Joint Research on Modeling- and Optimization-based Control

本文的研究目标是将系统建模、优化和控制的思想结合起来，为这些领域提供一种新的方法。1.局部建模和控制局部建模被认为是一种有效的非线性系统辨识方法。由于系统模型由多个对应于局部工作区域的局部模型组成，因此局部工作区域的选择至关重要。提出了一种利用库尔巴克判别信息(KDI)和阿凯克信息准则(AIC)自动选择合适运行模式的算法。数值仿真结果验证了该方法的有效性。这项研究的国际合作研究员、挪威科技大学Foss教授研究了通过这种方法建模的非线性系统的控制。2.基于遗传规划的非线性系统辨识发展了基于遗传规划的非线性系统辨识。该方法提供了系统模型结构，在系统辨识中起着重要作用。Wiener模型和Hammerstein模型通过这种方法被识别出来。

The object of the research is integrate the ideas of system modeling, optimization and control and develop a new approach for these fields.1.Local modeling and controlLocal modeling is known as a useful approach for nonlinear system identification. Since the system model is composed of several local models corresponding to local operating regions, selection of local operating regions is crucial. An algorithm for automatic selection of suitable operating regimes by using Kullback's discrimination information (KDI) and Akaike's information criterion (AIC). Numerical simulation results demonstrate the effectiveness of the proposed method. Professor Foss (Norwegian University of Science and Technology), international co-operating researcher of this research, addresses the control of the nonlinear systems modeled by this approach.2.Nonlinear system identification using Genetic ProgrammingNonlinear system identification based on Genetic Programming has been developed. This approach provides the system model structure, which plays an important part in system identification. Wiener and Hammerstein models are identified by this approach.