Wang, Meihong; (2003) Closed-loop identification using quantized data. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

A model of a system is important for applications such as simulation, prediction and control. Closed-loop identification (CLID) 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. Another is relay identification. 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 (SISO) control loop. It is based on a quantizer inserted into the feedback path. The novel contribution of this thesis is to bring the closed-loop identification with external excitation 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 identification scheme 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' scheme with expectation-maximization (EM) algorithm. The new QR algorithm can optimally estimate the model parameters and recover the underlying signal at the same time for an arbitrary number of quantizer levels.

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