基于无模型的递归控制障碍函数计算：超局部模型方法
Model-Free Based Computations of Recursive Control Barrier Function: Ultra-Local Model Approach
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中文总结 AI 辅助
本文提出基于超局部模型的无模型递归控制障碍函数计算框架，利用在线估计未知动力学构建约束，在自适应巡航控制基准上验证了其对高相对阶系统安全约束的执行与预判能力。
中文摘要 AI 辅助
控制障碍函数（CBF）为非线性控制系统中安全约束的强制执行提供了系统化框架，但其实现通常依赖精确的系统模型，这在存在显著建模不确定性或未知动力学时会限制其适用性。本文提出一种基于超局部模型方法的无模型递归控制障碍函数计算框架，该框架利用未知系统动力学的在线估计来构建CBF约束，无需系统动力学的显式模型，可增强对干扰和模型失配的鲁棒性。所得控制架构能为相对阶较高的系统强制执行并预判安全约束，在自适应巡航控制基准上验证了所提方法的有效性。
英文摘要
Control barrier functions (CBFs) provide a systematic framework for enforcing safety constraints in nonlinear control systems. However, their implementation typically relies on accurate system models, which can limit their applicability in the presence of significant modeling uncertainties or unknown dynamics. This paper proposes a model-free framework for the computation of recursive control barrier functions based on the ultra-local model approach that leverages online estimation of the unknown system dynamics to construct CBF constraints. This approach does not require an explicit model of the system dynamics and enhances robustness with respect to disturbances and model mismatch. The resulting control architecture enables the enforcement as well as the anticipation of safety constraints for systems with higher relative degree. The effectiveness of the proposed approach is illustrated on the adaptive cruise control benchmark.
