As the uncertainty of parameters and the random nature of disturbances that effect a ship’s course,one of the methods which can be used for designing a nonlinear ship course controller is the neural intelligent method. It is used here for designing a configuration of a nonlinear controller, which are then applied to ship course control. In the paper, we used fuzzy logic and neural network based on T-S model to design the controller. During the control design process, the structure of ship automatic steering and fuzzy neural network feedback controller were built. In order to enhance adaptive characteristics of the controller,the parameters of the network were optimized by a GA algorithm. The proposed course keeping method was tested in a simulation study involving a non-linear model of a ship. Simulation results show that the performance of the ship controller is valuable and easy to implement.
Given the uncertainty of parameters and the random nature of disturbance, a ship motion, is a complicated control problem. This paper has researched adaptive neural network systems and its application to ship’s motion control. In paper, Ship’s mathematical model is researched. Aimed at ship mathematical motion model, the model reference adaptive auto pilot is first designed based on the analysis of the model reference adaptive control theory. We used fuzzy logic and neural networks to design the feedback controller, used multilayer perceptron neural network to design the reference model and the ship course identification model network. Based on the fuzzy control and neural network, an intelligent adaptive control algorithm was presented in the paper. In consideration of the forces and moments from the environmental disturbance, such as winds, waves, currents, etc., Simulation experiments are carried out by using Matlab’s Simulink toolbox. The simulating result indicates the designed adaptive controller can get a good control performance for ship course tracking system.
Under the condition that the nonlinearity of ship steering model is considered and the assumption that the parameters of the model are uncertain, we proposed an adaptive control algorithm for ship course nonlinear system by incorporating the technique of neural network and fuzzy logic system. In the paper, we presented the structure and characteristics of Adaptive Neuro-Fuzzy Interference System (ANFIS), established the ship course controller, and realized an online learning algorithm to do online parameter estimation. We utilize fuzzy logic to solve the uncertainty problem of control system, neural network to optimize the controller parameters. To demonstrate the applicability of the proposed method, simulation results are presented at the end of this paper. The experiment shows that the ANFIS controller can achieve high performance control under parameter perturbation and other disturbances.
The Autopilot is importance for a ship to navigate safely and economically, so we proposes an intelligent reference modeling adaptive controller for ship steering based on neural networks. In order to satisfy the requirements of ship’s course control under various sea status, we used fuzzy logic and neural networks to design the feedback controller, used multilayer perceptron neural network to design the reference model and the identification network. In order to enhance adaptive characteristics of the controller,the parameters of membership functions and connection weights etc were revised online with neural network learning algorithm. The results of simulation shown that the performance of the ship controller is valuable and effective.
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