Adaptive fuzzy controller design for dynamic positioning system of vessels

This paper develops an adaptive fuzzy controller for the dynamic positioning (DP) system of vessels with unknown dynamic model parameters and unknown time-varying environmental disturbances. The controller is designed by combining the adaptive fuzzy system with the vectorial backstepping method. An adaptive fuzzy system is employed to approximate the uncertain term induced by unknown dynamic model parameters and unknown time-varying environmental disturbances. It is theoretically proved that the proposed adaptive fuzzy DP controller can make the vessel be maintained at the desired values of its position and heading with arbitrary accuracy, while guaranteeing the uniform ultimate boundedness of all signals in the closed-loop DP control system of vessels. Simulation studies with comparisons on a supply vessel are carried out, and the results illustrate the effectiveness of the proposed control scheme.     

Design of a sliding mode fuzzy controller for the guidance and control of an autonomous underwater vehicle

This work demonstrates the feasibility of applying a sliding mode fuzzy controller to motion control and line of sight guidance of an autonomous underwater vehicle. The design method of the sliding mode fuzzy controller offers a systematical means of constructing a set of shrinking-span and dilating-span membership functions for the controller. Stability and robustness of the control system are guaranteed by properly selecting the shrinking and dilating factors of the fuzzy membership functions. Control parameters selected for a testbed vehicle, AUV-HM1, are evaluated through tank and field experiments. Experimental results indicate the effectiveness of the proposed controller in dealing with model uncertainties, non-linearities of the vehicle dynamics, and environmental disturbances caused by ocean currents and waves.     

A simplified approach to design fuzzy logic controller for an underwater vehicle

Fuzzy logic controller (FLC) performance is greatly dependent on its inference rules. In most cases, the more rules being applied to an FLC, the accuracy of the control action is enhanced. Nevertheless, a large set of rules requires more computation time. As a result, an FLC implementation requires fast and high performance processors. This paper describes a simplified control scheme to design a fuzzy logic controller (FLC) for an underwater vehicle namely, deep submergence rescue vehicle (DSRV). The proposed method, known as the single input fuzzy logic controller (SIFLC), reduces the conventional two-input FLC (CFLC) to a single input FLC. The SIFLC offers significant reduction in rule inferences and simplifies the tuning process of control parameters. The performance of the proposed controller is validated via simulation by using the marine systems simulator (MSS) on the Matlab/Simulink^® platform. During simulation, the DSRV is subjected to ocean wave disturbances. The results indicate that the SIFLC, Mamdani and Sugeno type CFLC give identical response to the same input sets. However, an SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.     

堤坝检测水下机器人模糊自适应控制方法研究

GDROV是用于堤坝探测的水下机器人,设计上属于开架式机器人,其精确的数学模型很难获得.采用基于模糊逻辑的直接自适应控制方法,利用模糊基函数网络逼近理想控制输出,通过模糊逻辑动态调整控制器的参数自适应律,可有效解决水下机器人控制问题.建立GDROV的水动力模型,给出基于模糊逻辑的直接自适应控制算法,最后通过仿真试验和外场试验验证了该控制器对模型的不确定性具有较强的鲁棒性,且具有良好的跟踪性能.     

Robust optimal output tracking control of a midwater trawl system based on T-S fuzzy nonlinear model

A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model.A simplified nonlinear mathematical model is first employed to represent a midwater trawl system,and then a T-S fuzzy model is adopted to approximate the nonlinear system.Since the strong nonlinearities and the external disturbance of the trawling system,a mixed H 2 /H ∞ fuzzy output tracking control strategy via T-S fuzzy system is proposed to regulate the trawl depth to follow a desired trajectory.The trawl depth can be regulated by adjusting the winch velocity automatically and the tracking error can be minimized according to the robust optimal criterion.In order to validate the proposed control method,a computer simulation is conducted.The simulation results indicate that the proposed fuzzy robust optimal controller make the trawl net rapidly follow the desired trajectory under the model uncertainties and the external disturbance caused by wave and current.     

Fuzzy logic for depth control of Unmanned Undersea Vehicles

Fuzzy logic is a viable control strategy for depth control of undersea vehicles. It has been applied to the low speed ballast control problem for ARPA's Unmanned Undersea Vehicle (UUV), designed and built by Draper Laboratory. A fuzzy logic controller has been designed and tested in simulation that issues pump commands to effect changes in the UUV depth, while also regulating the pitch angle of the vehicle. The fuzzy logic controller performs comparably to the current ballast control design. The controller is also less sensitive to variations in the vehicle configuration and dynamics. The benefits of the fuzzy logic approach for this problem are: 1) simplicity, by not requiring a dynamic model, thus allowing for rapid development of a working design and less sensitivity to plant variations; 2) better matching of the control strategy and complexity with performance objectives and limitations; 3) the insight provided and easy modification of the controller, through the use of linguistic rules     

大深度载人潜水器低速大漂角模糊滑模航向控制研究

通过模型试验测量大深度载人潜水器低速大漂角运动时所受到的非线性水动力。基于一种新的模糊滑模控制策略,为潜水器设计了鲁棒航向控制器。在不同的漂角子区间内分别设计局部镇定的滑模控制器,然后通过Takagi-Sugeno模糊推理系统将它们光滑连接,得到模糊滑模控制。仿真计算结果充分显示了该控制策略的有效性。     

基于单片机和模糊控制的浮标自动防碰撞系统

针对海洋观测浮标易受过往船只碰撞及恶劣天气的影响而损坏,提出了基于单片机和模糊控制的浮标自动防碰撞系统。该系统以C8051F340单片机作为核心控制芯片,设计了控制系统的软硬件,实现了信号的采集、处理、分析和传送。以距离、风速信号及其变化量作为输入变量,建立了相应的模糊控制规则和控制算法,设计了模糊控制器,为浮标长期、安全运行提供了保证,并为海洋测量仪器实现智能化控制奠定了基础。     

海洋平台磁流变阻尼器控制技术研究

为了更有效地减小海洋平台动力响应,采用基于模糊控制算法的磁流变阻尼器对海洋平台的振动进行控制.以海洋平台位移响应误差和误差变化为输入变量,以最优控制力为输出变量,优化设计出模糊控制器.考虑实际磁流变阻尼器输出控制力上限存在限制,采用半主动控制算法计算接近于最优控制力的半主动控制力.以一固定式海洋平台为算例研究磁流变阻尼器的振动控制效果及其模糊性,仿真结果表明模糊磁流变控制器对于平台的振动可以实现非常有效的控制,且控制效果对结构阻尼和环境的不确定性具有较好的模糊性.     

An assessment of fuzzy logic vessel path control

A fuzzy logic controller for ship path control in restricted waters is developed and evaluated. The controller uses inputs of heading, yaw rate, and lateral offset from the nominal track to produce a commanded rudder angle. Input variable fuzzification, fuzzy associative memory rules, and output set defuzzification are described. Two maneuvering situations are evaluated: track keeping along a specified path where linearized regulator control is valid; and larger maneuvers onto a specified path where nonlinear modeling and control are required. For the track keeping assessment, the controller is benchmarked against a conventional linear quadratic Gaussian (LQG) optimal controller and Kalman filter control system. The Kalman filter is used to produce the input state variable estimates for the fuzzy controller as well. An initial startup transient and regulator control performance with an external hydrodynamic disturbance are evaluated using linear model simulations of a crude oil tanker. A fully nonlinear maneuvering model for a smaller product tanker is used to assess the larger maneuvers     

青岛近海表皮温度和表层温度之差的观测及模糊数学分析

１９９３．１１．２３．９：００－１１．２８．９：００随“东方红”号海洋考察船,采用“走航式海面遥感参数自动观测系统”在青岛近海进行了５个昼夜的连续观测。根据所得资料,首先对表皮水曙和表层水温之差与各因子之间的关系进行了分析,然后利用数学方法对所获数据进行了计算。结果表明,海洋表皮水温与表层水温之差和海面风速,气温与水温之差及太阳辐射等均有明显关系。但此关系难以用传统数学方法准确表述。应用模糊推理方     

An application of fuzzy fault tree analysis for spread mooring systems

In this paper, a fuzzy fault tree analysis methodology for spread mooring systems is presented. The methodology combines the effects of operational failures and human errors under fuzzy environment for the spread mooring configurations. In conventional fault tree analysis (FTA), which is an established technique in hazard identification, the ambiguous and imprecise events such as human errors cannot be handled efficiently. In addition to this, the tolerances of the probability values of hazards are not taken into account. Moreover, it is difficult to have an exact estimation of the failure rates of the system components or the probability of the occurrence of undesired events due to the lack of sufficient data. To overcome these disadvantages, a fault tree analysis based on the fuzzy set theory is proposed and applied to the spread mooring system alternatives. Furthermore, sensitivity analysis is carried out based on the fuzzy weighted index (FWI) in order to measure the impact of each basic event on the top event. The results show that the fuzzy fault tree risk analysis method (FFTA) is more flexible and adaptive than conventional fault tree analysis for fault diagnosis and hazard estimation of spread mooring systems.     

Significant wave height forecasting using wavelet fuzzy logic approach

Wave heights and periods are the significant inputs for coastal and ocean engineering applications. These applications may require to obtain information about the sea conditions in advance. This study aims to propose a forecasting scheme that enables to make forecasts up to 48 h lead time. The combination of wavelet and fuzzy logic approaches was employed as a forecasting methodology. Wavelet technique was used to separate time series into its spectral bands. Subsequently, these spectral bands were estimated individually by fuzzy logic approach. This combination of techniques is called wavelet fuzzy logic (WFL) approach. In addition to WFL method, fuzzy logic (FL), artificial neural networks (ANN), and autoregressive moving average (ARMA) methods were employed to the same data set for comparison purposes. It is seen that WFL outperforms those methods in all cases. The superiority of the WFL in model performances becomes very clear especially in higher lead times such as 48 h. Significant wave height and average wave period series obtained from buoys located off west coast of US were used to train and test the proposed models.     

基于模糊神经网络理论对水下拖曳体进行深度轨迹控制

以华南理工大学开发的自主稳定可控制水下拖曳体为研究对象,首先通过水下拖曳体在拖曳水池样机中的试验取得试验数据后作为训练样本,采用LM BP算法,建立基于神经网络理论构建的可控制水下拖曳体轨迹与姿态水动力的数值模型。在此基础上设计了一个控制系统,它主要由两部分组成:基于遗传算法的神经网络辨识器和基于模拟退火改进的遗传算法的模糊神经网络控制器。以满足预先设定的拖曳体水下监测轨迹要求为控制依据,由控制系统确定为达到所要求的运动轨迹而应采用的迫沉水翼转角,以此作为输入参数,通过LM BP神经网络模型的模拟计算预报在这一操纵动作控制下的拖曳体所表现的轨迹与姿态特征。数值模拟计算结果表明:该系统的设计达到了所要求的目的;借助这一系统,可以有效地实现对拖曳体的深度轨迹控制。     

Prediction of wave parameters by using fuzzy logic approach

The purpose of this study is to investigate the relationship between wind speed, previous and current wave characteristics. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy IF–THEN rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. In this paper, a sophisticated intelligent model, based on Takagi–Sugeno (TS) fuzzy modeling principles, was developed to predict the changes in wave characteristics such as significant wave height and zero up-crossing period due to the wind speed. Past measurements of significant wave height values and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals such as 1, 3, 6 and 12 h. The verification of the proposed model is achieved through the wave characteristics time series plots and various numerical error criterias. Also the model results were compared with classical Auto Regressive Moving Average with exogenous input (ARMAX) models. For the application of the proposed approach the offshore station located in the Pacific Ocean was used.     

Application of a fuzzy inference system for the prediction of longshore sediment transport

A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the FIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (K_sig = 0.39), the calibrated CERC formula (K_sig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model.     

A Novel Control Algorithm for Interaction Between Surface Waves and A Permeable Floating Structure

An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment. In the design procedure of the controller, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis is carried out for a real structure system by using Lyapunov method. The corresponding boundary value problems are then incorporated into scattering and radiation problems. They are analytically solved, based on separation of variables, to obtain series solutions in terms of the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width, thickness and mass has been thus drawn with a parametric approach. From which mathematical models are applied for the wave-induced displacement of the surge motion. A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system, but has the robustness against external disturbance.     

Ant colony fuzzy neural network controller for cruising vessel on river

This paper proposes an ant colony fuzzy neural network (ACFNN) controller for a cruising vessel on a river. The proposed controller comprises an ant colony (AC) algorithm, a fuzzy neural network (FNN) controller, and a switching law. The approximately optimal sailing line and short sailing time are obtained using the AC algorithm. First, the searching pattern of the AC algorithm is constructed using the data of the tidal current, river current, vessel velocity, and position of the coordinate. From a tracking error viewpoint, the switching law determines that the approximately optimal sailing line and the shorter sailing time are obtained using the AC algorithm, and that uncertain nonlinear factors are compensated by the FNN controller. The controller consists of an FNN identifier and a robust controller. The identifier is used to estimate the vessel velocity, and its parameters are tuned online by the adaptive law derived from the Lyapunov function. The robust controller is used to compensate for uncertainties of the tidal current and river current through the estimated law. The output of the ACFNN controller is the sum of the FNN identifier, the robust controller, and an auxiliary function. Finally, a simulation and a practical cruising vessel on a river are performed to verify the effectiveness of the presented controller.     

Estimation of pile group scour using adaptive neuro-fuzzy approach

An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour depth as well as scour width for a group of piles supporting a pier. The ANFIS model provides the system identification and interpretability of the fuzzy models and the learning capability of neural networks in a single system. Two combinations of input data were used in the analyses to predict scour depth: the first input combination involves dimensional parameters such as wave height, wave period, and water depth, while the second combination contains nondimensional numbers including the Reynolds number, the Keulegan–Carpenter number, the Shields parameter and the sediment number. The test results show that ANFIS performs better than the existing empirical formulae. The ANFIS predicts scour depth better when it is trained with the original (dimensional) rather than the nondimensional data. The depth of scour was predicted more accurately than its width. A sensitivity analysis showed that scour depth is governed mainly by the Keulegan–Carpenter number, and wave height has a greater influence on scour depth than the other independent parameters.     

Maneuvering control and trajectory tracking of very large crude carrier

This paper presents a complex control system of the ship motions in confined waters. The general structure of this system is based on the two different controllers connected in parallel. They are dedicated to the different tasks and operate in different conditions. One of them is based on the robust control technology while another is based on the fuzzy logic technique. To decide which controller to use depends on the velocity of the vessel. The control system was implemented at the first stage on a nonlinear multi-variable simulation model and at the second stage on a real-time object—floating, autonomous model of the very large crude carrier (VLCC tanker). The whole system was developed in the MATLAB/Simulink platform.