《中国舰船研究》：“智能船艇操纵与控制”专辑双语文章推荐

智能船艇操纵与控制是平台执行具体任务的基础功能，是水路交通与海洋工程领域智能化转型升级的关键技术。它融合了船海工程、人工智能（AI）、自动控制、通信导航、系统工程等学科领域的先进成果，可提升船艇航行的决策规划、运动控制能力。

智能船艇操纵与控制的关键在于，如何将AI与传统人工驾驶经验有机结合，提升船艇应对复杂环境和任务场景的能力。例如，开发机理−数据联合驱动的新模型，实现船艇运动更准确预报；利用DQN等强化学习算法，提升任务决策与航路规划的效率。以AI为代表的先进技术，为船艇操纵与控制的理论、方法和实验带来变革新机遇。本期为大家推荐《中国舰船研究》“智能船艇操纵与控制”专辑的10篇双语文章，内容涵盖智能船艇操纵预报、决策规划与运动控制等理论方法与工程应用，呈现了船舶与海洋工程、交通运输工程等领域的最新成果，欢迎阅读。

01

规则波中船舶操纵运动预报的灰箱建模研究

Prediction of ship maneuvering motion in regular wave with gray-box modelling

【摘要】[目的]针对船舶操纵运动实时准确预报的需求，开展规则波中船舶操纵运动预报的灰箱建模研究。[方法]建立船舶操纵运动方程，以表征操纵运动机理。应用泰勒级数展开方法近似静水水动力，采用经验公式估算规则波中二阶定常波浪漂移力，形成规则波中船舶操纵运动预报的数学模型。采用傅里叶变换方法解决不同频率的操纵与耐波运动数据分离问题，基于操纵运动数据和深度神经网络（DNN）技术，构建静水水动力修正及二阶定常波浪漂移力模型，并将其代入操纵运动机理方程，形成融合机理与数据的规则波中船舶操纵运动预报灰箱模型。然后以ONRT为研究对象，分别应用灰箱模型和数学模型预报规则波中船舶操纵运动。[结果]结果显示，对于所有运动工况，仿真单位时间步长耗时平均约2～3 ms，灰箱模型预报结果与试验数据相比其相对精度均值达94.83%，相比数学模型预报精度平均提高了4.50%。[结论]灰箱预报模型可以作为规则波中船舶操纵运动预报的有效方法，能为真实海洋环境中船舶操纵运动的实时预报奠定基础。

【Abstract】[Objectives] Aiming at the requirements of the real-time and accurate prediction of ship maneuvering motion, this paper investigates the prediction of ship maneuvering motion in regular waves using gray-box modelling to improve the accuracy. [Methods] A maneuvering motion equation is proposed to reveal the known movement mechanism. The hydrodynamic forces in calm water are approximated using the Taylor series expansion, and the second-order steady wave drift forces are estimated using empirical formulas, thereby obtaining the mathematical model for predicting ship maneuvering motion in regular waves. To promote the precision of the hydrodynamic expression, the Fourier transform method is adopted to separate the data of the maneuvering and seakeeping motions. A model for hydrodynamic correction and second-order steady wave drift forces is developed on the basis of the maneuvering motion and deep neural network (DNN) data, then submitted into the mechanistic equation of maneuvering motion. Finally, a gray-box modelling incorporation mechanism and data for predicting ship maneuvering motion in regular waves is established. [Results] Taking the Office of Naval Research Tumblehome (ONRT) as an example, the maneuvering motion is predicted with the adoption of the mathematical model and gray-box model respectively. For all simulated cases, the simulation of the unit time step costs 2–3 ms on average, and the average erroraccuracy between the results of the gray-box model and experiments is 94.83%, with the accuracy promoted by an average of 4.50% compared with the mathematical model. [Conclusions] Gray-box modelling can be recognized as an efficient method for predicting ship maneuvering motion, laying a foundation for the real-time prediction of maneuvering motion in real marine environments.

02

基于误差监测机制的船舶操纵运动自适应在线建模

Adaptive online modeling of ship maneuvering motion based on error monitoring

【摘要】[目的]针对实际航行中船舶动态特性变化所导致的模型失准问题，提出一种基于误差监测机制的船舶操纵运动自适应在线建模方法。[方法]通过模型预测误差监测机制判断模型更新时机，结合滑动窗口技术和支持向量机，基于航行数据实现模型的自适应重训练更新。以KCS集装箱船为研究对象，在变航速的Z形和回转运动场景下对所提方法进行测试验证，并分析误差监测机制中的超参数选取对在线建模的影响。[结果]仿真结果表明，误差检测机制能够降低模型在线更新频率，节约计算资源。相较于离线方法，所提方法在船舶动态特性变化时能够及时更新模型，保障预测精度。[结论]所提方法适用于船舶自身属性、环境变化等引发的动力学特性变化场景，可为船舶运动在线建模与预报提供技术方法，具有实际的工程意义。

【Abstract】[Objective] Aiming to address the problem of model inaccuracy caused by ship dynamic changes during actual navigation, this study proposes an adaptive online modeling method for ship maneuvering motion based on an error monitoring mechanism. [Methods] The method determines model update timing through a model prediction error monitoring mechanism and realizes the adaptive retraining update of the model based on voyage data by combining the sliding window technique and support vector machine. Taking a KCS container ship as the research object, the method is tested and validated under zigzag maneuvering and turning circle motion scenarios with variable speed, and the influence of the error monitoring mechanism's hyperparameter selection on the online modeling is analyzed. [Results] The simulation results show that the error detection mechanism can effectively reduce the frequency of online model updating and save computational resources. Compared with the offline method, this method can update the model in time when the dynamic characteristics of the ship change, thereby guaranteeing prediction accuracy. [Conclusion] The proposed method is applicable to scenarios in which the dynamic characteristics of ships change due to their own attributes, environmental changes, etc. Thus, it has practical engineering significance by providing a technical method for the online modeling and prediction of ship motion.

03

基于导航雷达回波视频数据的占据栅格地图构建方法

Method for constructing occupancy grid maps based on navigation radar echo video data

【摘要】[目的]为解决无人艇的船载导航雷达对养殖区、浮筒、小型漂浮物等海洋漂浮障碍物感知效果不佳的问题，提出一种基于导航雷达回波视频数据构建与更新的占据栅格地图的环境感知方法。[方法]首先，采用多级集合的形式描述雷达点迹与回波点间的包含关系，为栅格地图构建奠定基础，期间，基于群相邻关系对近邻点迹进行凝聚，抑制目标分裂导致的航迹偏差；然后，利用所提的基于自然对数函数的占据栅格地图概率更新算法，通过合理利用历史数据区分海杂波与微小海洋漂浮障碍物；最后，建立基于点迹属性的栅格地图概率扩散模型，以较好地保证典型动态目标占据栅格更新的实时性。[结果]实船试验结果表明，所提方法可准确获取养殖区、浮筒等成片海洋漂浮障碍物的轮廓信息，抑制目标分裂现象；与经典方法相比，所提方法对干舷0.5 m的小型漂浮物首次发现距离提升了78.34 m，定位精度提升了1.42 m。[结论]所提方法能够实现对多种海洋漂浮障碍物、海面运动目标的准确感知，确保无人艇航行安全。

【Abstract】[ Objective] To address the poor perception of marine floating obstacles such as aquaculture areas, buoys, and small floating objects by the navigation radar of unmanned surface vessels (USVs), a unified technical solution is proposed for stable and accurate perception of various types of marine floating obstacles. [ Methods] An environmental perception method based on constructing and updating an occupancy grid map from navigation radar echo video data is presented. First, a multi-level set approach is adopted to describe the inclusion relationship between radar tracks and echo points, laying the foundation for the construction of the grid map. During this process, neighboring tracks are aggregated based on group adjacency to mitigate trajectory deviation caused by target splitting. Then, a probability update algorithm for the occupancy grid map, based on the natural logarithm function, is proposed to effectively distinguish sea clutter from minor marine floating obstacles by leveraging historical data. Finally, a probability diffusion model for the grid map, grounded in track attributes, is established to ensure real-time updates for typical dynamic targets' occupied grids. [ Results] The results from actual ship trials show that the proposed method can accurately acquire the contour information of diverse marine floating obstacles like aquaculture areas and buoys and suppress target splitting phenomena. Compared with the classical methods, the initial detection distance for small floating objects with a freeboard of 0.5 m was improved by 78.34 m, and the positioning accuracy was improved by 2.97 m. [ Conclusion] The proposed method ensures accurate perception of marine floating obstacles and moving targets on the sea surface, safeguarding the safe navigation of USVs.

04

基于改进DQN算法的船舶全局路径规划研究

Ship global path planning based on improved DQN algorithm

【摘要】[目的]为提升实际海域环境下船舶航行路径的经济性与安全性，提出一种改进深度Q网络（DQN）算法的船舶全局路径规划方法。[方法]首先，引入优先经验回放机制赋予重要样本更高的权重，提升学习效率；然后，再通过决斗网络和噪声网络改进DQN的网络结构，使其对特定状态及其动作的价值评估更加准确，并同时具备一定的探索性和泛化性。[结果]实验结果表明，在马尼拉附近海域环境下，相比于A*算法和DQN算法，改进算法在路径长度上分别缩短了1.9%和1.0%，拐点数量上分别减少了62.5%和25%。[结论]实验结果验证了改进DQN算法能够更经济、更合理地规划出有效路径。

【Abstract】[Objective] In order to improve the economy and safety of ship navigation path in actual sea environment, this paper proposes a ship global path planning method with an improved Deep Q-Network (DQN) algorithm. [Method] First, a prioritized experience replay (PER) mechanism is introduced to the DQN to give higher weights to important samples and improve learning efficiency. Next, its network structure is improved through a dueling network and noisy network, enabling it to evaluate the values of specific states and actions more accurately and generalization capabilities. [Result] An experiment is carried out in the marine environment near Manila, and the results show that compared with the A* algorithm and DQN algorithm, the improved algorithm reduces the path length by 1.9% and 1.0% respectively, and the number of turning points by 62.5% and 25% respectively. [Conclusion] It is verified that the improved DQN algorithm can plan the effective path more economically and rationally.

05

基于不均匀分配信息素及多目标优化的改进蚁群算法在无人船路径规划中的应用研究

Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles

【摘要】[目的]针对无人船在复杂水域中路径规划难度大的问题，提出一种基于不均匀分配信息素及多目标优化的改进蚁群优化（ACO）算法。[方法]采用概率路线图法（PRM）得到一条初始路径，依据该路径和终点的方位信息指导ACO算法不均匀分配初始信息素，使得初始路径和终点附近的信息素浓度大，其他栅格的信息素浓度参照与两者的距离逐渐减少，改善蚂蚁在前期路径搜索盲目性大的问题，缩短计算时间；建立求解多目标路径规划问题的目标函数，通过设定权重来平衡安全指数、能耗和路径曲折度之间的关系，为不同的应用场景生成符合需求的多样化路径，并使信息素增量随路径的优劣进行自适应调整，以强化优质路径在整个蚁群中的影响；同时，设置启发式矩阵系数的自适应调整机制，引入与迭代次数相关的余弦调节因子，以提高ACO算法的寻优效率。对路径进行二次优化以获得全局最优路径，减少航行过程中的频繁转向和转弯幅度。最后，以黄石的“仙岛湖”和杭州的“千岛湖”两个真实湖泊为地图，通过实验将所提算法与其他传统的ACO算法、A*算法和改进ACO算法进行路径规划效果的比较。[结果]结果显示，相比其他传统的ACO算法，所提算法规划的路径最短（减少61.71%），距离障碍物最远，路径曲折度最小，运行时间也得到改善。[结论]实验结果表明，所提算法可降低无人船的航行能耗，减少转弯次数与转弯幅度，提升路径的平滑性和安全性。

【Abstract】[Objective] To address the challenges of path planning for unmanned surface vehicles in complex waters, this paper proposes an improved ant colony optimization (ACO) algorithm based on uneven distributed pheromone and multi-objective optimization.[Methods] First, a probabilistic roadmap method (PRM) is used to generate an initial path. Based on the orientation information of the initial path and the endpoint, the ACO algorithm is guided to unevenly distribute the initial pheromone, resulting in higher pheromone concentration of the initial path and endpoint while decreasing the pheromone concentration of other grids in mapping according to the initial path-endpoint distance. Therefore, the problem of the ants' blindness in the preliminary path search improved, the calculation time is shortened thereof. Next, an objective function is constructed for solving the multi-objective path planning problem, and the weights are set to balance the relationship among the safety index, the energy consumption, the tortuosity, so as to providing diversified path to meet the requirement for different scenarios, moreover adaptively adjust the increment of pheromone to strengthen the influence of high-quality path in the whole ants colony based on the pros and cons of the planed paths. Meanwhile, to optimize efficiency improvement, an adaptive adjustment strategy of heuristic matrix coefficient is established, incorporating cosine modulation factors pertaining to iteration numbers. To obtain the global optimal path, quadratic optimization is carried out to reduce turns and turning amplitudes. Finally, on the basis of the maps of two real lakes—Lake Xiangdao (Huangshi) and Lake Qiandao ( Hangzhou), the experiments are conducted to compare the effects of path planning using the proposed algorithm with that of other algorithms, i.e. traditional ACO, A* algorithm and improved ACO algorithm.[Results]The results indicate that the proposed algorithm has the shortest planning paths, which is 61.71% shorter than that of the traditional ACO algorithm, the farthest distance from obstacles, and the smallest tortuosity. The running time of the algorithm is also improved. [Conclusion] The experimental results show that the proposed algorithm can reduce energy consumption during navigation, as well as the number of turns and turning amplitude, improving the smoothness and safety of the planned path.

06

基于KFESO的多无人艇分布式协同路径跟踪复合抗扰控制

KFESO-based composite anti-disturbance control for distributed cooperative path following of unmanned surface vehicles

【摘要】[目的]针对无人水面艇（USV）受高低频混合多源干扰影响，难以精确获取状态信息及保证跟踪精度的问题，提出一种基于卡尔曼滤波联合扩张状态观测器（KFESO）的多艇分布式协同路径跟踪复合抗扰控制方法。[方法]联合Kalman滤波器构造KFESO用于估计无人艇各阶状态量以及集总扰动。设计分布式状态观测器观测虚拟领航艇速度信息，并根据参考速度估计值以及KFESO输出的位置和速度估计值，基于一致性理论与视线引导律设计运动学协同控制器。在此基础上，再利用反步法与动态面控制技术设计动力学抗扰控制器。使用李雅普诺夫稳定性理论证明控制系统下所有误差信号一致最终有界。[结果]仿真结果表明，所设计的控制方法能够准确获取USV各阶状态，且在高低频混合多源干扰下仍具有良好的跟踪精度与抗干扰能力。[结论]该方法能够缓解状态观测器在估计速度与精度之间的矛盾，提高多艇协同路径跟踪精度。

【Abstract】[Objective] Due to mixed-frequency multi-source disturbances, unmanned surface vehicles (USVs) encounter challenges in accurately capturing state information and ensuring path-tracking precision. To address this issue, a composite anti-disturbance control method based on an extended state observer combined with Kalman filter (KFESO) is proposed for distributed cooperative path following of multiple USVs. [Methods] Firstly, an extended state observer combined with Kalman filter is constructed to estimate the state variables and lumped disturbances of USVs. Secondly, a distributed state observer is designed to obtain the speed information of the virtual leader. Based on the consistency theory and the line-of-sight guidance law, a kinematic cooperative controller is designed by combining the output of the KFESO and the estimated reference speed. Furthermore, a kinetic anti-disturbance controller is designed using the backstepping method and the dynamic surface control technique. The Lyapunov stability theory is employed to prove that all error signals in the control system are uniformly ultimately bounded. [Results] Simulation experiments show that the proposed method can accurately obtain the states of USVs. Under mixed-frequency multi-source disturbances, compared with the standard ESO-based control method, it has higher tracking precision and stronger anti-disturbance ability. Regarding path tracking trajectories, the proposed method achieves reduced lateral deviations and more stable trajectories. For position errors, the convergence times are comparable, but the proposed method effectively eliminates oscillations. In terms of path parameter coordination error, the proposed method can stabilize the formation, whereas the comparison method suffers from high-frequency oscillations. In terms of state estimation accuracy, the proposed method significantly improves the estimation accuracy of various state variables, enables the distributed state observer to effectively estimate the speed of the virtual leader, and achieves smaller errors in speed and control force (moment), effectively mitigating the frequent actuator response to noise. [Conclusion] This method can resolve the trade-off between estimation speed and accuracy in ESO, and improve the precision of multi-USV cooperative path following.

07

基于神经动态优化与模型预测控制的欠驱动船舶精确路径跟踪

Precise path following of underactuated ship based on neurodynamic optimization and model predictive control

【摘要】[目的]旨在解决传统模型预测控制方法采用在线滚动方式进行优化求解，造成欠驱动船舶路径跟踪预测控制器计算量大的问题。[方法]将神经动态优化系统引入模型预测控制方法，提出一种具有实时性的欠驱动船舶路径跟踪预测控制器。首先，针对船舶欠驱动特性，采用并改进视线制导策略：针对传统视线制导策略的运动学模型不确定性问题，基于滑模思想，提出鲁棒视线制导方法；更进一步，针对外界干扰影响下船舶易产生侧滑角问题，对侧滑角进行补偿，提出鲁棒自适应视线制导方法，提高系统对模型不确定性与外界干扰的鲁棒性。其次，针对欠驱动船舶输入饱和问题，通过模型预测控制方法将船舶路径跟踪问题转化为含有输入约束限制的二次优化问题。最后，针对模型预测控制方法采用在线滚动优化策略导致计算负担增加问题，基于投影递归神经网络，建立神经动态优化求解器，通过并行求解含有输入约束限制的二次优化问题，提高计算效率。[结果]经过直线和曲线路径跟踪仿真，验证了本文所提出的具有实时性的欠驱动船舶路径跟踪预测控制器能够达到任意路径跟踪的目标。对比仿真实验结果也表明所提方法相较于Fmincon优化求解器（MATLAB内置求解器）计算效率提升约90倍，具有显著优势。[结论]研究结果对于提升欠驱动船舶路径跟踪预测控制的实时性能具有一定的工程实用参考价值。

【Abstract】[Objective] The traditional model predictive control method employs a repeated online optimization approach, resulting in a high computational burden for underactuated ship path-following predictive controller. To address this issue, this paper presents an efficient predictive controller for underactuated ship path following based on the neurodynamic optimization system. [Method] First, the line-of-sight (LOS) guidance principle is employed to mitigate the underactuated problem herein; for kinematic model uncertainty in traditional LOS guidance law, a robust LOS guidance method based on the sliding mode concept is proposed. Furthermore, the sideslip angle induced by external disturbances negatively affects path following. To compensate for this effect, a robust adaptive LOS guidance method is proposed, enhancing robustness against model uncertainty and external disturbances. Second, in order to address the input saturation problem, the model predictive control is adopted herein to transform ship path following problem into the quadratic optimization problem with input constraints. Finally, the neurodynamic optimization solver is proposed based on the projection recurrent neural network herein to solve the quadratic optimization problem with input constraints, enhancing the computational efficiency.[Results]In this study, both simulations for straight line path following and curved line path following are conducted. Overall, the simulation results show that the presented efficient predictive controller can achieve arbitrary path following. Additionally, the comparative simulations are performed, revealing that the presented method exhibits advantage in computational efficiency compared to the Fmincon optimization solver. Specifically, the neurodynamic optimization solver achieves approximately a 90-fold improvement in computational efficiency compared to the Fmincon optimization solver.[Conclusion]The research results have practical value for improving the real-time performance of underactuated ship path following. In the future, the proposed real-time predictive control method will be extended to the application of multi-ship cooperative predictive control.

08

基于超螺旋滑模观测的变质量无人艇航速自适应控制

Adaptive surge control of variable-mass unmanned surface vehicle based on super-twisting sliding mode observation

【摘要】[目的]为实现对变质量无人艇在各类载荷投放任务下的精准控制，提出一种适用于质量与吃水均发生未知改变情况的变质量无人艇航速自适应控制方法。[方法]以吃水及其高阶项为自变量，对变质量无人艇操纵运动模型中，质量、吃水，以及各水动力导数项间的耦合影响关系进行解析表达。针对变质量无人艇各运动状态量与其吃水项的高相关性，设计超螺旋滑模观测器对变质量无人艇的未知吃水与质量进行观测估计，并通过李雅普诺夫理论证明观测器的有限时间稳定。基于解耦后的变质量无人艇操纵运动模型，设计航速自适应控制算法，结合超螺旋滑模观测器的观测值与控制误差对自适应控制律实时更新，根据李雅普诺夫方法验证控制系统的整体稳定性。最后，针对变质量无人艇载荷投放任务场景，开展若干工况下的仿真实验。[结果]结果表明，所设计的吃水观测算法可实现对变质量无人艇吃水与质量的精准观测。在载荷发生阶跃变化与连续变化等典型工况下，该变质量无人艇航速自适应控制算法均可实现对目标航速的稳定跟踪。[结论]研究表明，所提控制算法可适用于变质量无人艇的各类典型控制工况。

【Abstract】[Objective] This paper presents a novel approach to the precise control of variable-mass unmanned surface vehicles (USVs) during payload deployment tasks, addressing the control challenges caused by unpredictable variations in both mass and draught. The primary objective is to propose an adaptive control method that can effectively adapt to these unknown variations in mass and draught, thereby ensuring the stable and reliable operation of the USV under complex and dynamic mass conditions. [Method] First, regarding to the motion modeling of variable-mass USVs, this study analyzes the impact mechanism of mass variations on the hydrodynamic characteristics of the vehicle. It also analyzes how these variations, through changes in the parameters of the dynamic model, affect the vehicle's motion state. To address the issue that current controller design models are insufficient in analytically and intuitively representing this coupling influencing process, we use the draught term as the reference variable. The progressive coupling relationships among draught and the mass term, added mass term, added moment of inertia term, and various hydrodynamic derivatives are systematically analyzed. Based on this analysis, the mathematical model for the maneuvering motion of the variable-mass USV is then constructed. Secondly, to design an effective estimation method, a super-twisting sliding mode observer is proposed for estimating the unknown draught and mass of the variable-mass USV. This method is based on an analysis of the coupling relationships between mass variations and the vehicle's motion state and control inputs, as described in the maneuvering model of the USV. Subsequently, addressing the motion control problem of variable-mass USVs under unknown mass variations, we propose an adaptive speed control strategy based on the sliding mode observer. Specifically, leveraging the maneuvering motion mathematical model of the variable-mass USV and the draught observations from the sliding mode observer, a feedback linearization method is used to design the adaptive speed control algorithm. The asymptotic stability of the proposed control algorithm is proved using the Lyapunov theory. [Results] A series of simulation experiments are conducted to validate the proposed method. In the mass step-change observation experiment, the super-twisting sliding mode observer demonstrates satisfactory performance. Compared to the traditional sliding mode observer, the average observation errors of the draught and mass are significantly reduced by 43.75% and 43.76%, respectively. Furthermore, it shows rapid convergence when mass changes occur suddenly. In the continuous mass change observation experiment, the observer also performs excellently, exhibiting fast convergence and high accuracy, thus demonstrating significant advantages compared to the traditional observer. The speed control experiments reveal that the designed adaptive speed control algorithm can stably track the target speed under both mass step-change and continuous-change conditions. Although it may require slightly more adjustment time compared to the traditional Backstepping controller, it offers significant advantages in handling variations in mass and draught, achieving superior control performance. In the environmental disturbance experiment, while the adaptive control algorithm maintains stable speed control, demonstrating a certain degree of robustness, it also highlights the need for further improvement in the draught observation method to enhance its disturbance rejection capabilities. [Conclusions] The control algorithm proposed in this paper is well-suited for control scenarios involving unknown mass variations, such as payload launch or agricultural dispensing operations. Future research should focus on mitigating the impact of external environmental disturbance on observation accuracy and enhancing the robustness of the observation algorithm to better handle such disturbances.

09

基于改进自适应控制分配的船舶推进器故障容错控制

Fault-tolerant control for ships with thruster faults based on improved adaptive control allocation

【摘要】[目的]针对过驱动船舶在作业中因部分推进器发生故障导致推进能力下降的问题，提出一种基于改进自适应控制分配的容错控制方法。[方法]首先，设计自适应控制分配算法对含有故障推进系统的配置矩阵进行在线自适应重构，在当前推进能力条件下尽量减小推力偏差，并在自适应更新律中引入微分项，以抑制自适应过程中的推力抖振现象；然后，将未处理的控制分配误差作为系统的广义干扰，设计修正型扩张状态观测器对包含其在内的系统集总扰动进行在线估计并在控制律中进行补偿；最后，对闭环控制系统误差有界性进行证明。[结果]以自研的过驱动船舶实验样机为对象进行的仿真与模型实验结果表明，所提方法在推进器发生故障时能显著降低作业误差，使系统更快地恢复至稳定状态，并有效地改善系统的抖振现象。[结论]综上所述，所提方法能够有效应对过驱动船舶推进器失效故障，提升船舶在作业时的容错能力，对提高过驱动船舶的安全性与可靠性具有重要意义。

【Abstract】[Objective] In the process of marine resource development, some thrusters of over-actuated ships are prone to failures during operation, resulting in a decrease in propulsion power. This paper aims to propose a fault-tolerant control method based on improved adaptive control allocation to enhance the fault tolerance and the safety reliability of ship operations. [Methods] Firstly, an adaptive control allocation algorithm is designed to online reconstruct the configuration matrix of the faulty propulsion system based on the current propulsion capacity, reducing the thrust deviation. Additionally, a differential term is added to the adaptive update law to suppress the thrust jitter. Then, the unprocessed control allocation error is regarded as a lumped disturbance, which is estimated and compensated by a modified extended state observer. Finally, the boundedness of the error in the closed-loop control system is proved using Lyapunov theory, ensuring the theoretical feasibility of the method. [Results] Simulation and modeling experiments are carried out using a self-developed over-actuated ship experimental prototype. In terms of the upper limit of positioning error, the IACA method demonstrates a significantly lower upper limit of positioning error across all directions when compared to the ACA and QPCA methods. Furthermore, regarding system dynamic performance, the IACA method facilitates rapid stabilization of the system to a steady state following thruster failure. In the simulation experiments, the abrupt changes in disturbance estimation values, actual force, and thrust deviation associated with the IACA method were minimal post-failure, indicating a rapid recovery. Additionally, the adaptive parameter updates were both faster and more stable, exhibiting minimal jitter, effectively enhancing the system’s performance in terms of jitter reduction. [Conclusion] The results show that the proposed method can effectively handle the failure of over-actuated ship thrusters. Verified by experiments, it can reduce operation errors, help the system quickly return to stability, improve the jitter, enhance the fault tolerance of the ship, provide an effective strategy for the fault-tolerant control of ship thrusters, and is of great significance for the safe and stable operation of the ship. However, further research and optimization are needed in the future.

10

基于MPC-IMFAC的船舶路径跟随控制方法研究

Ship path-following control method based on MPC-IMFAC

【摘要】[目的]旨在解决环境干扰和模型不确定性下的路径跟随控制问题，特别是外部风浪环境对船舶路径跟随控制的影响。[方法]在模型预测控制（MPC）控制器的基础上，引入改进无模型自适应控制（IMFAC）作为路径跟随控制补偿器，修正船舶状态与预测状态之间的误差，以解决在突发横风和外部存在风浪等环境干扰下的模型精度不足问题，从而提高路径跟随控制精度。并以缩比KVLCC2船模为对象进行船舶路径跟随控制仿真实验。[结果]仿真结果表明，与传统MPC控制相比，MPC-IMFAC方法使船舶在突发干扰下最大绝对航向误差降低25.4%。在时变环境干扰下绝对航向平均误差减少2.6%。[结论]研究表明，该控制方法在确保路径跟随控制精度的基础上，具备较好的抗干扰能力。

【Abstract】[Objective] This study aims to solve the problem of path-following control under environmental disturbances and model uncertainties, especially the effects of external wind and wave environments. [Method] Based on a model predictive control (MPC) controller, improved model-free adaptive control (IMFAC) is introduced as the path following control compensator. The error between the ship's actual state and predicted state is corrected to solve the problem of the insufficient accuracy of the model under environmental disturbances such as sudden crosswinds and external wind waves, thereby improving the precision of path-following control. [Results] Ship path-following control simulation experiments are conducted with a scaled-down KVLCC2 ship model. As the results show, compared with traditional MPC control, the MPC-IMFAC method reduces the maximum absolute heading error of the ship by 25.4% under sudden disturbances, and the average absolute heading error decreases by 2.6% under time-varying environmental disturbances. [Conclusion] The simulation results verify that this control method possesses superior anti-interference ability while ensuring path-following control accuracy.

期刊推荐

《中国舰船研究》创刊于2006年，由中国船舶重工集团有限公司主管，中国舰船研究设计中心主办。该刊以登载舰船及相关专业新的理论方法、技术手段、设计概念及科技成果为已任，高举创新旗帜，加强技术交流，开展学术争鸣，推动理论创新与技术进步，促进舰船事业发展和海军装备建设现代化。选题范围包括：

1）舰船工程基础理论；

2）总体设计与建造新技术、新方法及新手段、新船型技术开发与应用；

3）舰船、潜器及近海结构物的结构设计与强度计算技术；

4）轮机工程及其监控技术；

5）电力推进与电气控制技术；

6）观通、导航系统与电子武备技术；

7）综合信息系统与集成平台管理技术；

8）声学总体设计与综合隐身技术；

9）电磁兼容性设计技术；

10）舰船可靠性、可维修性与全寿期综合保障性技术；

11）舰船与海洋工程相关高新技术。

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