A model of a system is important for applications such as simulation, prediction and control. Closed-loop identification (CLIO) is a means of identifying a process model while the process is still under feedback control. The motivation of this project is to find a way to do closed-loop identification while causing minimum disruption to the controlled process. There are two main categories of closed loop identification. One is closed-loop identification with external excitation (Ljung 1987, System Identification: theory for the user, Englewood Cliffs, NJ: Prentice-Hall). Another is relay identification (A strom and Hagglund I984a, Automatic Tuning of Simple Regulators with Specifications on Phase and Amplitude Margins, Alltomatim, Vo1.20, No.5. pp645-651 ). The first achievement of this thesis is the establishment of a connection between previously unrelated facts by comparing the two main categories of closed loop identification methods. Their advantages and disadvantages were highlighted through case studies. The second. and the main achievement of this thesis is to propose a new closed-loop identification scheme for a single-input-single-output (5IS0) control loop. It is based on a quantizer insel1ed into the feedback path. The novel contribution of this thesis is to bring the closed-loop identitication with external excitatiun method and the relay identification method into a unified framework for the first time. It gives recommendations about the appropriate method to use for a given quantizer interval. When the quantization interval is small, the quantization error is persistently exciting, equivalent to an external excitation. The two-stage (step) method can be applied. When the quantization interval is large. the relay method can be applied instead. Nonlinearity caused by the quantizer is analyzed. which indicates that nonlinearity increases with the quantization interval. Simulations and experiments showed that the proposed closed-loop idclItification schemc based on quantization is successful. The third achievement of this thesis is the implementation. testing and extension of a quantized regression (QR) algorithm that retrieves the underlying information from quantized signals such as those from the analogue to digital converter of a plant instrument. The algorithm is a combination of the 'Gaussian Fit' schcme with expectation-maximization (EM) algorithm. The new QR algorithm can optimally estimate the model parametcrs and recover the underlying signal at the same time for an arbitrary number of quantizer levels.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:397912 |
| Date | January 2003 |
| Creators | Wang, Meihong |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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