System Identification using orthonormal basis filter
Dynamic models play key roles in model predictive control (MPC), fault tolerant control system and other model based control system. The process of developing model from experimental data is known as system identification. The widely used dynamic models for identification of linear time invariant sy...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/1872/1/ICCBPE2009A.jpg http://eprints.utp.edu.my/1872/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | Dynamic models play key roles in model predictive control (MPC), fault tolerant control system and other model based control system. The process of developing model from experimental data is known as system identification. The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous inputs (ARX) and Finite Impulse Response (FIR). Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error. In addition, ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model stuctures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few numbers of parameters (parsimonous in parameters). In addition the resulting OBF models are consistent in parameters. |
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