Abstract Dense medium separation (DMS) is an important process for the coal industry, but its time-varying and strongly nonlinear characteristics make it difficult to operate stably. To address this problem, this paper first adopts knowledge-based linear model and data-driven virtual unmodeled dynamics to formulate the DMS process. Then a model-data-based switching adaptive control approach is proposed to achieve the online adjustment of set point of dense medium density, which is a key operation parameter for the low-ash coal in production. To realize adaptive ability of the control system, the online parameter estimation methods for the linear model and virtual unmodeled dynamics are adopted, and their convergence are fully discussed, followed by the stability and convergence analysis of the closed-loop system. Finally, the effectiveness is demonstrated by using real data and semi-physical simulations.

中文翻译：

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基于模型数据的选煤重介质分离切换自适应控制

摘要 浓介质分离(DMS)是煤炭工业的重要工艺，但其时变和强非线性特性使其难以稳定运行。为了解决这个问题，本文首先采用基于知识的线性模型和数据驱动的虚拟未建模动力学来制定 DMS 过程。然后提出了一种基于模型数据的切换自适应控制方法来实现重介质密度设定值的在线调整，这是生产中低灰分煤的关键操作参数。为实现控制系统的自适应能力，采用线性模型和虚拟未建模动力学的在线参数估计方法，充分讨论其收敛性，然后进行闭环系统的稳定性和收敛性分析。最后，