PLC-Based adaptive controller for stability tank pressure

Authors

  • Trong Tuong Pham Ho Chi Minh City University of Technology and Education, Vietnam
  • Ngoc Binh Le Ho Chi Minh City University of Technology and Education, Vietnam
  • Huy Hoang Pham Ho Chi Minh City University of Technology and Education, Vietnam
  • Thi Linh Nhi Tran Ho Chi Minh City University of Technology and Education, Vietnam
  • Van Phuong Ta Ho Chi Minh City University of Technology and Education, Vietnam

Corressponding author's email:

15151094@student.hcmute.edu.vn

Keywords:

sliding mode control (SMC), pressure control system, adaptive neural controller, radial basis function neural networks, artificial neural networks (ANNs), adaptive neural networks

Abstract

 In recent years, the trend of applying intelligent controllers into an industrial system has been gaining more and more attention. Artificial neural networks is a must to mention when mentioning intelligent controllers. Not only for it’s good performance but also for it’s wide range of application. With an adaptive controller, we can save time of recalibrating the controller when load changes. This paper confirms the practical effect of applying Artificial Neural Networks (ANNs) using Radial basis function (RBF) bases on Sliding mode control (SMC) to control nonlinear systems. The proposed algorithm is put into comparison with the super twisting 2-SMC, which was designed to reduce chattering and increase the performance of conventional SMC. The pressure system is controlled by a Programmable Logic Controller (PLC), which is the most commonly used in industry, with a view to applying intelligent controllers in industrial applications to increase quality, productivity and reduce downtime of current systems.

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Published

27-12-2019

How to Cite

[1]
T. T. Pham, N. B. Le, H. H. Pham, T. L. N. Tran, and V. P. Ta, “PLC-Based adaptive controller for stability tank pressure”, JTE, vol. 14, no. 5, pp. 73–79, Dec. 2019.

Issue

Vol. 14 No. 5 (2019)

Section

Research Article

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