摘要：AbstractEmerging control applications in the Internet-of-Things are increasingly relying on communication networks and wireless channels to close the loop. Traditional model-based approaches, i.e., assuming a known wireless channel model, are focused on analyzing stability and designing appropriate controller structures. Such modeling is challenging as wireless channels are typically unknown a priori and only available through data samples. In this work we aim to design data-based controllers using channel samples and provide high confidence guarantees on the performance of these controllers when deployed over the actual unknown channel. To achieve these results we combine statistical learning (concentration inequalities) with structural properties of our problem (monotonicity with respect to the unknown channel parameters), and also provide sample complexity analysis.
关键词：KeywordsLearning AlgorithmsNetworked Control SystemsStatistical AnalysisCommunication channelsController Design
