论文标题
强大的自适应控制障碍功能：一种自适应和数据驱动的安全方法（扩展版）
Robust Adaptive Control Barrier Functions: An Adaptive & Data-Driven Approach to Safety (Extended Version)
论文作者
论文摘要
开发了一个新的框架，以控制具有结构性参数不确定性的约束非线性系统。通过在线参数适应和数据驱动的模型估计来实现安全集的正向不变性。新的自适应数据驱动的安全范式与最近在闭环中名义上收缩的系统合并了最新的自适应控制算法。该统一比其他安全控制器更一般，因为闭环收缩不需要可逆或特定形式。此外，该方法比非线性模型预测控制要便宜，因为它不需要完整的所需轨迹，而只是所需的终端状态。在具有不确定的非线性空气动力学的飞机的俯仰动力学上说明了该方法。
A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainties. Forward invariance of a safe set is achieved through online parameter adaptation and data-driven model estimation. The new adaptive data-driven safety paradigm is merged with a recent adaptive control algorithm for systems nominally contracting in closed-loop. This unification is more general than other safety controllers as closed-loop contraction does not require the system be invertible or in a particular form. Additionally, the approach is less expensive than nonlinear model predictive control as it does not require a full desired trajectory, but rather only a desired terminal state. The approach is illustrated on the pitch dynamics of an aircraft with uncertain nonlinear aerodynamics.
