Energy Optimization of the Fin/Rudder Roll Stabilization System Based on the Multi-objective Genetic Algorithm (MOGA)

Energy optimization is one of the key problems for ship roll reduction systems in the last decade. According to the nonlinear characteristics of ship motion, the four degrees of freedom nonlinear model of Fin/Rudder roll stabilization can be established. This paper analyzes energy consumption caused by overcoming the resistance and the yaw, which is added to the fin/rudder roll stabilization system as new performance index. In order to achieve the purpose of the roll reduction, ship course keeping and energy optimization, the self-tuning PID controller based on the multi-objective genetic algorithm(MOGA) method is used to optimize performance index. In addition, random weight coefficient is adopted to build a multi-objective genetic algorithm optimization model. The objective function is improved so that the objective function can be normalized to a constant level. Simulation results showed that the control method based on MOGA, compared with the traditional control method, not only improves the efficiency of roll stabilization and yaw control precision, but also optimizes the energy of the system. The proposed methodology can get a better performance at different sea states.     

Fuzzy sliding mode control method for vertical motion of autonomous underwater gliders北大核心CSCD

［Objective］This paper proposes a fuzzy sliding mode controller based on T-S fuzzy logic for the vertical plane motion control of an autonomous underwater glider (AUG) with limited actuator capability. ［Methods］In the fuzzy sliding mode controller, the fuzzy switching rate is used to replace the switching rate in the fixed time controller to effectively suppress buffeting. The fuzzy switching rate is obtained by fitting the switching rate of the fixed time controller with T-S fuzzy rules. Based on the limited capabilities of AUG actuators, a saturation auxiliary system is designed to improve the actuator saturation effect. Finally, the performance of the system is verified by Lyapunov stability analysis and numerical simulation. ［Results］The results show that the AUG under the fuzzy sliding mode controller and the saturation auxiliary system can converge in finite time. The effectiveness of the fuzzy sliding mode controller and the saturation auxiliary system are verified by numerical simulation. ［Conclusions］By making comparisons with the fixed-time controller, it is verified that the two controllers have similar control performance, and the buffeting of the fuzzy sliding mode controller is lesser. © 2022 Journal of Clinical Hepatology. All rights reserved.     

护卫舰横摇稳定舵的H∞/H2控制器的设计和比较（英文）

The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews, vessels, and cargoes; thus, it must be damped. This study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter(DEKF)–trained radial basis function neural networks(RBFNN) for the surface vessels. The autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in parallel. After analyzing the advantages of the DEKFtrained RBFNN control method theoretically, the ship's nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization system. Different sailing scenarios were conducted to investigate the motion responses of the ship in waves. The results demonstrate that the DEKF RBFNN–based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.     

ISS-based robust adaptive fuzzy algorithm for maintaining a ship＇s track

This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability （ISS） theory used to verify the system is input-to-state stable. Combining the Nussbaum gain with backstepping techniques, a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system. It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded. Consequently, a ship＇s linear track-keeping control can be implemented. Simulation results using Dalian Maritime University＇s ocean-going training ship ＇YULONG＇ are presented to validate the effectiveness of the proposed algorithm.     

Global robust and adaptive output feedback dynamic positioning of surface ships

A constructive method was presented to design a global robust and adaptive output feedback controller for dynamic positioning of surface ships under environmental disturbances induced by waves, wind, and ocean currents. The ship’s parameters were not required to be known. An adaptive observer was first designed to estimate the ship’s velocities and parameters. The ship position measurements were also passed through the adaptive observer to reduce high frequency measurement noise from entering the control system. Using these estimate signals, the control was then designed based on Lyapunov’s direct method to force the ship’s position and orientation to globally asymptotically converge to desired values. Simulation results illustrate the effectiveness of the proposed control system. In conclusion, the paper presented a new method to design an effective control system for dynamic positioning of surface ships.     

带不确定动态的欠驱动水面船舶鲁棒自适应路径跟踪控制(英文)

A robust adaptive control strategy was developed to force an underactuated surface vessel to follow a reference path,despite the presence of uncertain parameters and unstructured uncertainties including exogenous disturbances and measurement noise.The reference path can be a curve or a straight line.The proposed controller was designed by using Lyapunov’s direct method and sliding mode control and backstepping techniques.Because the sway axis of the vessel was not directly actuated,two sliding surfaces were introduced,the first one in terms of the surge motion tracking errors and the second one for the yaw motion tracking errors.The adaptive control law guaranteed the uniform ultimate boundedness of the tracking errors.Numerical simulation results were provided to validate the effectiveness of the proposed controller for path following of underactuated surface vessels.     

牵引车-飞机系统的路径跟踪控制(英文)

An aircraft tractor plays a significant role as a kind of important marine transport and support equipment. It’s necessary to study its controlling and manoeuvring stability to improve operation efficiency. A virtual prototyping model of the tractor-aircraft system based on Lagrange’s equation of the first kind with Lagrange mutipliers was established in this paper. According to the towing characteristics, a path-tracking controller using fuzzy logic theory was designed. Direction control herein was carried out through a compensatory tracking approach. Interactive co-simulation was performed to validate the path-tracking behavior in closed-loop. Simulation results indicated that the tractor followed the reference courses precisely on a flat ground.     

ISS-based robust adaptive fuzzy algorithm for maintaining a ship's track

This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability(ISS) theory used to verify the system is input-to-state stable. Combining the Nussbaum gain with backstepping techniques,a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system. It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded. Consequently,a ship's linear track-keeping control can be implemented. Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.     

A nonlinear model reference adaptive inverse control algorithm with pre-compensator

In this paper, the reduced-order modeling （ROM） technology and its corresponding linear theory are expanded from the linear dynamic system to the nonlinear one, and H∞ control theory is employed in the frequency domain to design some nonlinear system＇ s pre-compensator in some special way. The adaptive model inverse control （AMIC）theory coping with nonlinear system is improved as well. Such is the model reference adaptive inverse control with pre-compensator （PCMRAIC）. The aim of that algorithm is to construct a strategy of control as a whole. As a practical example of the application, the nunlerical simulation has been given on matlab software packages. The numerical result is given. The proposed strategy realizes the linearization control of nonlinear dynamic system. And it carries out a good performance to deal with the nonlinear system.     

Design of an adaptive controller for dive-plane control of a torpedo-shaped AUV

Underwater vehicles operating in complex ocean conditions present difficulties in determining accurate dynamic models. To guarantee robustness against parameter uncertainty, an adaptive controller for dive-plane control, based on Lyapunov theory and back-stepping techniques, was proposed. In the closed-loop system, asymptotic tracking of the reference depth and pitch angle trajectories was accomplished. Simulation results were presented which show effective dive-plane control in spite of the uncertainties in the system parameters.     

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

1 Introduction1 Recently, adaptive control approaches based on fuzzy neural network (FNN) have been studied in a lot of papers [1–4]. FNN combines the capability of fuzzy reasoning in handling uncertain information and the capability of artificial neural…     

船舶航向非线性系统的模型参考模糊自适应控制

考虑船舶航向控制系统模型中存在不确定非线性函数，并假设该函数是连续的，在以模糊系统对该函数进行逼近的基础上，利用Lyapunov理论，提出了一种新的模型参考模糊自适应控制算法。其特点是，无论取多少条模糊系统规则，自适应学习的参数只有一个，便于工程实现，而且还确保闭环系统渐近稳定，并使系统的模型跟踪误差为零。最后以远洋实习船“育龙轮”为倒，进行了船舶航向模型参考模糊自适应自动舵设计，并利用Matlab工具箱进行了仿真研究，结果证明该算法十分有效。     

基于反馈线性化的船舶航向自适应模糊滑模控制与仿真

针对非线性船舶航向控制中船舶参数不确定性以及外界干扰随机性的特点,基于反馈线性化方法和模糊系统的逼近能力,提出了一种新的基于反馈线性化的自适应模糊滑模控制器的设计方法,并在李雅普诺夫稳定性理论的基础上导出了自适应律。以某船为例,并利用Matlab进行仿真研究,仿真结果表明所设计的控制器是有效的。     

Finite-time ship formation control based on extended state observer北大核心CSCD

[目的]为了解决大多数船舶编队控制算法控制周期长和时效性差的问题,提出一种基于扩张状态观测器(ESO)的有限时间控制策略。[方法]采用一种非线性终端滑模算法,通过对控制律分区域设计,克服传统终端滑模存在的奇异性问题;将非线性终端滑模与图论结合,实现有限时间的船舶编队控制;利用扩张状态观测器观测并补偿船舶模型中的不确定性及外界的干扰,以保证船舶编队控制的精确性;运用Lyapunov理论验证船舶编队控制律的稳定性。[结果]仿真结果表明,采用所提控制策略使整个编队系统误差在5 s左右趋近于0,从而实现了稳定。[结论]该控制策略能有效控制船舶编队,且控制速度快、时效性好。     

船舶航向模糊自整定操舵控制器的研究

船舶航向操舵控制是个典型的非线性系统,而工程上经常使用的常规PID(Proportional Integral Differential)控制器则为线性控制,至于模糊控制虽为非线性控制,但稳态精度不高。将常规PID控制与模糊控制相结合,基于Norrbin非线性系统模型和模糊自整定PID控制器的设计步骤,提出一种新的船舶航向控制算法,即船舶航向模糊自整定操舵控制器,并针对5 446标准箱的集装箱船舶,用Matlab进行了仿真计算。仿真结果表明,该控制算法可以使船舶航向控制从动态和稳态上都具有较好的精度,跟踪响应迅速,超调量小。     

基于输入状态线性化的船舶航迹系统鲁棒控制及仿真

本文将Hoo环增益成形鲁棒控制与输入状态精确反馈线性化结合,针对船舶航迹控制系统的非线性数学模型,采用Nomoto船舶模型,给出了一种鲁棒控制器的设计方法。这种算法是对船舶航迹非线性系统进行输入一状态线性化的基础上,然后与闭环增益成形算法相结合而设计出来的控制律。以某船为对象,利用Matlab／Simulink工具箱对新的控制器进行数值仿真,结果表明该控制规律有比较理想的控制效果,在加入扰动后,该控制器能使船舶行驶在预设航迹上,对外界风浪干扰有较强的鲁棒性。     

基于Bech模型的船舶航向离散变结构控制

采用带不确定项的Bech模型可以更好地反映船舶运动的非线性特性以及不确定因素对船舶运动的影响。在Bech模型中增加不确定项，采用离散变结构控制理论设计船舶航向控制器。针对传统设计方法稳态抖振的缺点，提出一种新型的离散趋近律，可以消除传统趋近律自身参数导致的抖振。设计扰动估计器，能够自适应估计不确定因素对系统的影响，只要不确定因素的变化率有界，即可保证闭环系统的鲁棒稳定性，特别是对于幔变不确定性，具有很高的估计精度。仿真结果表明，所设计的离散变结构控制器可以快速准确地跟踪设定航向，并对参数摄动和外部干扰具有很强的鲁棒性。     

基于减聚类-自适应神经模糊推理的船舶航向保持控制设计

为解决船舶在非线性和不确定性条件下的常规航向保持控制参数难以确定和性能较差的问题,提出一种基于减法聚类和神经模糊推理系统(SC-ANFIS)的船舶航向保持控制设计。基于鲁棒PID控制,借助减法聚类算法的学习能力对输入样本进行聚类分析,优化模糊量化和模糊规则,继而用神经-模糊推理的方法解决船舶的不确定性问题和非线性控制问题;同时,为避免维数灾难等问题发生,采用多维隶属度函数设计一种可在线自调整的基于SC-ANFIS的航向保持控制系统,并设计仿真试验进行对比分析。仿真试验结果表明,在存在模型参数摄动和干扰的情况下,基于SC-ANFIS的航向保持控制系统可行、有效,能取得良好的控制效果。     

基于遗传算法的模糊控制型船舶自动驾驶仪研究

该文分析了常规船舶自动驾驶仪存在的缺陷,针对船舶操作这种非线性、时变参数的控制对象,提出了一种基于遗传算法的模糊控制型和Bang—Bang控制相结合的控制系统,提高了模糊控制系统的精度;最后,通过仿真将模糊型船舶自动驾驶仪与基于遗传算法的模糊控制型船舶自动驾驶仪的性能作了比较,结果表明该文所提出的方法控制性能良好。     

基于RBF网络和遗传优化的船舶操纵模糊控制器

设计了一种基于RBF网络和遗传优化的船舶操纵模糊控制器。首先讨论了传统模糊控制器应用于船舶操纵控制的不足，然后根据模糊系统在特定情况下与RBF网络具有等价关系的特点，采用具有加权平均输出的RBF网络构造了一个船舶操纵模糊控制器，有效地消除了小偏差范围的舵角抖动现象。在此基础上，根据船舶操纵的特点提出了一种尺度变换因子的自整定方法，并采用遗传算法对自整定过程中的可变参数进行优化，以使控制器能够适应实时控制过程中的时变性和不确定性，保持良好的控制性能。最后针对某大型船舶的非线性模型，采用Matlab 6．1的Simulink工具进行了转艏操纵仿真试验，获得了满意结果。