This brief presents a nonmodel-based adaptive control technique that combines principles from machine learning and iterative design optimization with those of continuous-time, falsification-based adaptive control. At the crux of the proposed control strategy are two core elements. First, the recursive Gaussian Process (RGP) modeling is used to maintain an online characterization of the system at hand without the need to maintain a complete database of previously collected measurements (which is required in traditional GP modeling). Second, an adaptation strategy is employed that falsifies candidate controllers from a continuous candidate design space based on desired performance specifications and statistical hypothesis testing. In specific, the control parameter design space is explored by selecting points associated with high uncertainty. Through the use of statistical hypothesis testing, regions of the design space determined to be suboptimal at a user-specified level of confidence are rejected in order to converge to an optimal set of control parameters. The RGP-based adaptation is validated through simulations and laboratory-scale experiments using an airborne wind energy case study. Through these studies, the RGP-based adaptation approach is shown to be effective and is shown to exhibit favorable convergence times when compared with a mature adaptive control technique, extremum seeking (ES).

中文翻译：

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基于递归高斯过程的自适应控制，应用于轻于空气的风能系统

本简介介绍了一种基于非模型的自适应控制技术，该技术将机器学习和迭代设计优化的原理与连续时间、基于证伪的自适应控制的原理相结合。所提出的控制策略的关键是两个核心要素。首先，递归高斯过程 (RGP) 建模用于维护手头系统的在线表征，而无需维护先前收集的测量值的完整数据库（这是传统 GP 建模所必需的）。其次，采用了一种适应策略，即伪造基于所需的性能规格和统计假设检验，来自连续候选设计空间的候选控制器。具体而言，通过选择与高不确定性相关的点来探索控制参数设计空间。通过使用统计假设检验，在用户指定的置信水平下被确定为次优的设计空间区域被拒绝，以便收敛到一组最佳控制参数。基于 RGP 的适应性通过使用机载风能案例研究的模拟和实验室规模实验得到验证。通过这些研究，与成熟的自适应控制技术极值搜索 (ES) 相比，基于 RGP 的自适应方法被证明是有效的，并且显示出有利的收敛时间。