不依赖在线优化的约束控制，确保安全递归可行。
Optimization-Free Constrained Control with Guaranteed Recursive Feasibility: A CBF-Based Reference Governor Approach
- 用软最小值聚合动态安全裕度构造平滑屏障函数
- 理论保证安全约束始终可行，性能媲美传统方法
- 适合对实时性与安全性要求高的控制场景
本文提出一种集成显式参考调节器（ERG）与控制屏障函数（CBF）的约束控制框架，无需在线优化即可保证递归可行性。将参考更新建模为扩展系统的虚拟控制输入，利用动态安全裕度（DSM）的软最小值聚合构造平滑屏障函数。与标准CBF不同，该方法通过李雅普诺夫水平集的前向不变性设计，确保安全约束始终可行。由此导出显式的闭式参考更新律，在严格保障安全的同时最小化对原始参考轨迹的偏离。理论分析证明渐近收敛性，数值仿真显示其性能可媲美传统ERG框架。
原文摘要 · Abstract (English)
This letter presents a constrained control framework that integrates Explicit Reference Governors (ERG) with Control Barrier Functions (CBF) to ensure recursive feasibility without online optimization. We formulate the reference update as a virtual control input for an augmented system, governed by a smooth barrier function constructed from the softmin aggregation of Dynamic Safety Margins (DSMs). Unlike standard CBF formulations, the proposed method guarantees the feasibility of safety constraints by design, exploiting the forward invariance properties of the underlying Lyapunov level sets. This allows for the derivation of an explicit, closed-form reference update law that strictly enforces safety while minimizing deviation from a nominal reference trajectory. Theoretical results confirm asymptotic convergence, and numerical simulations demonstrate that the proposed method achieves performance comparable to traditional ERG frameworks.
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