Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from the data stream generated during operation. Gaussian process-based learning is particularly well suited for safety-critical applications as it ensures bounded prediction errors. While there exist computationally efficient approximations for online inference, these approaches lack guarantees for the prediction error and have high memory requirements, and are therefore not applicable to safety-critical systems with tight memory constraints. In this work, we propose a novel networked online learning approach based on Gaussian process regression, which addresses the issue of limited local resources by employing remote data management in the cloud. Our approach formally guarantees a bounded tracking error with high probability, which is exploited to identify the most relevant data to achieve a certain control performance. We further propose an effective data transmission scheme between the local system and the cloud taking bandwidth limitations and time delay of the transmission channel into account. The effectiveness of the proposed method is successfully demonstrated in a simulation.
翻译:在未知环境中运行的安全临界技术系统要求有能力迅速调整其行为,这可以通过从运行期间生成的数据流中推断出一个在线模型来控制。Gaussian基于流程的学习特别适合于安全关键应用,因为它能确保受约束的预测错误。虽然有计算高效的在线推断近似值,但这些方法缺乏预测错误的保证,记忆要求高,因此不适用于有严格的内存限制的安全关键系统。在这项工作中,我们提议采用基于高西亚进程回归的新颖的网络在线学习方法,通过在云层中使用远程数据管理来解决有限当地资源的问题。我们的方法正式保证使用高概率的封闭跟踪错误,用于确定最相关的数据以达到某种控制性能。我们进一步提议在本地系统和云层之间建立一个有效的数据传输计划,将带宽限制和传输频道延迟的时间考虑在内。在模拟中成功展示了拟议方法的有效性。