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Plant modeling can be classified as white box modeling, gray box modeling or black box modeling. In a case where physical characteristics of plants are known, white box or gray box modeling is applicable. However detailed models often have very complex structures and it is very difficult to identify the parameters of them. In such cases, simplified models are often used, but sometimes they give insufficient performance. In order to overcome the problem, an identification method with combination of simplified physical models and error models is presented. In the proposed method, to enhance the performance of the identified model, we introduce error systems to predict the estimation errors of the physical models where the error systems consist of steady-error model and transient-error model which are identified separately. By compensating the estimation error using the error systems, we can obtain more accurate estimation. An application to an internal combustion engine is demonstrated to show the effectiveness of the proposed method.

Kernel Identification Method for Error Model on Engine Model Identificaion