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International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 306-316

Published online September 25, 2024

https://doi.org/10.5391/IJFIS.2024.24.3.306

© The Korean Institute of Intelligent Systems

Design and Noise Reduction for Fuzzy Proportional-Integral-Derivative Logic Controller Using Kalman Filter

Tien Anh Tran1,2

1Faculty of Marine Engineering, Vietnam Maritime University, Haiphong, Vietnam
2Marine Research Institute, Vietnam Maritime University, Haiphong, Vietnam

Correspondence to :
Tien Anh Tran (trantienanhvimaru@gmail.com)

Received: June 9, 2020; Accepted: September 16, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Controlling diesel engine speed is essential for stable and efficient ship operation. The diesel engine speed directly affects the fuel consumption of marine diesel engines. The choice of optimal engine speed is guided by extensive research in ship energy efficiency and diesel engine speed control theory. This study investigates the above issues by proposing a novel approach. The proposed method is more effective than traditional control methods. First, the traditional proportional-integral-derivative (PID) controller of marine diesel engine speed is established. Secondly, this controller adopts online tuning through fuzzy logic control theory using the Kalman filter method. Thereafter, a fuzzy logic controller and genetic algorithm are applied to tune the traditional PID controller. This study aims to obtain the optimal diesel engine speed controller with better dynamic and static performance than the traditional control methods. The results have been compared and verified with the equivalence fuzzy PID controller. The proposed controller is useful and significant in marine engineering, as it increases the stable and responded characteristics of marine diesel engine speed controllers.

Keywords: Marine diesel engine, Modern control theory, Genetic algorithm, Fuel oil consumption, Fuzzy PID control

The author appreciates the colleagues at the Marine Research Institute, Vietnam Maritime University, Haiphong City, Vietnam, for their support.

No potential conflict of interest relevant to this article was reported.

Tien Anh Tran is a Researcher at the Department of Electrical Engineering, University of Malta, Malta. He was a Postdoctoral researcher at the Department of Naval Architecture and Ocean Engineering, Seoul National University (SNU), Seoul, South Korea (October 2022–October 2023). Currently, he is an Assistant Professor (Lecturer) at the Department of Marine Engineering, Vietnam Maritime University, Haiphong City, Vietnam, an Honorary Professor at School of Computing Science and Engineering, Galgotias University, India, and an Honorary Adjunct Professor at School of Computer Science and Engineering, Lovely Professional University (LPU), India. Additionally, he is an adjunct faculty member at SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India. He received his B.Eng. and M.Sc. from Vietnam Maritime University, Vietnam in 2011 and 2014, respectively. He got his Ph.D. Degree at Wuhan University of Technology, Wuhan, China in 2018. He is an Editor/Guest Editor for reputed journals indexed in SCI/SCIE, such as Environment, Development and Sustainability, IET Intelligent Transport System, International Journal of Distributed Sensor Networks, Sustainable Computing: Informatics and Systems, International Journal of Renewable Energy Technology, International Journal of Energy Optimization and Engineering, IEEE Internet of Things Magazine, and Mathematics. In 2015, he was awarded the Chinese Government Scholarship (CSC) for a full funding of the Doctor of Philosophy (Ph.D.) program in China. In 2019, he was awarded the NEPTUNE prize for outstanding researchers by Vietnam Maritime University. In 2022, he was one of the five outstanding scientists in Vietnam to be nominated for the Ta Quang Buu prize by the National Foundation for Science & Technology Development (NAFOSTED). Additionally, he had been selected and awarded a full scholarship for the Postdoctoral Fellowship Program of the National Research Foundation (NRF) for Foreign Researchers by the Government of South Korea.

Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 306-316

Published online September 25, 2024 https://doi.org/10.5391/IJFIS.2024.24.3.306

Copyright © The Korean Institute of Intelligent Systems.

Design and Noise Reduction for Fuzzy Proportional-Integral-Derivative Logic Controller Using Kalman Filter

Tien Anh Tran1,2

1Faculty of Marine Engineering, Vietnam Maritime University, Haiphong, Vietnam
2Marine Research Institute, Vietnam Maritime University, Haiphong, Vietnam

Correspondence to:Tien Anh Tran (trantienanhvimaru@gmail.com)

Received: June 9, 2020; Accepted: September 16, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Controlling diesel engine speed is essential for stable and efficient ship operation. The diesel engine speed directly affects the fuel consumption of marine diesel engines. The choice of optimal engine speed is guided by extensive research in ship energy efficiency and diesel engine speed control theory. This study investigates the above issues by proposing a novel approach. The proposed method is more effective than traditional control methods. First, the traditional proportional-integral-derivative (PID) controller of marine diesel engine speed is established. Secondly, this controller adopts online tuning through fuzzy logic control theory using the Kalman filter method. Thereafter, a fuzzy logic controller and genetic algorithm are applied to tune the traditional PID controller. This study aims to obtain the optimal diesel engine speed controller with better dynamic and static performance than the traditional control methods. The results have been compared and verified with the equivalence fuzzy PID controller. The proposed controller is useful and significant in marine engineering, as it increases the stable and responded characteristics of marine diesel engine speed controllers.

Keywords: Marine diesel engine, Modern control theory, Genetic algorithm, Fuel oil consumption, Fuzzy PID control

Fig 1.

Figure 1.

Marine diesel engine speed control system.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 2.

Figure 2.

Framework of marine diesel engine speed controller.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 3.

Figure 3.

General scheme of a control system using state estimation.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 4.

Figure 4.

Marine diesel engine speed controller using Kalman filter.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 5.

Figure 5.

Control model of marine diesel engine speed.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 6.

Figure 6.

Inference of the fuzzy logic controller.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 7.

Figure 7.

Fuzzy logic control rule.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 8.

Figure 8.

Surface simulation of output signals: (a) proportional gain (Kp), (b) integral gain (Ki), and (c) derivative gain (Kd).

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 9.

Figure 9.

Model of marine diesel engine speed controller in Simulink/MATLAB.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 10.

Figure 10.

Equivalent PID logic controller on the Simulink platform.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 11.

Figure 11.

Control signal .

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 12.

Figure 12.

Output signal.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 13.

Figure 13.

Unit step signal.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 14.

Figure 14.

Error derivative signal.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

Fig 15.

Figure 15.

Trajectory between output gains and input. (a) Trajectory of Kp and input. (b) Trajectory of Ki and input. (c) Trajectory of Kd and input.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 306-316https://doi.org/10.5391/IJFIS.2024.24.3.306

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