Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
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DOI：
10.1109/cdc45484.2021.9683557
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
John Jackson;L. Laurenti;E. Frew;Morteza Lahijanian
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文献类型：
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作者：
John Jackson;L. Laurenti;E. Frew;Morteza Lahijanian
自治系统通常具有复杂且可能未知的动态，例如，黑盒组件这导致不可预测的行为，并使控制设计与性能保证的一个重大挑战。本文提出了一种数据驱动的控制综合框架，这样的系统受到有限迹线性时序逻辑（LTLf）规范。该框架结合了一个基线（离线）控制器与一个新的在线控制器和细化过程，提高了基线保证新的数据被收集。基线控制器是离线计算的不确定抽象构造高斯过程（GP）回归给定的数据集。离线控制器提供了满足LTLf规范的概率的下限，由于离散化和回归误差，这可能远非最佳。协同作用产生于在线控制器使用离线抽象沿着当前状态和新数据来选择下一个最佳动作。在线控制器可以改善基线保证，因为它避免了离散化误差，并减少了回归误差，因为新的数据被收集。新的数据还用于使用局部GP回归来细化抽象和离线控制器，这显著降低了计算开销。评估显示了所提出的离线-在线框架的有效性，特别是与离线控制器相比时。
Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performance guarantees a major challenge. This paper presents a data-driven control synthesis framework for such systems subject to linear temporal logic on finite traces (LTLf) specifications. The framework combines a baseline (offline) controller with a novel online controller and refinement procedure that improves the baseline guarantees as new data is collected. The baseline controller is computed offline on an uncertain abstraction constructed using Gaussian process (GP) regression on a given dataset. The offline controller provides a lower bound on the probability of satisfying the LTLf specification, which may be far from optimal due to both discretization and regression errors. The synergy arises from the online controller using the offline abstraction along with the current state and new data to choose the next best action. The online controller may improve the baseline guarantees since it avoids the discretization error and reduces regression error as new data is collected. The new data are also used to refine the abstraction and offline controller using local GP regression, which significantly reduces the computation overhead. Evaluations show the efficacy of the proposed offline-online framework, especially when compared against the offline controller.