Nonlinear innovation identification of ship response model via the hyperbolic tangent function

Song, Chunyu; Zhang, Xianku; Zhang, Guoqing

doi:10.1177/0959651820956531

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…4 To precisely describe these systems, they can only be modeled by nonlinear systems. 5,6 Thus, the estimation of nonlinear systems has been extensively discussed for decades. 7,8 Block-structured nonlinear systems include several typical models: the Hammerstein and Wiener models, 9 the feedback nonlinear model and so on, which can accurately approximate nonlinear dynamics in some physical, biological, and chemical processes.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Zong

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article covers the identification of the feedback nonlinear systems. The parameterized nonlinear identification model involves products of system nonlinear part parameters and system linear part parameters. To deal with these product terms, the hierarchical identification based on maximum likelihood is used to decompose the system into two subsystems. The Levenberg–Marquardt and particle swarm optimization methods are applied for identifying the two subsystems, respectively. For the purpose of enhancing the optimization capability of the basic particle swarm optimization method, some improvement strategies are constructed. Thus, the improved particle swarm optimization algorithm which combines the adaptive feedback, the linearly decreasing method and the mutational operator is applied to adaptively adjust the search strategy and improve the ergodicity. The simulation indicates that the derived estimation is effective because it can obtain high identification accuracy and fast convergence speed when identifying the feedback systems. Besides, the case study demonstrates that the proposed method can be well used to identify the electro-hydraulic-servo position system.

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Section: Introductionmentioning

confidence: 99%

“…4 To precisely describe these systems, they can only be modeled by nonlinear systems. 5,6 Thus, the estimation of nonlinear systems has been extensively discussed for decades. 7,8…”

Section: Introductionmentioning

confidence: 99%

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Zong

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Actually, most real-world industrial processes present nonlinear behavior. For decades, the study of nonlinear systems has attracted much research interest, including system identification, [1][2][3] system control, 4,5 state estimation, [6][7][8][9] and so on. In the identification area, 10 the works that are elaborated on the class of nonlinear systems concern some specific structure of models, such as black-box models, fuzzy and neural network models, and block-structured models [11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

On the combined estimation of the parameters and the states of fractional-order systems

Marzougui

Bedoui

Abderrahim

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, we propose an approach for system identification for a class of discrete-time fractional-order Hammerstein systems using only input–output data. Using a combined state and parameter estimation approach, we develop an algorithm serving to estimate simultaneously, the system parameters, the system orders, and the system states. By minimizing the defining criterion, which is non-convex and nonlinear in the parameters, the model parameters are estimated using the recursive least squares and the Levenberg–Marquardt algorithms. Next, the system states are estimated using the Luenberger observer. Then, the convergence analysis of the proposed algorithm is proved. Finally, the Monte Carlo simulation analysis and an application to Ultracapacitor system are used to demonstrate the effectiveness of the suggested method.

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“…However, considering long-term ship operation, the performance of steering gear and hull resistance are time-varying, resulting in changes in ship maneuverability. Additionally, changes in the wave flow and ship draft will influence the ship's maneuverability in real time [3]. This has a certain impact on accurate ship motion control using automatic rudders and advanced motion controllers (sliding mode control, model predictive control, etc.)…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism

Song

Zhang

2022

JMSE

Self Cite

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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.

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Nonlinear innovation identification of ship response model via the hyperbolic tangent function

Cited by 4 publications

References 17 publications

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

Hierarchical maximum likelihood identification based on Levenberg–Marquardt and particle swarm optimization for feedback nonlinear systems

On the combined estimation of the parameters and the states of fractional-order systems

Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism

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