提出新型体积屏障函数方法，实现多约束下系统安全与性能的平衡
Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method
- 基于扰动观测器估计不确定性，保证误差指数收敛
- 引入体积屏障函数确保多约束下优化问题始终有解
- 适合对安全性要求高的机器人、自动驾驶等控制场景
本文研究不确定系统在多重控制屏障函数（CBF）约束和输入约束下的安全关键控制问题。提出一种新框架，生成的安全滤波器在安全风险出现时最小化对参考输入的修改，兼顾安全与性能。采用基于鲁棒符号误差积分（RISE）的非线性扰动观测器（DOB）估计系统不确定性，确保估计误差指数收敛至零。该误差界被融入安全控制器中，降低保守性同时保障安全。为应对多重CBF与输入约束带来的挑战，通过分析二次规划（QP）可行域，提出新型体积屏障函数（VCBF），确保可行空间不退化。进一步提出基于DOB-VCBF的方法，即使在扰动下仍能保持QP可解性，从而确保系统安全。最后通过多组仿真与实验验证了所提控制器的有效性。
原文摘要 · Abstract (English)
In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear disturbance observer (DOB) based on the robust integral of the sign of the error (RISE) is used to estimate system uncertainties, ensuring that the estimation error converges to zero exponentially. This error bound is integrated into the safety-critical controller to reduce conservativeness while ensuring safety. To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem. % ensuring solution feasibility by keeping the volume as a positive value. To ensure that the feasible space does not vanish under disturbances, a DOB-VCBF-based method is introduced, ensuring system safety while maintaining the feasibility of the resulting QP. Subsequently, several groups of simulation and experimental results are provided to validate the effectiveness of the proposed controller.
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