Combined Neural Network and PD Adaptive Tracking Controller for Ship Steering System

The ship motion is characterized by nonlinearity, time varying, uncertainty and complex interference from the environment, therefore there are certain limits in conventional PID control and self-adapting control for ship steering system. This paper combines three intelligent control technologies, that is, fuzzy control, neural network and extension control, to propose a multimode intelligent control method. Fuzzy control is utilized to solve control problem of uncertainty system, and learning ability of neural network is utilized to optimize the controller parameters. A new multi-mode transition controller based on extension control is presented and well designed in this paper, which may realize smooth switching during control process. In order to satisfy the requirements of higher accuracy and faster response of complex system, every control strategy designed can realize ideal control effect within the scope of its effective control. The simulation experiment is made to test dynamic and static performances of ship steering system under model parameter perturbation and wave interference. The simulation results show that the control system achieves satisfactory performances by implementing the multimode intelligent control.

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Adaptive Learning Based Tracking Control of Marine Vessels with Prescribed Performance

Mathematical Problems in Engineering ◽

10.1155/2018/2595721 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Zhao Xu ◽

Shuzhi Sam Ge ◽

Changhua Hu ◽

Jinwen Hu

Keyword(s):

Neural Network ◽

Adaptive Learning ◽

Tracking Control ◽

Tracking Error ◽

Adaptive Tracking ◽

Performance Constraints ◽

Tracking Controller ◽

Unknown Disturbances ◽

The Stability ◽

Marine Vessels

A novel adaptive tracking controller of fully actuated marine vessels is proposed with completely unknown dynamics and external disturbances. The model of dominant dynamic behaviors and unknown disturbances of the vessel are learned by a neural network in real time. The controller is designed and it unifies backstepping and adaptive neural network techniques with predefined tracking performance constraints on the tracking convergence rate and the transient and steady-state tracking error. The stability of the proposed adaptive tracking controller of the vessel is proven with a uniformly bounded tracking error. The proposed adaptive tracking controller is shown to be effective in the tracking control of marine vessels by simulations.

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Combined Neural Network and PD Adaptive Tracking Controller for Ship Steering System

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.13.1.8 ◽

2017 ◽

Vol 13 (1) ◽

pp. 59-66 ◽

Cited By ~ 1

Author(s):

Abdul-Basset Al- Hussein

Keyword(s):

Neural Network ◽

Sliding Mode ◽

Rbf Neural Network ◽

Approximation Error ◽

System Stability ◽

Stability And Convergence ◽

Control Law ◽

Adaptive Tracking ◽

Uncertain Dynamics ◽

Tracking Controller

In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship. Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task. The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks. The stability of the system with the combined control law guaranteed through Lyapunov analysis. Numeric simulation results confirm the proposed controller provide good system stability and convergence.

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A neural-network-based adaptive tracking controller design for a class of nonlinear systems

IEEE International Conference on Networking, Sensing and Control, 2004 ◽

10.1109/icnsc.2004.1297077 ◽

2004 ◽

Cited By ~ 1

Author(s):

Li-Wen Chen ◽

Yaojung Shiao

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Controller Design ◽

Adaptive Tracking ◽

Tracking Controller

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Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

Computational Intelligence and Neuroscience ◽

10.1155/2022/4579263 ◽

2022 ◽

Vol 2022 ◽

pp. 1-9

Author(s):

Zhijun Fu ◽

Yan Lu ◽

Fang Zhou ◽

Yaohua Guo ◽

Pengyan Guo ◽

...

Keyword(s):

Neural Network ◽

Control Method ◽

Nonlinear Identification ◽

Adaptive Tracking Control ◽

Adaptive Tracking ◽

Model Free ◽

Model Control ◽

Tracking Controller ◽

Good Identification ◽

Unknown System

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

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