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To reduce the ship wave-making resistance, the lines of the bulbous bow of a hull are optimized by an automatic optimization platform at the ship design stage. Parametric modeling was applied to the hull by using non-uniform rational basis spline (NURBS). The Rankine-source panel method was used to calculate the wave-making resistance. A hybrid optimization strategy was applied to achieve the optimization goal. A Ro-Ro ship was taken as an example to illustrate the optimization method adopted, with the objective to minimize the wave-making resistance. The optimization results show that wave-making resistance obviously reduces and the wave-shape of the near bow becomes gentle after the lines of the bulbous bow of the hull are optimized, which demonstrates the validity of the proposed optimization design strategy.

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.

Support Vector Machines (SVM) based system identification is proposed to determine the hydrodynamic coefficients in the mathematical model of ship manoeuvring motion. To reduce the complexity of the mathematical model, sensitivity analysis is performed. Particle Swarm Optimization (PSO) is employed to obtain the optimal regularization factor in SVM. Combined with free model tests, the hydrodynamic coefficients are identified by using the optimized SVM Comparison between the prediction results and the test results demonstrates the validity of the modeling method proposed.

In this paper, a novel adaptive neural network (NN) controller is proposed for trajectory tracking of autonomous underwater vehicles (AUVs) in the presence of model errors and external disturbance. A command filtered technique is used to tackle the problem of “explosion of complexity” inherent in the conventional backstepping method. Furthermore, the norm of the ideal weighting vector in neural network systems is considered as the estimation parameter, such that only one parameter is adjusted. It is also shown that the proposed NN based adaptive robust controller can guarantee the uniformly ultimately bounded of the AUV systems. Finally a numerical example is given to demonstrate the validity of the results.

The method of system based manoeuvring simulation provides an effective way to predict ship manoeuvrability. Accurate determination of the hydrodynamic derivatives in the mathematical model of ship manoeuvring motion is vital to the prediction accuracy. A support vector machines (SVM) based approach is proposed in this paper. By analyzing the data from free-running model tests of KVLCC2 ship, the hydrodynamic derivatives in an Abkowitz model are identified. To diminish the parameter drift in the identification, a difference method is adopted to reconstruct the sample for identification. To obtain the optimized structural parameters in SVM, particle swarm optimization (PSO) method is incorporated into SVM. Predictions of manoeuvring motion are presented based on the regression model. Comparisons between the predicted results and the test results demonstrate the validity of the proposed methods.

To achieve a robust autopilot for ship course-keeping, a controller based on support vector machines (SVM) and L2-gain is proposed. A cascaded plant consists of a nonlinear ship response model and the rudder actuator dynamics. Uncertainties including modeling errors and external disturbances are taken into account in the response model. SVM is applied to identify the complicated nonlinear dynamics including the modeling errors, while L2-gain design is used to suppress the uncertain disturbances. To obtain the optimized structural parameters in SVM, particle swarm optimization (PSO) method is incorporated. Stability analysis and simulation results demonstrate the validity of the proposed control scheme.Copyright © 2013 by ASME

In this article, Li+ initial inserted rate and stoichiometric window of electrode materials in Li-ion batteries are identified using Multi-Objective Genetic Algorithm (MOGA). The article is motivated by the problem of fitting both the OCP model and incremental capacity analysis (ICA) curve, which comes from OCP curve and has higher parameter sensitivity, of batteries to those experimental data. A dynamic weight coefficient is also proposed to deal with the multi-objective problem by transforming the MOGA to GA. LiCoO2 and LiFePO4 systems are investigate.

Support Vector Machine is applied to the modeling of a nonlinear dynamic system. Linear kernel is adopted in sample training and the parameters in the mathematical model are calculated by resultant lagrangian factors and support vectors. To diminish the parameter drift in identification, training samples are reconstructed by difference method. Correlation analysis demonstrates the validity of reconstruction. Based on the regressive mathematical model, the dynamics of the system is predicted and comparison between predicted results and test results confirms the parameters identified.

This paper analyzed the Electrical Fast Transient (EFT) radiation interference characteristic and effect factors of three classical structures including: the electromagnetic relay, the inductance coil and the thin wire which are commonly used in the Measuring-control System. The EFT interference radiation model has been built by the NUFDTD method. This method divides the simulation space into a sort of grids which are non-uniform. Thus it can acquire a more detailed simulation model while using less computational resource. Use this method, the changing regularity of EFT radiation field, when the different stimulate source being put on the according structure, has been studied. At the same time, the corresponding relationship of the distributing characteristic of the EFT radiation field and the device structure has been discussed. Finally, based on the analysis and discuss conclusion, according to each classical structure, some anti-interference methods have been brought forward.

Blind estimation of SIMO (single-input multiple-output) rapidly time-varying communications channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind estimation of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability/equalizability are investigated. The cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. A simulation example is presented to illustrate the proposed approach.