用可学习的屏障函数实现安全、高效且无需调参的时序逻辑控制。
Feasibility-aware Learning of Robust Temporal Logic Controllers using BarrierNet
- 构建可训练的时变高阶屏障函数，自动调整约束和超参数。
- 在严苛输入限制下仍保持高时序逻辑鲁棒性，优于传统方法。
- 适合需要长期安全约束的复杂动态系统控制场景。
控制屏障函数（CBFs）被用于强制执行以信号时序逻辑（STL）表达的安全与任务规范。然而，现有的CBF-STL方法通常依赖固定超参数和每步优化，导致行为过于保守、在紧输入边界附近不可行，并难以满足长时域STL任务。为解决这些问题，我们提出一种可行性感知的学习框架，构建可训练的时变高阶控制屏障函数（HOCBF）约束及其超参数，确保给定STL规范的满足。我们引入统一的鲁棒性度量，联合捕捉STL满足度、约束可行性及控制边界合规性，并提出神经网络架构生成最大化该鲁棒性的控制输入。所提控制器保证了严格可行的HOCBF约束，且无需手动调参。仿真结果表明，该框架在紧输入边界下仍维持高STL鲁棒性，在复杂环境中显著优于固定参数和非自适应基线方法。
原文摘要 · Abstract (English)
Control Barrier Functions (CBFs) have been used to enforce safety and task specifications expressed in Signal Temporal Logic (STL). However, existing CBF-STL approaches typically rely on fixed hyperparameters and per-step optimization, which can lead to overly conservative behavior, infeasibility near tight input limits, and difficulty satisfying long-horizon STL tasks. To address these limitations, we propose a feasibility-aware learning framework that constructs trainable, time-varying High Order Control Barrier Function (HOCBF) constraints and hyperparameters that guarantee satisfaction of a given STL specification. We introduce a unified robustness measure that jointly captures STL satisfaction, constraint feasibility, and control-bound compliance, and propose a neural network architecture to generate control inputs that maximize this robustness. The resulting controller guarantees STL satisfaction with strictly feasible HOCBF constraints and requires no manual tuning. Simulation results demonstrate that the proposed framework maintains high STL robustness under tight input bounds and significantly outperforms fixed-parameter and non-adaptive baselines in complex environments.
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