Adaptive Sampling Control Loops for Resilient Cyber-Physical Systems
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Solution Overview
Problem
Distributed cyber-physical systems (CPS) are vulnerable to both physical component failures and cyber threats, such as malicious attacks, which can affect system performance and stability, and existing control designs often focus on one domain without considering interdependencies between cyber and physical spaces.
Innovation Solution
A resilient control method that dynamically adjusts the sampling frequency of control loops based on prognostic information from both cyber and physical spaces, using a resilient control agent to monitor and respond to adverse conditions by reducing sampling frequency in stable loops and increasing it in unstable ones, thereby maintaining system performance and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sampling frequency of all control loops is maintained at a high level to ensure system stability and rapid response to adverse conditions, then system reliability is improved, but energy consumption and network bandwidth usage increase
Solution Approach 1:
The patent implements dynamic adjustment of sampling frequencies for different control loops based on their real-time stability status. The resilient control agent continuously monitors control loops and adapts sampling rates - maintaining high sampling frequencies for unstable loops while reducing frequencies for stable loops, thereby optimizing energy consumption while preserving system reliability where needed
Solution Approach 2:
The patent applies different sampling frequencies to different control loops based on their individual stability characteristics rather than using a uniform sampling rate. This localized approach allows each control loop to receive appropriate monitoring intensity - high-frequency sampling for vulnerable loops and low-frequency sampling for stable loops - reducing overall energy consumption while maintaining system-wide reliability
2Reliability
If the sampling frequency is increased to detect and respond to adverse conditions more quickly, then system security is improved, but network bandwidth consumption increases
Solution Approach 1:
The system dynamically adjusts sampling frequencies based on the stability status of each control loop. When adverse conditions are detected in specific loops, the resilient control agent increases sampling frequency for those loops to enhance security monitoring, while maintaining lower sampling frequencies for stable loops, thereby optimizing network bandwidth usage while preserving system security
Solution Approach 2:
The patent implements localized sampling rate adjustment for individual control loops rather than uniformly increasing sampling across the entire system. This allows network bandwidth to be concentrated on loops experiencing adverse conditions while reducing bandwidth consumption from stable loops, achieving security improvement where needed without proportionally increasing overall network usage
3Device complexity
If traditional control designs focus on only one domain (cyber or physical) to simplify control architecture, then device complexity is reduced, but adaptability to interdependent cyber-physical threats decreases
Solution Approach 1:
The patent merges cyber domain monitoring and physical domain control into a unified resilient control framework. The resilient control agent simultaneously processes prognostic information from both cyber and physical spaces and coordinates control actions across both domains, enabling the system to handle interdependent cyber-physical threats while maintaining manageable architecture complexity through integrated design
Solution Approach 2:
The resilient control agent performs multiple functions - monitoring both cyber and physical domains, detecting adverse conditions, adjusting sampling frequencies, and coordinating control responses - within a single unified controller. This multi-functional approach enhances adaptability to complex cyber-physical threats while avoiding the need for separate specialized control systems that would increase overall complexity
Data Source
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AI summary
A distributed cyber-physical system includes physical elements (15-1, 15-2,..., 15-n) disposed in a physical space (11). Controllers (13-1, 13-2,..., 13-n) are disposed in a cyber space (10). Each of the physical elements (15-1, 15-2,..., 15-n) corresponds to a corresponding controller (13-1, 13-2,..., 13-n). A cyber infrastructure (12) is disposed in the cyber space (10). The cyber infrastructure (12) manages a connection between the controllers (13-1, 13-2,..., 13-n) and the physical elements (15-1, 15-2,..., 15-n). Control loops (14) are established via the cyber infrastructure (12). Each of the control loops (14) includes a physical element and a corresponding controller. A resilient control agent (31) is configured to monitor each of the control loops (14), determine when one of the control loops (14) is experiencing an adverse condition, reduce a sampling frequency of the control loops (14) that are not experiencing the adverse condition, and increase a sampling frequency of the control loop (14) that is experiencing the adverse condition.