Adaptive Plant Control Using Spectral Anomaly Indicators
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Solution Overview
Problem
Existing control systems struggle to detect and respond to anomalies and cyber-attacks in real-time, especially when the plant physics or architecture changes, leading to instability and performance issues, as they rely on fixed architectures and historical data, which are not effective for unanticipated malfunctions.
Innovation Solution
The implementation of anomaly detection and anomaly-based control methods that use spectral correlations to determine health and anomaly indicators from runtime data, allowing for adaptive control without the need for historical data or system identification, and can be embedded in smart devices for distributed control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed controller architecture is used, then the controller is simple to implement, but it cannot adapt to unanticipated malfunctions or changes in plant physics
Solution Approach 1:
The controller transitions from a fixed architecture to a dynamic adaptive architecture that modifies its control strategy in real-time based on anomaly detection. The controller architecture evolves during operation to handle unanticipated malfunctions by adjusting control parameters and strategies dynamically.
Solution Approach 2:
The controller performs self-detection and self-adaptation through anomaly detection algorithms that monitor system behavior autonomously. The system identifies its own anomalies and adjusts its control strategy without external intervention, enabling it to handle unanticipated malfunctions independently.
2Reliability
If robust control theory is used to make the controller insensitive to friction disturbances, then the controller handles friction variations well, but it becomes confused when basic electro-mechanical physics changes or sensors malfunction
Solution Approach 1:
The system implements feedback through anomaly detection that monitors controller confusion and system behavior. When the controller becomes confused due to sensor malfunctions or physics changes, the feedback mechanism detects these anomalies and triggers adaptive responses, allowing the system to distinguish between normal friction variations and actual malfunctions.
Solution Approach 2:
The system applies partial adaptation by maintaining robust control for known friction disturbances while adding selective anomaly detection for unanticipated malfunctions. This approach keeps the controller insensitive to normal friction variations while becoming sensitive to actual sensor failures or physics changes through targeted anomaly monitoring.
3Difficulty of detecting and measuring
If classical fault detection is used to match I/O patterns against known models, then the detection is simple, but it cannot guard against unanticipated or undetected malfunctions including cyber-attacks
Solution Approach 1:
The system adds another dimension to fault detection by moving from pattern matching in the time domain to spectral analysis in the frequency domain. This dimensional change enables detection of anomalies that do not match known patterns, including cyber-attacks and unanticipated malfunctions, while maintaining computational simplicity through spectral correlation methods.
4Measurement precision
If extensive historical data collection is performed for anomaly detection, then the detection accuracy improves, but the computational complexity and data storage requirements increase
Solution Approach 1:
The system extracts only the essential spectral correlation features from runtime data rather than analyzing extensive historical datasets. By taking out and focusing on the critical spectral relationships between stability radii, the system achieves high detection accuracy with minimal computational overhead and no need for large historical data storage.
Data Source
AI summary
A plant control system includes a plant system and a control system controlling the plant system. Runtime conditions of an operating point of the plant control system are received. The runtime conditions include a runtime state of the plant system, a runtime output of the plant system, and a runtime control action applied to the plant system. Reference conditions of a reference point corresponding to the operating point are determined. Stability radius measures of a state difference, an output difference, and a control action difference are computed. One or more of an observability anomaly indicator, health observability indicator, tracking performance anomaly indicator, tracking performance health indicator, controllability anomaly indicator, and controllability health indicator are determined based on respective spectral correlations between two of the stability radius measure of the output difference, the stability radius measure of the state difference, and the stability radius measure of the control action difference.