Dynamical systems, for instance in model predictive control, often contain unknown parameters, which must be determined during system operation. Online, or on-the-fly, parameter identification methods are therefore necessary. The challenge of online methods is that one must continuously estimate parameters as experimental data becomes available. The existing techniques in the context of time-dependent partial differential equations exclude the case where the system depends nonlinearly on the parameters. Based on a model reference adaptive system approach, we present an online parameter identification method for nonlinear infinite-dimensional evolutionary systems.

中文翻译：

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非线性在线参数辨识的模型参考自适应系统方法

动态系统，例如在模型预测控制中，通常包含未知参数，这些参数必须在系统运行期间确定。因此，需要在线或即时参数识别方法。在线方法的挑战在于，当实验数据可用时，必须不断地估计参数。瞬态偏微分方程背景下的现有技术排除了系统非线性依赖于参数的情况。基于模型参考自适应系统方法，我们提出了一种非线性无限维进化系统的在线参数识别方法。