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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. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear robust track keeping control algorithm for energy efficiency from the perspective of ship motion control, based on the great significance of the twenty-first Century Maritime Silk Road. Replacing the course error e with the sin ⁡ (ω e) driven by the sine function in course keeping loop to serve as the input of the controller, the simulation results tested on the ship autopilot simulation platform show that the controller output (maximum rudder angle) decreases, and the control effect of track keeping is better than the previous one in aspects of energy efficiency and rudder shake inhibition. Otherwise, the proposed nonlinear track keeping control algorithm still works in relatively severe sea states. Substituting the nonlinear feedback for the linear feedback has the generality of control effect improving. [ABSTRACT FROM AUTHOR]

In this note, a novel adaptive constrained control algorithm is proposed for dynamic positioning vessels in the presence of input amplitude and rate saturations. In the scheme, both kinematics and kinetics loops are augmented by the auxiliary system to deal with the saturation impact. Meanwhile, for merits of the command filters and minimal leering parameter technique, the cumbersome analytic derivatives of the virtual control law need not be required in the control design, and only two adaptive parameters are updated online to stabilise the effect of the model uncertainty and the external disturbance for each subsystem. Using the Lyapunov theory, the semi‐global uniformly ultimately bounded is proved for the closed‐loop system. The effectiveness and robustness of the proposed algorithm are demonstrated by the numerical example in the presence of the simulated marine disturbance. [ABSTRACT FROM AUTHOR]

The pressure control for a liquid nature gas (LNG) ship is vital for the cargo handling system which is a nonlinear, unstable, and controllable complex system accompanied by the dynamics of time delay. To improve the control effect, this article proposed a robust controller based on closed-loop gain shaping algorithm by mirror-mapping approach. In addition, the complete mechanism models for the system are established to predict the changes of temperature, pressure, and liquid inventory in the cargo tank. The heat exchanges and evaporation of liquid are also considered. By collecting the data from the LNG ship “Dapeng,” the system models are validated. At the same time, the comparative experiment is introduced to verify the reliability and effectiveness. The scheme has been compared with the 2-DOF structure control law of modified Smith predictor. The comparative experimental results show that the scheme proposed in this note has a strong disturbance rejection ability and steady-state performance. The controller designed in this note has advantages of simplified construction method, satisfactory control effect and robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a constraint MPC (Model Predictive Control) based ship motion control strategy for Z-shaped navigation, which aims to increase ship safety and reduce crew fatigue and energy consumption costs in rough seas. The strategy uses a time-driven and event-driven dual trigger release mechanism to control the yaw path, helping to avoid hazardous navigation areas. By limiting the rudder angle and adjusting the ship's wave encounter angle, safe and stable navigation is ensured and capsizing in rough seas is effectively prevented. The mathematical model is created, and the model predictive control method is used to predict and control the motion state of the ship with constraints on the control variables. The stability of the algorithm is demonstrated using the Lyapunov stability criterion, and the MATLAB simulation platform verifies its validity, robustness, and superiority. The simulation shows that the control strategy can avoid the dangerous area of navigation under the premise of maintaining a good control effect and preventing the accident of capsizing caused by steering with large rudder angles in rough seas. The proposed strategy could be further developed for the autonomous control of MASS (Maritime Autonomous Surface Ship). • We propose an MPC-based ship control strategy for Z-shaped navigation. • Our strategy employs a time and event-triggered mechanism to control the yaw path. • By constraining rudder angles and adjusting the wave encounter angle, the strategy ensures safe navigation. [ABSTRACT FROM AUTHOR]

This note tackles the problem of path-following control for underactuated ships with actuators uncertainties and unknown environmental disturbances. A novel dynamical virtual ship (DVS) guidance principle is initially developed to deal with the assumption that 'the reference path could be generated by a virtual ship', which is normal but not workable in the current literature. In addition, a practical robust control scheme is proposed using feedforward approximation, dynamic surface control, and robust neural damping techniques. In the algorithm, the strong couplings among state variables, underactuated characteristic and the actuators uncertainties are considered to guarantee semiglobal uniform ultimate boundedness of the closed-loop system. The outstanding advantages are that the control law has a concise form and is easy to implement in practice.The control inputs of interest are two actuator-steerable variables: the main engine speed and the rudder angle. Although the uncertainties are compensated by NNs, no adaptive parameters are derived for merits of the robust neural damping technique. Finally, the experiment has demonstrated the effectiveness of the proposed scheme, comparing with the LOS self-tuning fuzzy control. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of ship maneuvering state prediction, a nonparametric identification method based on weighted multi-kernel relevance vector machine (WMRVM) is proposed to establish the motion prediction models of the vessel YUKUN. The WMRVM is composed of weight parameters and different types of kernel functions. On the one hand, RVM provides probability interpretation, its kernel functions do not have to satisfy Mercer condition; on the other hand, WMRVM improves the prediction accuracy of single kernel RVM. Taking the vessel YUKUN as the research plant, the full-scale trials satisfy the requirements of maneuverability test. The 20°/20° zigzag test data and 30° turning test data are used as the generalization verification. The prediction results obtained by the proposed algorithm are compared with those obtained by RVM and support vector regression (ε -SVR). The results show that the proposed algorithm has higher prediction accuracy and better generalization. This research can lay the foundation for the application of RVM in ship maneuvering state prediction and online identification modeling. • Based on the full-scale trial data of vessel YUKUN, the research explores a nonparametric identification of 3 DOF mathematical model of vessel YUKUN. • This research proposes a nonparametric identification modeling method for ship maneuvering state based on relevance vector machine (RVM). • This research inspired by the multi-kernel model improves the single kernel RVM to weighted multi-kernel relevance vector machine (WMRVM). • The prediction results are compared with those of RVM and support vector regression. The final comparison results show that the WMRVM has high prediction accuracy and good generalization. • This research can lay the foundation for the application of RVM in ship motion prediction and online identification modeling. This research can provide some support for model-based intelligent ship motion control. [ABSTRACT FROM AUTHOR]

This paper investigates the formation control problem of underactuated unmanned surface vehicles with unknown dynamics and ocean disturbances. To tackle the leader-follower configuration, an improved fixed-time velocity controller is proposed for the virtual vehicle to guide the followers efficiently. For the tracking control of the follower vehicles, a concise controller is designed by borrowing on backstepping and nonsingular sliding mode control techniques. A novel adaptive filter is developed to address the so called "explosion of complexity" problem in the traditional backstepping framework, which could simplify the control structure and facilitate the implementation of the proposed controller in practical engineering. Furthermore, the radial basis function neural network is employed to identify the unknown dynamics of the vehicle model. A bounded-feedback adaptive law is developed to estimate the upper bound of the ocean disturbances. That could greatly enhance the robustness and accuracy of the controller. Finally, a rigorous proof has been given to guarantee the practical fixed-time stability of the closed-loop system. Two examples are provided to demonstrate the effectiveness and superiority of the theoretical results. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of parameter identification of ship nonlinear motion mathematical model, grey wolf optimizer-support vector regression (GWO-SVR) is embedded into the nonlinear innovation processed by hyperbolic tangent function. A novel identification scheme called nonlinear innovation GWO-SVR(NGWO-SVR) is proposed for identification of ship nonlinear motion mathematical model. Based on the full-scale trial data of vessel YUKUN, the NGWO-SVR is used to identify the parameters of the nonlinear motion mathematical model. Using full-scale trial data different from the identified samples, the generalization of the parameters obtained from SVR, GWO-SVR and NGWO-SVR are verified. From the evaluation indicator of the prediction results, the parameters identified by the proposed algorithm are more generalized under the condition of effectiveness of certain accuracy. In addition, the indirect sensitivity analysis method is used to analyze the parameters of the nonlinear motion mathematical model. The research results can provide a priori characteristics for intelligent ship motion control and path planning and a reference scheme for the optimization of ship motion mathematical model in marine simulator. • Based on the full-scale trial data of vessel YUKUN, the research explores parameter identification of nonlinear motion mathematical model of vessel YUKUN. • In order to further improve the accuracy of parameter identification of ship nonlinear motion mathematical model, grey wolf optimizer-support vector regression (GWO-SVR) is embedded into the nonlinear innovation processed by hyperbolic tangent function. The nonlinear innovation GWO-SVR(NGWO-SVR) is proposed for identification of ship nonlinear motion model. • The indirect sensitivity analysis method is used to analyze the parameters of the nonlinear motion mathematical model. • The research results can provide a priori characteristics for intelligent ship motion control and path planning and a reference scheme for the optimization of ship motion mathematical model in marine simulator. [ABSTRACT FROM AUTHOR]

To further save energy and reduce carbon emissions, a simple and robust green energy-saving control algorithm was proposed under the premise that the ship achieved an excellent course-keeping effect. To this end, the controller is designed by employing the third-order closed-loop gain shaping algorithm, and the final control law is obtained via the nonlinear switching feedback technique. The robustness of the system is proved by the Nyquist stability criterion. An evaluation model is carefully established for energy-saving and carbon-reduction of the course-keeping controller. The nonlinear Nomoto and Norrbin models for MV "Yukun" are implemented for simulating and further assessing under normal and heavy sea conditions. Compared with the nonlinear decoration controller, the proposed controller enhances the ship's course-keeping accuracy, shortens the delay time and settling time of the controller, lessens the steering angle and steering frequency of the rudder, increases the robustness of the system, and reduces the comprehensive energy-saving index by 26.1% under normal sea conditions. Meanwhile, good controller performance and green energy-saving effects are more in line with the requirements of navigation practice, which is essential for ships to achieve safe, economical, green, and low-carbon navigation. • A simple and robust controller based on third-order CGSA is designed for ship course-keeping control. • The nonlinear switching feedback technology is employed to improve the energy-saving effect of the controller. • An evaluation model is carefully established for energy-saving and carbon-reduction of the course-keeping controller. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear decoration control based on perturbation of the ship longitudinal Motion Model to reduce energy loss by achieving a good anti-pitch control effect. Even when the model is perturbed, it can continue to maintain a good robust performance and energy-saving effect for multiple-input multiple-output (MIMO) unstable systems. This proposed method uses the root locus shaping technology and the weighting matrix of mathematical tools to pull the root locus of the transfer function mathematical model with unstable zero-pole and zero-pole processes to the left half-plane of the teaching practice ship "YUKUN" of Dalian Maritime University used as the research object. The closed-loop gain shaping algorithm used to design the controller is then introduced, followed by the introduction of the first-order closed-loop gain shaping algorithm to design the robust controller with the shaped model. Finally, the nonlinear power function to decorate and improve the robust controller is presented. The simulation results show that when the model is perturbed, the nonlinear decoration control improves the control effects of the ship's heave and pitch, reduces control energy, and is critical for the ship's smooth and efficient navigation. [ABSTRACT FROM AUTHOR]

To address the challenging of balancing Unmanned Surface Vessel (USV) autonomous collision avoidance and formation maintenance in uncertain environments, a formation construction and navigation decision-making strategy based on Hybrid Deep Reinforcement Learning (HDRL) is proposed in this study. The novelty of this study is that: (1) A HDRL training approach is proposed, incorporating diverse DRL for virtual leader and followers, thereby significantly enhancing the decision-making adaptability of USVs formation. (2) The multi-head attention mechanism of decentralized Critic strategy is used to enhance the attentional focus of the HDRL algorithm towards different agents, thereby effectively improving convergence speed. (3) The method employs a meticulously designed hybrid reward function and incorporates the Optimal Reciprocal Collision Avoidance (ORCA) for speed selection, thereby providing optimal speed recommendations based on the current situation. It is worth emphasizing that the method exhibits exceptional performance in terms of success rate, navigation time, average reward value, and other relevant indicators within the simulation. Finally, the method is validated through the utilization of real-time navigation data in the Panama Canal, thereby substantiating the potential engineering applicability. [ABSTRACT FROM AUTHOR]

To address the issue of decision-making for ship collision avoidance in complex encounter situations, an Autonomous Collision Avoidance Decision-making (ACAD) model based on Attention Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (ATL-TD3) algorithm for Unmanned Surface Vessel (USV) is proposed in this study. The novelty of this method is: (1) Incorporating LSTM and multi-head self-attention mechanism into the existing TD3 algorithm network enhances its focus on pertinent historical state information. Furthermore, the generalization ability of the trained model is further enhanced through optimization of the accumulated experience. (2) A comprehensive reward mechanism is designed, incorporating ship domain, arena, yaw, and other relevant factors, which solve the encounters defined in clauses 13–17 in Chapter 2 of COLREGs and immediate danger. The experiment involved a comparative analysis of the average reward curve and success rate during training under different environments. Additionally, 20 cases of Imazu classic encounters and real navigation data from the Suez Canal were utilized to validate the proposed algorithm. It is highlight that the ACAD model trained by the ATL-TD3 algorithm exhibits remarkable performance in the generalization verification, surpassing conventional collision avoidance methods. • Incorporating LSTM and MHA mechanism into the existing TD3 algorithm network enhances its focus on pertinent historical state information. • The new reward function can solve the encounters in clauses 13–17 in Chapter 2 of COLREGs and immediate danger. [ABSTRACT FROM AUTHOR]

This study investigates the problems in the automatic route planning method, which is a critical function for the safe navigation of manned and unmanned ships. We designed a route planning process by extracting the water depth information, such as the position and depth value of water depth points, using the electronic navigation chart (ENC) data. The discrete water depth points were reorganized to form a Delaunay triangulation (DT) data structure, which is the basis of the DT algorithm. The L+ algorithm was proposed and employed on the DT map to provide an appropriate planning route. Furthermore, the FO L+ (final optimization L+) algorithm was used to design an optimized planning route connected by vital waypoints only. Finally, two closed-loop experiments were performed on an electronic chart display and information system platform to demonstrate the effectiveness of the proposed algorithms. Time cost and length of routes were also given to illustrate differences of them. The results of this study indicated that different route planning approaches can be designed based on different safety depths by using the L+ and FO L+ algorithms based on ENC data, rather than grid maps or other pre-processed maps. • Accuracy: Water depth information is extracted from ENC data directly and employed in algorithms. • Innovation: A new strategy for identifying the adjacent nodes is given in the algorithms. Additionally, navigation safety is guaranteed. • Feasibility: L+ and FO L+ algorithms are applied on ECDIS platform, which demonstrates that the algorithms are feasible for practical use. [ABSTRACT FROM AUTHOR]

To solve the problems of external disturbance, internal model uncertainty and rudder angle energy input to much in ship course keeping, an active disturbance rejection control algorithm based on nonlinear feedback is proposed. Firstly, the ADRC algorithm is designed to control the ship course, and the external disturbance and internal uncertainty are estimated by the linear state observer, and then the controller is compensated. In the process of navigation, "large rudder angle" and "high rudder frequency" will increase ship roll and rudder wear. Nonlinear feedback is used to modify the error to reduce the excessive input of rudder angle energy. Zero order holder is introduced to reduce rudder frequency and rudder wear. Finally, the simulation results show that the controller has well tracking effect. The nonlinear feedback with ZOH has smaller rudder angle and frequency. In severe sea state, high rudder frequency and high rudder amplitude will cause the wear of ship steering gear, excessive heeling and reduced stability, so the algorithm proposed in this paper is more secure and energy-saving. • The ship model is simplified, external disturbance and internal disturbance are regarded as total disturbance. • The nonlinear feedback and ADRC are combined to reduce the control energy input. • ZOH is used to control output to reduce rudder frequency. [ABSTRACT FROM AUTHOR]

This paper investigates a practical fixed-time formation control strategy for underactuated autonomous surface vessels with unknown dynamics based on event-triggered mechanism. To deal with the leader–follower configuration without the information of leader velocity, the virtual vessel is designed to track the leader at desired bearing and distance. A concise adaptive law is developed to compensate the adverse effect of the unknown time-varying functions and unstructured uncertainty. Furthermore, the event-triggered mechanism is introduced to facilitate the smart triggering of control inputs. Different from most of the existing works, the proposed controller is with merits of smaller computation load and less energy consumption. Theoretical analysis proves that under the presented algorithm, all the tracking errors in the closed-loop system can converge to the neighborhood of origin within fixed-time settling. Meanwhile, the convergence time is independent with the initial states of the system. Finally, numerical examples are presented to demonstrate the feasibility and superiority of the proposed algorithm. • An improved velocity regulator is designed for the virtual vessels to assist the tracking control of followers. Using only relative position, the upper bound of the leader velocity is estimated by the concise adaptive law. • A novel robust practical fixed-time formation controller is proposed for multiple underactuated ASVs with uncertain dynamics, unstructured uncertainty and unknown control input gain. • By fusion of the dynamic surface technique (DSC) and event-triggered mechanism, both the analytic derivatives of the virtual controller and the continuous update of control signals are avoided. [ABSTRACT FROM AUTHOR]

The ability to avoid shorelines, reefs and moving ships or obstacles is the premise of automatic navigation. For achieving the goal, conventional A star algorithm is improved to plan route globally. The safety distance is set between planning route and obstacles. Additionally, the planning route is further optimized to remove unnecessary waypoints, and only vital waypoints are retained for conforming to navigation practice and track keeping control. Besides, minimum course alteration algorithm (MCA) is proposed to avoid moving ships or obstacles constrained by COLREGs. The proposed algorithms are validated in simulation. The results show that the algorithms are credible in two and multi ships encounter situations, even the targets ships take unexpected course alteration and the own ship could well avoid collision with shorelines, reefs and moving ships or obstacles under external disturbance. [ABSTRACT FROM AUTHOR]

• An adaptive NN-based algorithm is embedded into the proposed fault-tolerant strategy, which do not require priori information of the system dynamics. • a concise adaptive law is developed to compensate the bound of the weight matrix rather than the whole weight matrix of NNs. • A novel event-triggered decentralized cooperative controller is presented to achieve motion synchronization of multiple ASVs and reduce the inter-vehicle communication burden. This paper investigates the problem of cooperative path-following control for underactuated autonomous surface vessels in the presence of the model uncertainties, environmental disturbance, actuator failures and limited communication. A novel intelligent controller is designed for autonomous surface vessels to track priori parameterized reference paths so that the desired formation can be achieved. In the algorithm, the model uncertainties are estimated by neural networks. By virtue of the dynamic surface control technique, the inherent problem "explosion of complexity" occurred in the traditional Backstepping design framework is avoided. A concise adaptive law is developed to compensate the upper bound of the weight matrix for neural networks, rather than the whole weight matrix. Furthermore, the event-triggered mechanism is embedded into the cooperative controller to reduce the frequency of inter-vehicle communication. The cooperative controller is updated only at the instant when the threshold condition is violated. By using the Lyapunov theory, both the semi-globally uniformly ultimately bounded and fault-tolerance capability of the closed-loop system are guaranteed. Finally, numerical examples are performed to demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the tracking control of the autonomous surface vessels (ASVs) with the time-varying disturbances. While the echo state network (ESN) accounts for the unknown dynamics in the model, a model-based event-triggered control (MBETC) scheme is presented by using the compound learning technique, which combines the learning of the ESN and the estimation of the compound disturbance. Different from the existing compound learning, the proposed scheme updates the estimates of the ESN weights and the compound disturbances in an event-triggered manner, in which two novel prediction errors are involved in their update laws. The values of prediction errors are obtained by using the online-recorded data during the inter-event time. To solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC), an event-triggered adaptive model is established to generate the continuous estimates of the states and direct the control laws. By the merit of ETC, the proposed scheme can importantly reduce the communication traffic in the measurement network compared with its continuous substitutes. By the merit of online-recorded-data-based compound learning, the proposed scheme can achieve the good understanding of synthetic uncertainties. All the errors in the closed-loop system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, a numerical example corroborates the proposed scheme. • This paper develops an event-triggered compound learning technique. • This paper develops a uniform ETC scheme in the measurement network for the ASVs. • The problem of "jumps of virtual control laws" is solved by using the adaptive model. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the parameterized LOS (line-of-sight) guidance cannot be directly applied to the navigation practice, this paper proposed an indirect nonlinear feedback concise robust track-keeping control strategy for ships, which consists of a robust course-keeping controller and a MILOS (multi-technique improved LOS) guidance system. The second-order closed-loop gain shaping algorithm was employed to design the linear course-keeping controller, then the fuzzy logic system was used to adjust the integral coefficient online to improve the transient performance. The introduction of nonlinear feedback technique further reduces the steering frequency and saves energy. For the guidance system, three techniques are introduced to enhance the track-keeping effect. A nonlinear sideslip angle observer is developed to eliminate the static track error caused by the wind and current interference. A time-varying lookahead distance and a new advance turning distance are proposed to achieve the smooth turning between track sections. Simulation results show that compared with the conventional LOS guidance, the proposed strategy is more consistent with the requirement of navigation practice and has comprehensive advantages in both dynamic and static performance, which is of great significance for enhancing the ability of ship's autonomous navigation. The novelty and contribution of this paper can be briefly concluded as following points: • The second order closed-loop gain shaping algorithm is employed to design a PID controller in series with a first order filter for course-keeping. • The fuzzy logic system is used to adjust the integrate coefficient online to improve the dynamic performance of the course-keeping system. • A nonlinear sideslip angle observer is constructed based on the conventional LOS guidance frame. • A new advance turning distance setting strategy is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural finite-time formation control algorithm for multiple underactuated surface vessels with actuator faults. In the algorithm, the leader-follower formation problem is formulated as a two-stage tracking problem. First, to address the leader-follower configuration without the information of leader velocity, the virtual vessel is designed to track the reference trajectory of the leader. Second, an adaptive finite-time fault-tolerant control (AFFTC) algorithm is presented for the follower to track the virtual vessel. By fusion of the neural network (NN) and the fractional power, the model uncertainty is approximated and the finite-time convergence is obtained. Furthermore, a concise adaptive law is developed to compensate the upper bounded of NN weights and lump disturbance which include the approximation error of NN, control gain uncertainty, actuator faults and marine environmental disturbance. On the basis of Lyapunov theory, stability analysis proves that all the signals in the closed-loop system are practical finite-time stable. Finally, numerical simulations are performed to demonstrate the performance and superiority of the proposed algorithm. • An improved adaptive velocity algorithm is designed for virtual vessels. Using only relative position, the velocity information of the leader is estimated by adaptive law. • A novel adaptive finite-time fault-tolerant control (AFFTC) algorithm is proposed for followers. By fusion of the NNs and fractional power, both the model uncertainty compensation and the finite-time results are achieved. • A concise adaptive law is developed to estimate the norm of the NNs, rather than each element of the NNs' weight. That would reduce the calculation load dramatically and facilitate the implementation of the algorithm in practical engineering. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control of a fully-submerged hydrofoil craft (FSHC) on the horizontal plane. The motions of a FSHC can be classified into three cruising conditions, namely the hulborne, the taking off, and the foilborne. Considering that the FSHC has the different model dynamics and disturbances between the three cruising conditions, we treat the FSHC model as a switched system between the hullborne and the foliborne conditions, in which the taking off condition is conveniently omitted and deemed as the fast transient. The compound learning technique is introduced to offset the uncertainties, and it is comprised of two parts. First, the fuzzy logic system (FLS) is employed to approximate the unknown model dynamics in each degree of freedom. Based on a serial-parallel estimation model (SPEM), the composite adaptive laws are fabricated to estimate the weights of the FLS by combining the tracking errors with the prediction errors between the estimation model and the dynamic loop of the FSHC. Second, the disturbance observer (DOB) is fabricated to estimate the compound disturbance, which is comprised of the approximation error of the FLS and the disturbance. Based on the compound learning, we develop the switched adaptive backstepping control laws of the FSHC. By employing the average dwell-time method, all the tracking and the estimation errors in the switched system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the numerical experiment is conducted to verify the effectiveness of the proposed scheme. • The switched control laws are fabricated to adapt to two different cruising conditions of the FSHC. • The compound learning technique is introduced to achieve the better understanding of uncertainties. • An improved DOB is constructed to fit in with the switched system. [ABSTRACT FROM AUTHOR]

The theoretical prediction of trimaran motion and load in complex environment has always been a hot issue in the field of ship and ocean engineering. Based on the calculation principle of Rankine source and free-surface Green function for solving ship-wave interaction, a new kind of nonlinear three-dimensional time-domain Rankine-Green matching method for simulating the trimaran motion and wave load in head seas is studied. In the numerical simulation, the influence of factors such as wave slamming, green water, panel space and density, are investigated systematically. And the elevation of surrounding wave surface caused by speed effect and side hull interference is also considered to modify the instantaneous impact load. In order to verify the reliability of Rankine-Green matching method, a kind of segmented trimaran model with water jet propulsion system was designed and taken in regular and irregular waves. Finally, the numerical forecasting values based on Rankine-Green matching method are compared with the results of model test and traditional frequency-domain Green function method to illustrate its advantages and application scope. • A nonlinear time domain Rankine-Green matching method towards trimaran motion and wave load is established. • The nonlinear influence caused by wave slamming and green water is studied in trimaran motion and wave load. • The interference of boundary dimension and density is analyzed for accuracy of final numerical simulation. • A kind of segmented trimaran model with water jet propulsion system was taken in regular and irregular waves. • The advantages and application scope of time domain Rankine-Green matching method is illustrated. [ABSTRACT FROM AUTHOR]