非控制仿射系统的预测流控制屏障函数

中文摘要 AI 辅助

控制屏障函数（CBFs）通过在时间上逐点施加条件来保证安全性，而不考虑状态在未来时间范围内的演变。因此，基于CBF的控制通常是短视的。预测流CBFs（P-CBFs）将CBFs从当前状态的函数推广为在参数化控制下预测流的泛函。P-CBFs可以在整个预测范围内证明安全性，同时允许在该范围内进行性能优化。然而，先前关于P-CBFs的工作仅适用于控制仿射系统，并且存在预测范围可能缩小甚至消失的局限性。本文通过引入一种规划控制来解决这两个缺点，该规划控制从参数化计划平滑过渡到备份控制（如果需要），并引入一个时间偏移参数来确定是否需要过渡。控制计划和时间偏移参数的演变由单个保证可行的凸优化确定，如果控制限制是凸多面体，则该优化简化为二次规划（QP）。实时执行的控制由瞬时规划控制和时间偏移决定。该方法同时解决了固定预测范围内的安全认证和性能优化问题。在自动驾驶汽车穿越密集障碍物环境的仿真中，将该方法与非线性模型预测控制进行了比较。

英文摘要

Control barrier functions (CBFs) enforce safety through conditions imposed pointwise in time without consideration of state evolution over a future horizon. Thus, CBF-based controls are typically myopic. Predicted-flow CBFs (P-CBFs) generalize CBFs from a function of the current state to a functional of the predicted flow under a parametrized control. P-CBFs can certify safety over the entire prediction horizon while simultaneously allowing for performance optimization over the horizon. However, prior work with P-CBFs only applies to control-affine systems, and suffers from a limitation where the prediction horizon can shrink or even vanish. This article addresses both of these shortcomings by introducing a planning control that smoothly transitions from a parametric plan to a backup control (if needed) and a time-shift parameter that determines if transition is needed. The evolution of the control-plan and time-shift parameters is determined from a single guaranteed-feasible convex optimization, which reduces to a quadratic program (QP) if the control limits are a convex polyhedron. The real-time executed control is determined from the instantaneous planning control and time shift. This method simultaneously addresses safety certification and performance optimization over the fixed prediction horizon. The approach is compared to nonlinear model predictive control in simulation of an autonomous car navigating a dense obstacle environment.

发表机构

• University of Kentucky（肯塔基大学）

机构由 AI 辅助整理，请以论文原文为准。