Intelligent Adaptive PID Control for Ship Motion Using Radial Basis Function Artificial Neural Networks

Maha Yousif Hasan; Noor Q. Yousif; Mohammed K. Hamzah; Huthaifa Al-Khazraji; Amjad J. Humaidi; Farouk Zouari

doi:10.51173/jt.v8i2.2915

Authors

Maha Yousif Hasan College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq
Noor Q. Yousif College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq
Mohammed K. Hamzah College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq
Huthaifa Al-Khazraji College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq
Amjad J. Humaidi College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq
Farouk Zouari University of Tunis Elmanar, Tunis, Tunisia

DOI:

https://doi.org/10.51173/jt.v8i2.2915

Keywords:

Adaptive PID Control, Radial Basis Function (RBF), Neural Network, Ship Dynamics, Motional Control, Tracking Accuracy, Tracking Performance

Abstract

This study presents an intelligent adaptive PID controller based on Radial Basis Function neural network (PID–RBF) for motion control of a ship subjected to surge, heave, and pitch dynamics. The proposed controller adjusts PID parameters based on tracking errors to perfectly follow the desired trajectory and compensate for nonlinearity and uncertainty. The numerical simulations show that the proposed adaptive PID–RBF controller outperforms the traditional adaptive PID control in terms of accuracy and dynamic performance. The control efforts required by the proposed method have been considerably reduced. In the surge channel, the control energy is reduced from the value to , while there is a reduction in control energy from to in the sense of pitch channel motion. In addition, the control signal from the proposed PID-RBF controller is smoother than that from the conventional control approach. This indicates that the proposed controller could reduce stress on the actuator and thereby keep it in a safe operating range by lowering the maximum control signal input. In addition, the results show that the proposed controller has better rejection capability and robustness than the conventional controller. The proposed PID–RBF controller demonstrates promising performance in the motion control of a ship across three motion channels (surge, heave, and pitch).

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Author Biographies

Maha Yousif Hasan, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

Noor Q. Yousif, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

Mohammed K. Hamzah, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

Huthaifa Al-Khazraji, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

Amjad J. Humaidi, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

Farouk Zouari, University of Tunis Elmanar, Tunis, Tunisia

Farouk Zouari was born in Tunis, Tunisia, on August 27, 1980. He earned an Engineering degree in Electrical Engineering in 2004, followed by a Master’s degree in Automatic Control and Signal Processing in 2005, and a PhD in Electrical Engineering in 2014, all from the National Engineering School of Tunis at the University of Tunis El Manar, Tunisia. He is currently a researcher at the University of Tunis El Manar, where he actively contributes to advancements in the field of control systems.

Dr. Zouari’s research interests encompass fractional-order systems, neural control theory, nonlinear control, and intelligent adaptive control. He has demonstrated expertise in designing robust control strategies, integrating neural networks and artificial intelligence techniques to enhance system performance. His work addresses critical challenges in dynamic systems, with a focus on innovative solutions for systems characterized by uncertainties, nonlinearities, and delays.

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