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Research on drum water level control of marine auxiliary boiler based on ADRC

Gan H., Lv B.

Polish Maritime Research

2018

S 2

35--41

SThe active disturbance rejection control (ADRC) exhibits strong robustness and adaptability in the presence of strong interference of a large class of uncertain systems. In order to better control the water level of the auxiliary boiler drum, this paper applies the ADRC to improve its control precision, robustness and timeliness. Firstly, the change of drum water level in a large oil tanker auxiliary boiler is analyzed and the dynamic equation is established. After the mathematic changes, the dynamic equation is transformed into a two-order system. Aiming at the characteristics of nonlinear, time-varying and strong disturbance of the boiler water level control system in marine auxiliary boiler, studying the ADRC that includes tracking differentiator (TD), extended state observer (ESO), nonlinear feedback (NLSEF) and disturbance compensation of four parts. The establishment of two kinds of two-order ADRC controller to control the drum level. Through the theoretical analysis and simulation experiment test, and comparing with the cascade control strategy, the experimental results show that the active disturbance rejection controller satisfies the steady state response index of the system, and has good timeliness and accuracy to the control of water level of the drum.

Speaker Identification using Data-Driven Score Classification

Gan H., Mporas I., Safavi S., Sotudeh R.

Image Processing & Communications

2016

Vol. 21, no. 2

55--64

We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.