The Experts below are selected from a list of 2049 Experts worldwide ranked by ideXlab platform

Junsheng Ren - One of the best experts on this subject based on the ideXlab platform.

  • Locally Weighted Non-Parametric Modeling of Ship Maneuvering Motion Based on Sparse Gaussian Process
    'MDPI AG', 2021
    Co-Authors: Zhao Zhang, Junsheng Ren
    Abstract:

    This paper explores a fast and efficient method for identifying and modeling Ship Maneuvering motion, and conducts a comprehensive experiment. Through the Ship Maneuvering test, the dynamics interaction between Ship and the environment is obtained. Then, the LWL (Locally Weighted Learning algorithm) underlying architecture is constructed by sparse Gaussian Process to reduce the data requirements of LWL-based Ship Maneuvering motion modeling and to improve the performance for LWL. On this basis, a non-parametric model of Ship Maneuvering motion is established based on the locally weighted sparse Gaussian Process, and the traditional mathematical model of Ship Maneuvering motion is replaced by the generative model. This generative model considers the hydrodynamic effects of Ships, and reduces the sensitivity of local weighted learning to sample data. In addition, matrix operations are transferred to the auxiliary platform to optimize the calculation performance of the method. Finally, the simulation results of Ship Maneuvering motion indicate that this method has the characteristics of efficiency, rapidity and universality, and its accuracy conforms to engineering practice

  • modified genetic optimization based locally weighted learning identification modeling of Ship Maneuvering with full scale trial
    Future Generation Computer Systems, 2019
    Co-Authors: Weiwei Bai, Junsheng Ren
    Abstract:

    Abstract This paper explores a novel nonparametric identification modeling technique for Ship Maneuvering system. In order to solve the over-learning or under-learning problem which may exist in distance metric optimization process in the locally weighted learning (LWL), a modified genetic algorithm (GA) is proposed. In our algorithm, a novel fitness function is defined, which can assign the maximum fitness to the optimal distance metric. And the performance analysis is developed based on the schema theory. Additionally, GA is a global search algorithm that the locally optimal is avoided. The LWL is applied to identify the characteristics of Ship Maneuvering motion by using the optimal distance metric. The proposed scheme improves the nonlinear mapping ability of LWL especially in the highly nonlinear area. The illustrative examples are utilized to demonstrate the effectiveness of the proposed scheme, including a synthetic data set and the YUKUN scientific research vessel of Dalian Maritime University. The simulation results indicate that the proposed scheme is a powerful modeling tool for Ship Maneuvering system.

  • grid index subspace constructed locally weighted learning identification modeling for high dimensional Ship Maneuvering system
    Isa Transactions, 2019
    Co-Authors: Weiwei Bai, Junsheng Ren, C Philip L Chen
    Abstract:

    For off-line locally weighted learning (LWL), all training data points need to be stored in memory, which would lead to a heavy computational burden, especially for large amount of training data. To avoid heavy computational burden in LWL, the grid index subspace constructed algorithm is presented for high dimensional Ship Maneuvering system in this study. First, high dimensional training data can be encoded and stored in equal interval grid, and training data are divided into grids. Second, query point is encoded by using the same strategy as in the first step, and the grid number which belongs to the query point is obtained. Third, the subspace would be per-allocated to the query point by using the grid index which has a light computational complexity. Different from the general cluster algorithm, a subspace rather than a neighborhood is assigned to query point. This way, LWL is carried out in a subspace, and the computational complexity is significantly reduced. As a consequence, real-time performance is effectively guaranteed. Finally, theoretical calculations and simulation examples are given to validate the effectiveness of the proposed scheme.

Weiwei Bai - One of the best experts on this subject based on the ideXlab platform.

  • modified genetic optimization based locally weighted learning identification modeling of Ship Maneuvering with full scale trial
    Future Generation Computer Systems, 2019
    Co-Authors: Weiwei Bai, Junsheng Ren
    Abstract:

    Abstract This paper explores a novel nonparametric identification modeling technique for Ship Maneuvering system. In order to solve the over-learning or under-learning problem which may exist in distance metric optimization process in the locally weighted learning (LWL), a modified genetic algorithm (GA) is proposed. In our algorithm, a novel fitness function is defined, which can assign the maximum fitness to the optimal distance metric. And the performance analysis is developed based on the schema theory. Additionally, GA is a global search algorithm that the locally optimal is avoided. The LWL is applied to identify the characteristics of Ship Maneuvering motion by using the optimal distance metric. The proposed scheme improves the nonlinear mapping ability of LWL especially in the highly nonlinear area. The illustrative examples are utilized to demonstrate the effectiveness of the proposed scheme, including a synthetic data set and the YUKUN scientific research vessel of Dalian Maritime University. The simulation results indicate that the proposed scheme is a powerful modeling tool for Ship Maneuvering system.

  • grid index subspace constructed locally weighted learning identification modeling for high dimensional Ship Maneuvering system
    Isa Transactions, 2019
    Co-Authors: Weiwei Bai, Junsheng Ren, C Philip L Chen
    Abstract:

    For off-line locally weighted learning (LWL), all training data points need to be stored in memory, which would lead to a heavy computational burden, especially for large amount of training data. To avoid heavy computational burden in LWL, the grid index subspace constructed algorithm is presented for high dimensional Ship Maneuvering system in this study. First, high dimensional training data can be encoded and stored in equal interval grid, and training data are divided into grids. Second, query point is encoded by using the same strategy as in the first step, and the grid number which belongs to the query point is obtained. Third, the subspace would be per-allocated to the query point by using the grid index which has a light computational complexity. Different from the general cluster algorithm, a subspace rather than a neighborhood is assigned to query point. This way, LWL is carried out in a subspace, and the computational complexity is significantly reduced. As a consequence, real-time performance is effectively guaranteed. Finally, theoretical calculations and simulation examples are given to validate the effectiveness of the proposed scheme.

C Philip L Chen - One of the best experts on this subject based on the ideXlab platform.

  • grid index subspace constructed locally weighted learning identification modeling for high dimensional Ship Maneuvering system
    Isa Transactions, 2019
    Co-Authors: Weiwei Bai, Junsheng Ren, C Philip L Chen
    Abstract:

    For off-line locally weighted learning (LWL), all training data points need to be stored in memory, which would lead to a heavy computational burden, especially for large amount of training data. To avoid heavy computational burden in LWL, the grid index subspace constructed algorithm is presented for high dimensional Ship Maneuvering system in this study. First, high dimensional training data can be encoded and stored in equal interval grid, and training data are divided into grids. Second, query point is encoded by using the same strategy as in the first step, and the grid number which belongs to the query point is obtained. Third, the subspace would be per-allocated to the query point by using the grid index which has a light computational complexity. Different from the general cluster algorithm, a subspace rather than a neighborhood is assigned to query point. This way, LWL is carried out in a subspace, and the computational complexity is significantly reduced. As a consequence, real-time performance is effectively guaranteed. Finally, theoretical calculations and simulation examples are given to validate the effectiveness of the proposed scheme.

Rudy R Negenborn - One of the best experts on this subject based on the ideXlab platform.

  • adaptive control for autonomous Ships with uncertain model and unknown propeller dynamics
    Control Engineering Practice, 2019
    Co-Authors: Ali Haseltalab, Rudy R Negenborn
    Abstract:

    Motion control is one of the most critical aspects in the design of autonomous Ships. During Maneuvering, the dynamics of propellers as well as the craft hydrodynamical specifications experience severe uncertainties. In this paper, an adaptive control approach is proposed to control the motion and trajectory tracking of an autonomous vessel by adopting neural networks that is used for estimating the dynamics of the propellers and handling hydrodynamical uncertainties. Considering that the Maneuvering model of a vessel resemble a nonlinear non-affine-in-control system, the proposed neural-based adaptive control algorithm is designed to estimate the nonlinear influence of the input function which in this case is the dynamics of propellers and thrusters. It is also shown that the proposed methodology is capable of handling state dependent uncertainties within the Ship Maneuvering model. A Lyapunov-based technique and Uniform Ultimate Boundedness are used to prove the correctness of the algorithm. To assess the method's performance, several experiments are considered including trajectory tracking simulations in the port of Rotterdam.

Vishwanath Nagarajan - One of the best experts on this subject based on the ideXlab platform.

  • Bifurcation analysis of a high-speed twin-propeller twin-rudder Ship Maneuvering model in roll-coupling motion
    Nonlinear Dynamics, 2016
    Co-Authors: Anil Kumar Dash, Vishwanath Nagarajan
    Abstract:

    In this paper, bifurcation analysis of a high-speed twin-propeller twin-rudder Ship Maneuvering mathematical model has been carried out. Surge, sway, yaw, and roll are the degrees of freedom considered in the model. Coupling of roll with sway and yaw motion during zigzag and turning maneuvers is shown. Hopf, fold, and period-doubling-type bifurcations are identified by allowing one-parameter numerical continuation of equilibrium. The vertical center of gravity is considered as the bifurcation parameter. Physical behavior of roll motion and capsize during the bifurcations are discussed. The bifurcation analysis is carried out using MATCONT. MATCONT is an MATLAB-based program that computes curves of equilibrium and its bifurcation points for any dynamical system. Influence of the wind on roll angle during turning maneuver is shown.

  • A Stochastic Response Surface Approach for Uncertainty Propagation in Ship Maneuvering
    International shipbuilding progress, 2014
    Co-Authors: Anil Kumar Dash, Vishwanath Nagarajan
    Abstract:

    The uncertainty of various coefficients of 8 degrees of freedom (DoF) twin-propeller twin-rudder (TPTR) Maneuvering mathematical model has been determined. A stochastic response surface method (SRSM) is developed for propagating these uncertainties to the full scale simulations of Ship maneuvers. The proposed SRSM uses Hermite polynomial chaos (PC) expansion of standard random variables (SRVs) for analysis. Here SRVs are Gaussian variables. The SRSM approximates all model inputs and outputs as a function of SRVs and develops an approximate surrogate model for a specific output. The SRSM treats any deterministic model as a “black-box”. SRSM requires less computational time as compared to standard Monte Carlo Simulation (MCS) method. Full scale simulations of a TPTR model installed with gas turbine propulsion at a cruising speed of 30 knots have been carried out. There are 60 uncertain inputs and 14 uncertain outputs present in the model. A linear sensitivity study has been carried out to select a set of most sensitive inputs for each one of the different outputs. Only the sensitive inputs are considered for computing the output distribution. Asymmetric behavior and uncertainty in the characteristics of twin-propeller twin-rudder system are significant. Uncertainty for overshoot angle, advance, tactical diameter, propeller revolution and thrust, engine torque, rudder normal force and torque are presented.