论文标题
$ \ MATHCAL {L} _1 $ - $ \ MATHCAL {GP} $：$ \ MATHCAL {L} _1 $自适应控制贝叶斯学习
$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning
论文作者
论文摘要
我们提出$ \ MATHCAL {l} _1 $ - $ \ MATHCAL {GP} $，一种基于$ \ Mathcal {L} _1 $自适应控制和高斯过程回归（GPR）的体系结构，用于安全同时控制和学习。一方面，$ \ MATHCAL {L} _1 $自适应控制提供了稳定性和瞬态性能保证，从而使GPR有效，安全地学习了不确定的动态。另一方面，可以方便地将学习的动态纳入$ \ Mathcal {l} _1 $控制体系结构而不牺牲鲁棒性和跟踪性能。随后，学习的动态可能会导致较不保守的设计，以进行性能/稳健性权衡。我们通过数值模拟说明了所提出的体系结构的功效。
We present $\mathcal{L}_1$-$\mathcal{GP}$, an architecture based on $\mathcal{L}_1$ adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning. On one hand, the $\mathcal{L}_1$ adaptive control provides stability and transient performance guarantees, which allows for GPR to efficiently and safely learn the uncertain dynamics. On the other hand, the learned dynamics can be conveniently incorporated into the $\mathcal{L}_1$ control architecture without sacrificing robustness and tracking performance. Subsequently, the learned dynamics can lead to less conservative designs for performance/robustness tradeoff. We illustrate the efficacy of the proposed architecture via numerical simulations.
