论文标题
通过近似离散时间模型的采样数据系统具有控制屏障功能的安全性
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
论文作者
论文摘要
控制障碍功能（CBF）已被证明是非线性系统安全至关重要控制器设计的强大工具。现有的设计范例不能解决理论（具有连续时间模型的控制器设计）与实践（离散的时间采样实现结果控制器）之间的差距；这可能导致性能不佳，并且违反了硬件实例的安全性。我们提出了一种方法，通过将采样DATA对应物合成这些基于CBF的控制器的方法，使用近似离散的时间模型和采样DATA控制屏障函数（SD-CBFS）。使用系统连续时间模型的属性，我们建立了SD-CBF与采样数据系统的实际安全概念之间的关系。此外，我们构建了基于凸优化的控制器，该控制器正式将非线性系统赋予实践中的安全保证。我们证明了这些控制器在模拟中的功效。
Control Barrier Functions (CBFs) have been demonstrated to be a powerful tool for safety-critical controller design for nonlinear systems. Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CBF-based controllers using approximate discrete time models and Sampled-Data Control Barrier Functions (SD-CBFs). Using properties of a system's continuous time model, we establish a relationship between SD-CBFs and a notion of practical safety for sampled-data systems. Furthermore, we construct convex optimization-based controllers that formally endow nonlinear systems with safety guarantees in practice. We demonstrate the efficacy of these controllers in simulation.
