This research is concerned with the problem of parameter identification for ship response model. A novel nonlinear innovation–based algorithm is proposed by use of the hyperbolic tangent function and the stochastic gradient algorithm. In order to demonstrate the validity of the algorithm, two identification experiments are adopted by the “Galaxy” ship and the “Yupeng” ship. Furthermore, the comparison experiment is illustrated to verify the effectiveness of the proposed algorithm, including the least square algorithm, the traditional stochastic gradient algorithm and the improved nonlinear innovation–based stochastic gradient algorithm. The identification results indicate that the improved stochastic gradient algorithm is with higher accuracy by 95.2% than the original algorithm and 11.75% than the least square algorithm. In addition, the proposed algorithm is with advantages of fast speed and high accuracy of identification. That can be extended to other parameter identification systems with the limited test data.
This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models.
In this manuscript, a concept of modifying the results of the existing robust controller decorated by a nonlinear S function is presented to improve the system performance. A case-based study of level control of water tanks illustrates the effectiveness of nonlinear decoration in improving robustness and controlling energy-saving performance with an S-function-decorated robust controller. The performance of the controlled system was analyzed through Lyapunov stability theorem and robust control theory, and was evaluated with a performance index. By demonstrating three comparing simulations of different scenes, it testifies to the fact that the nonlinear decorated robust controller meets the requirement of improving the system performance index. Compared with the nonlinear feedback and the fuzzy control, the performance index of the system using a nonlinear decorated controller is reduced by more than 10% with satisfactory robustness. This nonlinear decorated robust controller is proven to be energy efficient, simple and clear and easy to use, valuable for extensive application.
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