论文标题
安全控制合成，具有不确定的动态和约束
Safe Control Synthesis with Uncertain Dynamics and Constraints
论文作者
论文摘要
本文考虑了动力学模型和安全限制的动态系统的安全控制合成。我们制定了新型的概率和鲁棒（最坏情况）控制Lyapunov功能（CLF）和控制屏障功能（CBF）约束，这些函数（CBF）考虑了两种情况下不确定性的影响。我们表明，概率或健壮的（最坏情况）的配方会导致二阶锥体程序（SOCP），从而实现有效的安全和稳定的控制合成。我们在未知环境中导航的自主机器人的Pybullet模拟中评估了我们的方法，并将性能与基线CLF-CBF二次编程方法进行比较。
This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov function (CLF) and control barrier function (CBF) constraints that take into account the effect of uncertainty in either case. We show that either the probabilistic or the robust (worst-case) formulation leads to a second-order cone program (SOCP), which enables efficient safe and stable control synthesis. We evaluate our approach in PyBullet simulations of an autonomous robot navigating in unknown environments and compare the performance with a baseline CLF-CBF quadratic programming approach.
