论文标题
通过分布可靠的优化，可用于不确定系统模型的安全稳定控制合成
Safe and Stable Control Synthesis for Uncertain System Models via Distributionally Robust Optimization
论文作者
论文摘要
本文认为在模型不确定性存在下，动态系统的安全性和稳定性。可以分别使用控制屏障函数（CBF）和控制Lyapunov功能（CLF）指定安全性和稳定性约束。要考虑模型的不确定性，通常会考虑使用约束的稳健和机会表述。但是，这需要模型不确定性的已知误差范围或已知分布，并且所得的制剂可能会遭受过度保守或过度保障的损失。在本文中，我们假设只有一组有限的模型参数不确定性样本，并制定了具有CBF安全性和CLF稳定性保证的控制综合的分配强大的机会约束程序（DRCCP）。为了促进在线执行期间有效计算控制输入的计算，我们将DRCCP的重新介绍为二阶圆锥计划（SOCP）。与1）基线CLF-CBF二次编程方法相比，在自适应巡航控制示例中评估了我们的公式，2）一种可靠的方法，该方法假定系统不确定性的已知误差范围，以及3）一种偶然受限的方法，该方法假设不确定的不确定性的高斯过程分布。
This paper considers enforcing safety and stability of dynamical systems in the presence of model uncertainty. Safety and stability constraints may be specified using a control barrier function (CBF) and a control Lyapunov function (CLF), respectively. To take model uncertainty into account, robust and chance formulations of the constraints are commonly considered. However, this requires known error bounds or a known distribution for the model uncertainty, and the resulting formulations may suffer from over-conservatism or over-confidence. In this paper, we assume that only a finite set of model parametric uncertainty samples is available and formulate a distributionally robust chance-constrained program (DRCCP) for control synthesis with CBF safety and CLF stability guarantees. To facilitate efficient computation of control inputs during online execution, we present a reformulation of the DRCCP as a second-order cone program (SOCP). Our formulation is evaluated in an adaptive cruise control example in comparison to 1) a baseline CLF-CBF quadratic programming approach, 2) a robust approach that assumes known error bounds of the system uncertainty, and 3) a chance-constrained approach that assumes a known Gaussian Process distribution of the uncertainty.
