Neural Control Barrier Functions for Signal Temporal Logic Specifications with Input Constraints
This work addresses a domain-specific problem in control systems for ensuring safety and task fulfillment, representing an incremental advancement by refining existing methods for STL specifications.
The paper tackles controller synthesis for continuous-time systems with Signal Temporal Logic (STL) specifications and input constraints by proposing a neural network-based framework for time-varying control barrier functions, and demonstrates its effectiveness in simulation studies on various dynamical systems.
Signal Temporal Logic (STL) provides a powerful framework to describe complex tasks involving temporal and logical behavior in dynamical systems. This work addresses controller synthesis for continuous-time systems subject to STL specifications and input constraints. We propose a neural network-based framework for synthesizing time-varying control barrier functions (TVCBF) and their corresponding controllers for systems to fulfill a fragment of STL specifications while respecting input constraints. We formulate barrier conditions incorporating the spatial and temporal logic of the given STL specification. We also incorporate a method to refine the time-varying set that satisfies the STL specification for the given input constraints. Additionally, we introduce a validity condition to provide formal safety guarantees across the entire state space. Finally, we demonstrate the effectiveness of the proposed approach through several simulation studies considering different STL tasks for various dynamical systems (including affine and non-affine systems).