Research And Application Of Multivariable System Identification And Modeling Technique

Multivariable system identification and modeling is the theory of multivariable system's mathematic model research in production process. And it is the core technique to improve control system's performance, design the advanced control system and carry out the optimization control. In this thesis, the identification methods using input and output data of the multivariable system to obtain model were studied. Some typical methods were studied into open-loop and close-loop, and some methods were put forward to solve different problems. The theories, simulation experiments and the identification software development were included in this thesis. The identification methods of model's structure were studied in addition to identification of model's parameters. Main work is shown as following.The theory and methods of multivariable system identification were studied, emphasized on the study of least squares method, instrumental variable method and hierarchical gradient algorithm. The validity and veracity was checked by simulation experiments.Some improved algorithms were proposed for different situation. Weighted instrumental variable method and multi-innovation gradient algorithm overcome the data saturation and bad data. Extended hierarchical gradient algorithm overcomes the color noise. And two-stage hierarchical gradient algorithm solves the close-loop problem. And the simulation experiments indicated the proposed algorithm has good performance.The identification of Model's structure was studied, which included model order and lag time. The simulation experiment was given using AIC method.Identification software for multivariable system was designed and developed. It used the main methods in this thesis as its core algorithms. And it is useful to obtain model in practical process.