Minimum variance (MV) benchmark is a quantification that is widely applied to compare actual performance of a control loop against its optimal performance. For linear systems, MV benchmark has been well studied, and several well-practiced techniques have been proposed to evaluate the performance of the systems. However, these techniques may not provide satisfactory performance in some situations. For instance, the operational data sampled from a multi-model system does not satisfy stationary conditions that are required for conventional methods. As a result, linear MV benchmark techniques cannot be applied. One possible approach to design MV controller for a multi-model system is to segregate, and label the data using a dynamic clustering method. Then, the MV benchmark can be determined by the multi-model MV control. In this paper, a support vector regression based method is proposed in which all three steps of dynamic clustering, model identification and MV controller design are performed simultaneously through a data driven approach. In addition, a new support vector-based clustering method is proposed by which the data are clustered in residual space of the process model. The optimality and convergence of the proposed algorithm are studied. The proposed multi-model MV controller design technique is demonstrated through a simulated continuous stirred-tank reactor (CSTR) example, which is operated in varying conditions.

最小方差 (MV) 基准是一种量化，广泛应用于将控制回路的实际性能与其最佳性能进行比较。对于线性系统，MV 基准已经得到了很好的研究，并且已经提出了几种行之有效的技术来评估系统的性能。但是，这些技术在某些情况下可能无法提供令人满意的性能。例如，从多模型系统采样的操作数据不满足传统方法所需的平稳条件。因此，不能应用线性 MV 基准测试技术。为多模型系统设计 MV 控制器的一种可能方法是使用动态聚类方法分离和标记数据。然后，可以通过多模型 MV 控制来确定 MV 基准。在本文中，提出了一种基于支持向量回归的方法，其中通过数据驱动的方法同时执行动态聚类、模型识别和 MV 控制器设计这三个步骤。此外，提出了一种新的基于支持向量的聚类方法，将数据聚类在过程模型的残差空间中。研究了该算法的最优性和收敛性。通过在不同条件下运行的模拟连续搅拌釜反应器 (CSTR) 示例演示了所提出的多模型 MV 控制器设计技术。提出了一种新的基于支持向量的聚类方法，将数据聚类在过程模型的残差空间中。研究了该算法的最优性和收敛性。通过在不同条件下运行的模拟连续搅拌釜反应器 (CSTR) 示例演示了所提出的多模型 MV 控制器设计技术。提出了一种新的基于支持向量的聚类方法，将数据聚类在过程模型的残差空间中。研究了该算法的最优性和收敛性。通过在不同条件下运行的模拟连续搅拌釜反应器 (CSTR) 示例演示了所提出的多模型 MV 控制器设计技术。