships between neighboring nodes' safety constraints, considering the possibility of overlapping or interdependent constraints that may influence each other.

Dynamic Constraint Adaptation: Implement mechanisms for nodes to dynamically adjust their safety constraints based on the behavior of neighboring nodes. This adaptive approach can account for changing interactions and ensure a more robust safety-critical control framework.

Machine Learning Techniques: Utilize machine learning algorithms to learn and adapt to the evolving relationships between nodes' safety constraints. By leveraging data-driven approaches, the system can better capture complex interactions and dependencies among nodes.

Game Theory: Apply game theory principles to model the interactions between nodes with potentially conflicting safety constraints. By analyzing the strategic behavior of nodes, the system can optimize control strategies in a more comprehensive and realistic manner.

Can the concepts of collaborative safety-critical control be applied to other domains beyond networked dynamic systems, such as multi-agent robotics or cyber-physical systems with heterogeneous components

The concepts of collaborative safety-critical control can indeed be applied to various domains beyond networked dynamic systems. Here are some examples:

Multi-Agent Robotics: In the field of multi-agent robotics, collaborative safety-critical control can enable a team of robots to work together efficiently while ensuring safety. By coordinating their actions and sharing information, robots can navigate complex environments and achieve common objectives without compromising safety.

Cyber-Physical Systems: In cyber-physical systems with heterogeneous components, collaborative safety-critical control can facilitate the integration of diverse elements while maintaining system-wide safety. By establishing communication protocols and control strategies that account for the different components' characteristics, the system can operate effectively and securely.

Autonomous Vehicles: Applying collaborative safety-critical control to autonomous vehicles can enhance their ability to navigate traffic scenarios and avoid collisions. By enabling vehicles to communicate and coordinate their movements, the system can optimize traffic flow and ensure safe operation on the road.

Smart Grids: In the context of smart grids, collaborative safety-critical control can help manage energy distribution and consumption efficiently. By coordinating the actions of grid components and responding to dynamic energy demands, the system can maintain stability and reliability while prioritizing safety.