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Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.

Under unforeseen circumstances, a hierarchical path planning (HPP) scheme combining global and local tasks of an unmanned surface vehicle (USV) is proposed by devising fuzzy artificial potential field (FAPF). Within the HPP scheme, the elite reservation, diversity increment, adaptive mutation probability and adaptive genetic algorithm are incorporated to generate optimally sparse waypoints together with smooth path. To exclusively accommodate unpredictable environments, the innovative FAPF method with fuzzy decision-making is developed to remove singular dilemmas, pertaining to which the obstacles on the path make the USV hesitate. By inserting virtual returning points onto the global path, a hybrid compound combining the global and local paths is established. Finally, the simulations and comparisons in various geographical areas demonstrate the effectiveness and superiority of the proposed HPP scheme.

A variable-fuzzy-predictor-based predictive control approach is presented to solve the dynamic trajectory tracking problem of an autonomous underwater vehicle (AUV) in a three-dimensional underwater environment. To adapt to the characteristics of AUV’s motion such as nonlinearity and time-varying dynamics, a predictive controller framework is proposed based on a variable fuzzy predictor whose structure and parameters are both online adjusted. To achieve a precise estimation of AUV’s motion, the variable multi-dimensional fuzzy predictor employs a sliding data window (SDW) as the system status observer, and uses the Delaunay triangulation partition method for model construction. The predictive control scheme takes advantages of the constraints-tolerance of predictive control, the uncertainty immunity of fuzzy logic calculation, and the adaptability of variable fuzzy model. The comparative simulation of the AUV trajectory tracking control is conducted in the scenario of underwater operation of an offshore platform, and the result demonstrates the feasibility and effectiveness of the proposed control strategy in respect of accuracy and stability.

In this paper, suffering from multiple unknowns including unmodeled dynamics, uncertainties, and disturbances, a fuzzy unknown observer-based robust adaptive path following control (FUO-RAPFC) scheme for an underactuated surface vehicle (USV) is proposed. Main contributions are as follows: (1) A surge-guided line-of-sight (SGLOS) guidance law is created to guide surge speed and heading angle, simultaneously, and thereby adapting to path following errors to enhance robustness and flexibility; (2) By virtue of combining auxiliary observation dynamics with adaptive approximation error compensation, the FUO is innovatively devised to accurately lumped unknowns rather than bounded observations; (3) FUO-based robust adaptive tracking control laws are eventually synthesized and ensure that the guided signals can be globally asymptotically tracked. Simulation studies are conducted to demonstrate remarkable performance of the FUO-RAPFC scheme.

A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun ’s full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.

This paper proposed a modular tide level prediction model based on nonlinear autoregressive exogenous model (NARX) neural network in order to improve the accuracy of tide prediction. The model divides tide data into two parts: the astronomical tide data affected by celestial tide generating force, and non-astronomical tide data affected by various environmental factors. NARX neural network and harmonic analysis are used to simulate and predict the non-astronomical and astronomical part of tide respectively, and then the final result is obtained by combining the two parts. In this paper, the tide data from Yorktown, USA, are used to simulate the prediction of tide level, and the results are compared with the traditional harmonic analysis (HA) method and Genetic Algorithm-Back Propagation (GA-BP) neural network. The results show that as a dynamic neural network, NARX neural network modular prediction model is more suitable for the analysis and prediction of time series data and has better stability and accuracy.

In order to improve the accuracy of tide prediction, a modularized tide real-time prediction model based on wavelet neural network is proposed. The tides are divided into astronomical tides affected by the tidal force of celestial bodies and non-astronomical tides affected by environmental factors. The harmonic analysis method and WNN (wavelet neural network) are used to forecast the astronomical tide and non-astronomical tide respectively. The final tidal forecast data is composed of the output results of the two methods. In the prediction of non-astronomical tides, the parameters of wavelet neural network based on GA (genetic algorithm) are optimized to improve the prediction accuracy. The tide prediction simulation is carried out by using the measured tide data of old port Tampa tide station. The simulation results show that the method has high prediction accuracy.

In order to scientifically and reasonably establish the safety assessment model of the precaution area of the Ships’ Routing System and better guarantee the navigation safety in the complex navigation environment of the precaution area, a security evaluation model of precautionary area is proposed based on improved entropy weight TOPSIS. Firstly, some representative safety evaluation indicators are selected and the index system is established through the analysis of the current research status. Then based on the entropy weight TOPSIS method for safety evaluation, a penalty-incentive variable weight function is introduced to adjust the weight of each evaluation index by tuning the sensitivity of the over-deterioration and over-optimization index values. Finally, a security evaluation model of the precaution area based on the improved entropy weight TOPSIS method is constructed. The precaution area "A" of the Ships’ Routing System of Yangtze River Estuary is selected as an example to conduct risk assessment, and its safety level is obtained. The evaluation result is compared with that of AHP matter element method to verify the effectiveness of the model.

Freight traffic of waterways is an important indicator for evaluating freight volume and the basis for decision-making management of freight traffic of waterways. Therefore, the forecast of freight traffic of waterways has important research significance. Grey system theory can make better predictions for small sample data and has a wide range of applications in the fields of economics and industrial control. Therefore, this paper intends to use the GM (1, 1) model in the grey model to predict the freight traffic of waterways. However, because a single grey prediction model has a single change rule about exponent, it is difficult to use the grey model alone to make more accurate predictions of fluctuating data such freight volume. In order to improve the prediction accuracy, the paper makes a residual correction to the model. Finally, the data of freight traffic of waterways from 2007 to 2016 is used as a sample to predict the data of 2017 and 2018 for case analysis and model verification. The research results show that the GM (1, 1) model based on the residual error correction is a suitable model and can better forecast the freight volume of water transportation.

For analyzing the distribution characteristics of maritime traffic dangers and ensure the safety of ships navigating at sea, plotting the ship’s trajectory and making statistic based on the relevant dynamic data that can be extracted and analyzed from the Automatic Indentification System (AIS) information, the Distance to Closet Point of Approach (DCPA) between any two ships have been computed and applied. Through constructing the innovative three-dimensional bubbles chart to reflect the spatial characteristics from the DCPA matrix. Utilizing the Kernel Density Estimation (KDE) method, draws the heat map in the vicinity of San Diego sea area, measures the traffic danger characteristics of the costal sea area, and compares the analysis results with ArcGIS Pro 2.3. It reflects that the analysis results are consistent with the actual situation. The heat map has the powerful scientificity and practicability, can accurately measure the traffic distribution in the sea area, and provides auxiliary references for the planning and design of the ship's routing system and maritime traffic safety management.

The control of the ship's course determines the correctness of navigation, ensures the correct destination, and plays a significant role in the navigation of the ship. Based on RBF neural network and ship mathematical model, a predictive controller using Euclidean distance to calculate center vector for network learning is designed to predict and adjust the three parameters of PID in real time and online, so as to obtain the optimal PID parameters faster. By supervised learning ensuring the course to meet the requirements and the stable navigation of the ship. Then the simulation experiment is carried out to verify the accuracy of the designed controller in Simulink environment of MATLAB software. Compared with the traditional PID controller, this method of this paper reduces the overshoot of the system and the setting time, and improves the accuracy of the ship's course automatic control simultaneously. In addition, the simulation results of ship course control simulation demonstrate the better adaptability, robustness and anti-interference ability of the intelligent PID control strategy.

In order to make tide prediction more efficient and accurate, this paper proposes a method based on adding momentum term to optimize wavelet neural network for modular tide prediction. In the study, the tides are divided into astronomical tides which are affected by the tidal force of celestial bodies and non astronomical tides which are affected by environmental factors and other factors. The harmonic analysis method and wavelet neural network are used to predict the tides respectively. When using wavelet neural network to predict non astronomical tide, the method of adding momentum term is used to optimize the weight and parameters of wavelet neural network to improve the prediction accuracy. The simulation experiment is carried out by using the data measured at Tampa tide station in western Florida. The results show that the tide prediction model can improve the prediction accuracy and is efficient and feasible.

Real-time prediction of ship roll motion is vital for marine safety and efficiency of operations onboard the ship. However, ship roll motion is a complex time-varying nonlinear process which varies with various sailing conditions as well as time-varying environmental factors. To achieve precise real-time ship roll prediction, an ensemble prediction scheme is constructed by combining the discrete wavelet transform (DWT) method with the variable-structure radial basis function (RBF) network. The DWT is used to reduce the time-series data redundancies and carry the data information in few significant uncoupled sub-series, thus facilitate the identification and prediction by using the variable RBF networks. The variable RBF networks are used to represent time-varying dynamics with both the structure and parameters are tuned in real time. The DWT-transform-based variable RBF networks are used to represent the time-varying nonlinear dynamics of ship roll movement during ship maneuvering. The effectiveness of the proposed DWT-based real-time roll prediction scheme is demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected during sea test of M.V. YuKun.

Vessel traffic flow forecasting is indispensable for the development of national shipping industry and the coordinated development of regional economy. In this paper, an improved PSO-BP (particle swarm optimization-back propagation) prediction model is established for the prediction of the total vessel traffic flow in a designated port area. The presented prediction model is referred to as SAPSO-BP neural network which utilizes the SAPSO (self-adaptive particle swarm optimization) algorithm to adjust the structure parameters of BP neural network. Facilitated by the establishment of foundation networks and satellite communication of Automatic Identification System (AIS) receivers, the detailed information of vessel is becoming increasingly obtainable. Therefore, a large number of real-observed vessel traffic flow data based on AIS records of Port area of Los Angeles (LA) has been chosen as the testing database to validate the effectiveness of the SAPSO-BP prediction model in vessel traffic flow forecasting. The grey correlation analysis (GCA) is employed to confirm the input dimension of the prediction model. Finally, simulation results demonstrate that the presented prediction approach can achieve vessel traffic flow trend predictions with reasonable, satisfactory convergence and stability.

Motivated by the challenging difficulty in tracking an uncertain marine vehicle (MV) with unknown dynamics and disturbances to any unmeasurable/unknown trajectory, which is unresolved, an adaptive universe-based fuzzy control (AUFC) scheme with retractable fuzzy partitioning (RFP) in global universe of discourse (UoD) is created to achieve global asymptotic model-free trajectory-independent tracking. By defining an error surface and intensively exploring the MV structure, tracking error dynamics are sufficiently trimmed via separating external unknowns including trajectory dynamics and disturbances from internal nonlinearities dependent on tracking errors. An innovative retractable fuzzy approximator (RFA) using the RFP is developed to estimate internal nonlinearities and does not require a priori knowledge on the UoD, thereby contributing to a globally adaptive approximation based control approach in conjunction with Lyapunov synthesis. Together with RFA residuals, external unknowns are globally dominated by adaptive universal compensators driven by tracking error surface. Eventually, tracking errors and their derivatives globally asymptotically converge to the origin and all other signals of the closed-loop system are bounded. Simulation studies demonstrate superior performance of the proposed AUFC scheme in terms of both tracking and approximation.

Real-time prediction of tidal level is of great significance for activities of human beings in the fields of marine and coastal engineering. However, the disturbance factors of tidal level are very...

Suffering from complex sideslip angles, path following control of an under actuated surface vehicle (USV) becomes significantly challenging and remains unresolved. In this paper, a finite-time observer based guidance and control (FOGC) scheme for path following of an USV with time-varying and large sideslip angles and unknown external disturbances is proposed. The salient features of the proposed FOGC scheme are as follows: 1) time-varying large sideslip angle is exactly estimated by a finite-time sideslip observer, and thereby contributing to the sideslip-tangent line-of-sight guidance law which significantly enhances the robustness of the guidance system to unknown sideslip angles which are significantly large and time-varying; 2) a finite-time disturbance observer (FDO) is devised to exactly observe unknown external disturbances, and thereby implementing FDO-based surge and heading robust tracking controllers, which possess remarkable tracking accuracy and precise disturbance rejection, simultaneously; and 3) by virtue of cascade analysis and Lyapunov approach, global asymptotic stability of the integrated guidance-control system is rigorously ensured. Simulation studies and comparisons are conducted to demonstrate the effectiveness and superiority of the proposed FOGC scheme.

A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the period 1900–2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ∼12 months, except for the representative grid of the northwestern region.

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system (ANFIS) model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules: the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system (FIS) structure, three approaches which include grid partition (GP), fuzzy c-means (FCM) and sub-clustering (SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

A variable-structure online sequential extreme learning machine (OS-ELM) is proposed by incorporating a hidden units pruning strategy. As conventional OS-ELM increases network dimensionality by adding newly-received samples as hidden units, the hidden layer dimension would expand and result in “dimensionality curse” finally. Furthermore, the vast number of hidden units cannot represent time-varying dynamics adaptively and would deteriorate the network generalization capability. Therefore, there is a practical need to adjust the dimension of OS-ELM not only by adding hidden units but also by simultaneously pruning superfluous units which contribute less to the output. To evaluate the individual contribution of existing hidden units, an index is proposed referred to as normalized error reduction ratio. The variable structure OS-ELM adds newly received samples in hidden units, and prunes those units contribute less to current dynamics from network simultaneously, thus the resulted network possesses parsimonious structure which can represent current system dynamics more efficiently. The online network structure adjustment approach can handle samples which are presented one-by-one or chuck-by-chuck. The variable-structure OS-ELM (VS-OSELM) can be implemented for online identification and prediction of time-varying systems. In this study, to evaluate the efficiency of VS-OSELM, it was implemented for real-time prediction of tidal level change which is a complex time-varying process. Online tidal prediction simulations is conducted based on the real measured tidal and meteorological data of Old Port Tampa in Florida, United States. Simulation results demonstrate that the proposed variable-structure OS-ELM is suitable for identification and prediction of complex time-varying systems with high prediction accuracy and fast computation speed.

A novel sampling pool selection scheme is proposed for the online sequential extreme learning machine (OS-ELM) based on improved Gath-Geva (IGG) fuzzy segmentation algorithm. Tidal change is a time-varying process whose dynamics vary with changes of internal and environmental factors such as celestial bodies movements, coastal topology and environmental disturbances. When OS-ELM is implemented for identifying time-varying system dynamics, it usually sequentially selects samples with fixed number. Under such circumstance, samples representing different system dynamics are mixed together so that the online representation and prediction abilities of OS-ELM may be deteriorated. To consciously select samples with most representing ability and construct appropriate sampling pool for OS-ELM, in this study, a dynamic sampling pool selection scheme is proposed based on IGG fuzzy segmentation approach. Time series of input and output variables are segmented as per their dynamics characteristics. The change points split up the time series into several segments and the change points themselves represent the changes of system dynamics. Samples within the same segment are considered as possessing homogeneous characteristics. To achieve best representing abilities for current system dynamics, the proposed IGG-based sampling scheme is implemented for selecting sampling pool. The OS-ELM selects homogeneous samples from sampling pool thus possesses better representing ability for current dynamics. In the meantime, conventional harmonic analysis is also applied to represent the influences of celestial bodies and coastal topology. The harmonic method and IGG-based OS-ELM are combined together and the resulted modular prediction scheme is applied for online tidal level prediction of ports of King Point, Mokuoloe and Old Port Tampa in the United States. Simulation results demonstrate the feasibility and effectiveness of the proposed sampling scheme and the modular tidal prediction approach.

To attenuate the effect of uncertainties and unknown disturbances, a composite trajectory tracking control scheme using disturbance observer and neural networks (NN) is proposed for an unmanned surface vehicle (USV) in this paper. In the absence of uncertainties and unknown disturbances, by defining a nonsingular terminal sliding mode (NTSM) manifold, a NTSM-based controller is designed for the USV to guarantee the tracking errors exactly converge to zero within a finite time. In the presence of uncertainties and unknown disturbances, NN is employed to compensate uncertainties while a disturbance observer is applied to simultaneously observe NN approximation error and unknown disturbances. Simulation studies demonstrate the effectiveness of the proposed control scheme.

To improve the tidal prediction accuracy, a prediction scheme is proposed by using improved Extreme Learning Machine (IELM) based on a sliding data window. The changes of tidal level are complex processes which are influenced by not only the movement of celestial bodies but also the non-periodic meteorological factors such as wind, air pressure, and water temperature. Harmonic analysis is a traditional tidal level analysis and forecasting method. However, it cannot take into account the impact of time-varying factors. Extreme Learning Machine is a single-hidden-layer feed-forward network (SLFN) with extremely fast learning speed and good generalization performance. The algorithm of ELM has the good fitting ability for nonlinear processes. In order to achieve tidal level prediction with high accuracy, the model of improved extreme learning machine with sliding data window (SDW) is proposed, referred to as Improved Extreme Learning Machine with Sliding Data Window (IELM-SDW). The SDW is used to reflect the current changing dynamics of the tidal level. The improvement of ELM applies temporal difference (TD) learning algorithm. According to the actually received information, adjust the forecasting results. Measurements of tidal level data from Adak, Atka and Juneau are used for the simulation experiment. Comparison simulations are conducted with approaches of the conventional ELM and harmonic analysis. Comparison results demonstrate the effectiveness of the proposed IELM-SDW.

Subject to complex uncertainties, a novel surge-varying adaptive LOS (SALOS) based path following control scheme is addressed for underactuated marine vehicles (UMVs) in this paper. Main contributions are as follows: (1) The surge velocity and heading angle are guided with path following errors, simultaneously, and the unknown sideslip angle is estimated by an adaptive term online. (2) Complex unknown uncertainties including the internal parameter perturbations, external environmental disturbances and unmodelled dynamics are compositely estimated by the constructed fuzzy uncertainty observer (FUO); (3) Robust surge and heading tracking controllers based on the FUO are developed to ensure that the tracking errors and uncertainty observation errors are uniformly ultimately bounded (UUB). Simulation studies are conducted to demonstrate the effectiveness and remarkable performance of the proposed SALOS scheme.