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Hyperbolic Tangent-Based Adaptive Neural Network Ship Heading Control
- 언어ENG
- URLhttps://db.koreascholar.com/Article/Detail/447545
pp.15-25
To address the issue of low heading tracking efficiency caused by nonlinear dynamic characteristics in ship heading motion, this paper proposes a neural network-based adaptive hyperbolic tangent control method for ship heading. By designing a second-order system robust controller, a saturated auxiliary design system is introduced into the regulator for direct internal compensation, enhancing the system's anti-interference capability under complex operating conditions. Meanwhile, hyperbolic tangent nonlinear modification is incorporated into the control strategy to optimize the output characteristics of control signals. The controller adopts a backstepping approach to design virtual control laws for trajectory tracking and utilizes the Radial Basis Function (RBF) of neural networks to approximate the uncertain parts of the ship model. The control algorithm is simulated and tested in the MATLAB environment, and its tracking effect is analyzed. Simulation results show that the control algorithm can ensure the stability of the closed-loop system under conditions of dynamic changes in system parameters, external disturbances, and uncertainties, and effectively solve the nonlinear problems in ship traffic control during trajectory tracking. The controller is designed concisely, meets the requirements of engineering practice, improves ship maneuverability, and has reference value for ship control.
1.Introduction
2. Problem Analysis
2.1. Mathematical Model of the Ship Heading Control System
2.2 Compensatory Auxiliary System within theController
2.3. Radial Basis Function (RBF) Neural Network
2.4. Introduction of the Hyperbolic TangentFunction
3. Controller Design
4. Stability Analysis
5. Simulation Verification
6. Conclusion
References
