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Multimodel Approach for Intelligent Control and Applications

机译：智能控制和应用的多模型方法

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The use of the multimodel approach in the modelling, analysis and control of non-linear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a partitioning of the non-linear system's full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. However, most of these strategies, which suppose the determination of these local models as well as their validity domain, remain arbitrary and are generally fixed thanks to a certain a priori knowledge of the system whatever its order. Recently, we have proposed a new approach to derive a multimodel basis which allows us to limit the number of models in the basis to almost four models. Meanwhile, the transition problem between the different models, which may use either a simple commutation or a fusion technique, remains still arise. In this plenary talk, a fuzzy fusion technique is presented and has the following main advantages: (1) use of a fuzzy partitioning in order to determine the validity of each model which enhances the robustness of the solution, (2) introduction, besides the four extreme models, of another model, called average model, determined as an average of the boundary models.
机译：许多研究者提倡在非线性复杂和/或定义不明确的系统的建模，分析和控制中使用多模型方法。该方法假定定义在给定区域或域中有效的一组局部模型。文献中存在不同的策略，这些策略通常基于将非线性系统的整个操作范围划分为多个较小的运行状态，每个运行状态均与局部有效的模型或控制器相关联。但是，大多数这样的策略（假定确定这些局部模型及其有效性域）仍然是任意的，并且由于对系统的先验知识（无论其顺序如何）而通常是固定的。最近，我们提出了一种导出多模型基础的新方法，该方法允许我们将基础中的模型数量限制为几乎四个模型。同时，仍然存在可能使用简单换向或融合技术的不同模型之间的过渡问题。在本次全体会议上，提出了一种模糊融合技术，该技术具有以下主要优点：（1）使用模糊分区来确定每个模型的有效性，从而提高解决方案的鲁棒性；（2）除了四个极端模型，另一个模型称为平均模型，确定为边界模型的平均值。

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