Abstract Data-driven, model-free control strategies leverage statistical or learning techniques to design controllers based on data instead of dynamic models. We have previously introduced the dissipativity learning control (DLC) method, where the dissipativity property is learned from the input–output trajectories of a system, based on which L 2 -optimal P/PI/PID controller synthesis is performed. In this work, we analyze the statistical conditions on dissipativity learning that enable control performance guarantees, and establish theoretical results on performance under nominal conditions as well as in the presence of statistical errors. The implementation of DLC is further formalized and is illustrated on a two-phase chemical reactor, along with a comparison to model identification-based LQG control.

中文翻译：

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耗散学习控制 (DLC)：输入输出数据驱动的无模型控制的理论基础

摘要 数据驱动、无模型控制策略利用统计或学习技术来设计基于数据而不是动态模型的控制器。我们之前已经介绍了耗散学习控制 (DLC) 方法，该方法从系统的输入-输出轨迹中学习耗散特性，并在此基础上执行 L 2 -最优 P/PI/PID 控制器综合。在这项工作中，我们分析了能够保证控制性能的耗散学习的统计条件，并在标称条件下以及存在统计误差的情况下建立了性能的理论结果。DLC 的实施进一步形式化，并在两相化学反应器上进行了说明，并与基于模型识别的 LQG 控制进行了比较。