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Recent Advances in Electrical & Electronic Engineering

Author(s): Ling Wang*, Dengji Li, Xuelian Hu*, Pinggai Zhang and Muhammad I. Menhas

DOI: 10.2174/2352096513999201020212639

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Controller for the Pulverizing System Based on Intelligent Virtual Reference Feedback Tuning

Page: [210 - 221] Pages: 12

  • * (Excluding Mailing and Handling)

Abstract

Background: The pulverizing system is an important part of the coal-fired unit; the safety and efficient operation of which are essential to improve the economy of the units. Due to the needs of industrial development, the pulverizing system has become increasingly complex, and it is challenging to design the optimal controllers based on traditional model-based methods.

Objective: This paper proposes an improved intelligent data-driven control method to design the optimal controller for the pulverizing system, which does not need any information on the model.

Methods: The proposed method is based on intelligent virtual reference feedback tuning and a new adaptive human learning optimization algorithm, in which adaptive human learning optimization is used to find the best values of the controller as well as the reference model to achieve the optimal control performance. The proposed method only needs a set of input and output data of the system and can avoid the influence of the model error.

Results: The results of the CEC14 benchmark functions show that the proposed algorithm possesses a better searchability than the other five binary-coding optimization algorithms. Furthermore, the simulation results on the pulverizing system demonstrate that the presented method has advantages over different control methods, including the model-based PID methods, Z-N method and so on.

Conclusion: The proposed method can easily and efficiently design the optimal controller without any model information, and it can save a lot of efforts and time for engineering applications. Therefore, it is a very promising control method.

Keywords: Pulverizing system, adaptive human learning optimization, data-driven control, virtual reference feedback tuning, intelligent virtual reference, feedback tuning, reference model.

Graphical Abstract

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