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中文摘要：
针对机炉协调系统，提出一种基于子空间辨识和多模型策略结合的数据驱动建模方法．基于多模型建模的分解合成原理，原有非线性系统首先被划分为若干个工作区间，通过获取与各工作区间对应的输入输出数据，使用子空间辨识的方法以获得局部状态空间模型．随后，利用各局部模型在切换点上的连贯关系，将所有局部模型转化到一组共同的基下，从而获得整体多模型系统．由于其数据驱动特性，该建模方法可以在系统机理和结构未知的情况下，被广泛用于各种类型对象，同时由于所得模型具有状态空间形式，因此又适用于高级控制器的设计．基于辨识模型，将多模型预测控制器用于机炉协调系统的大范围变工况调节．仿真研究证明了该方法的有效性．
英文摘要：
A novel data-driven modeling strategy for boiler-turbine coordinated system using subspace identification and multi-model method is proposed. According to the decomposition and synthesis principle of multi-modeling, the original nonlinear system is divided into several local models, and subspace method is used to identify the local state-space model by obtaining the input and output data corresponding to the local models. Then coherence relations of each local model in the swithching points are used to transform all local models to the common basis to build the integrated multi-model system. Due to its data-driven feature, the proposed modeling method can easily be adapted to different types of systems without knowing the underlying system models, and the state-space form of the resulting model makes it suitable for advanced controller design. Based on the identification model, a constrained multi- model predictive controller （MMPC） is designed to operate the boiler-turbine system in a wide range of operation. Simulation results demonstrate the effectiveness of the proposed approach.
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