Neural Barrier Function Learning for Cyber-Physical Safety Verification
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Solution Overview
Problem
Existing methods for constructing barrier functions in control systems are limited, often requiring manual identification and lacking guarantees of safety, and existing machine learning approaches are restricted to specific initial conditions or control policies, limiting their applicability to complex dynamical systems.
Innovation Solution
An automated machine learning system that learns barrier functions using neural networks to ensure safety conditions, capable of identifying globally safe regions in control systems, and integrates with satisfiability modulo theories solvers for verification, applicable to both discrete and continuous time control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification of barrier functions is used, then safety guarantees can be obtained, but the process is time-consuming and difficult to apply to complex systems
Solution Approach 1:
The patent replaces manual analytical identification of barrier functions with an automated machine learning system using neural networks. The system learns barrier functions from system trajectories and data, automatically generating safety certificates without requiring manual mathematical analysis, thus eliminating the time-consuming aspect while maintaining safety guarantees through formal verification integration.
Solution Approach 2:
The system enables the control system itself to generate its own barrier functions through self-learning from operational data and trajectories. The neural network automatically identifies safe regions and learns barrier functions directly from system behavior, making the process self-service and eliminating dependency on manual expert analysis.
2Extent of automation
If existing machine learning approaches are used, then automation is achieved, but applicability is limited to specific initial conditions or control policies
Solution Approach 1:
The patent creates a universal barrier function learning system that can handle diverse control systems, initial conditions, and control policies. The neural network is trained on system trajectories and learns generalizable barrier functions that apply across different operating conditions and system configurations, making the approach versatile and broadly applicable beyond specific cases.
Solution Approach 2:
The system dynamically adapts to different control systems and operating conditions by learning from system trajectories. The barrier functions are not fixed but are learned and updated based on actual system behavior, allowing the system to adjust to varying initial conditions and control policies while maintaining safety guarantees.
3Productivity
If approximate barrier functions are learned, then automation and speed are improved, but absolute safety guarantees are not provided
Solution Approach 1:
The patent introduces satisfiability modulo theories (SMT) solvers as an intermediary verification layer. The neural network generates approximate barrier functions quickly, and the SMT solver formally verifies these functions to provide absolute safety guarantees. This two-stage approach maintains both speed and reliability by combining machine learning with formal verification.
Solution Approach 2:
The system incorporates feedback from formal verification results to improve and validate the learned barrier functions. The SMT solver provides feedback on whether the learned functions satisfy safety properties, allowing iterative refinement and ensuring that the final barrier functions provide absolute safety guarantees while maintaining the speed benefits of automated learning.
Data Source
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AI summary
Described is a system and method for generating safety conditions for a cyber-physical system with state space S, action space A and trajectory data labelled as either safe or unsafe. In operation, the system receives inputs and ten minimizes loss functions to cause a neural network to become a barrier function. Based on the barrier function, the system can then determine if the cyber-physical system is entering an usafe state, such that if the cyber-physical system is entering the usafe state, then the cyber-physical system is caused to initiate a maneuver to position the cyber-physical system into a safe state.