共查询到20条相似文献，搜索用时 15 毫秒
1.
A neural-network-based learning control scheme for the motion control of autonomous underwater vehicles (AUV) is described. The scheme has a number of advantages over the classical control schemes and conventional adaptive control techniques. The dynamics of the controlled vehicle need not be fully known. The controller with the aid of a gain layer learns the dynamics and adapts fast to give the correct control action. The dynamic response and tracking performance could be accurately controlled by adjusting the network learning rate. A modified direct control scheme using multilayered neural network architecture is used in the studies with backpropagation as the learning algorithm. Results of simulation studies using nonlinear AUV dynamics are described in detail. The robustness of the control system to sudden and slow varying disturbances in the dynamics is studied and the results are presented 相似文献
2.
This paper presents a neural network (NN) controller for a fishing vessel rudder roll system. The aim of this study is to build a NN controller which uses rudder to regulate both the yaw and roll motion. The neural controller design is accomplished with using the classical back-propagation algorithm (CBA). Effectiveness of the proposed NN control scheme is compared with linear quadratic regulator (LQR) results by simulations carried out a fishing vessel rudder roll stabilizer system. 相似文献
3.
依据人工操舵的基本原理 ,提出了一种能适应船舶航行环境变化和自动获取船舶操纵运动特性知识的神经网络控制器。仿真结果表明 ,此神经网络控制器不仅能对系统进行有效的控制 ,而且可达到无超调、无波动的控制效果。此外 ,其操舵规律与人工操作时的最佳操舵规律一致 相似文献
4.
A neural network based control system “Self-Organizing Neural-Net-Controller System: SONCS” has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs). In this paper, an on-line adaptation method “Imaginary Training” is proposed to improve the time-consuming adaptation process of the original SONCS. The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object. The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out. In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data. A neural network “Identification Network” which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network. The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV “Twin-Burger”. It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation 相似文献
5.
A new approach to automatic solar disk state detection by all-sky images using machine learning methods is developed and implemented. The efficiency of the most widely used machine learning algorithms is analyzed. The effect of reducing the dimensionality of the feature space on the classification accuracy is estimated. The multilayer artificial neural network model has shown the best accuracy in terms of the true score. The operation result demonstrates the effectiveness of machine learning methods applied to solar disk state detection by all-sky images. 相似文献
6.
In this paper, we present a mathematical model including seakeeping and maneuvering characteristics to analyze the roll reduction for a ship traveling with the stabilizer fin in random waves. The self-tuning PID controller based on the neural network theory is applied to adjust optimal stabilizer fin angles to reduce the ship roll motion in waves. Two multilayer neural networks, including the system identification neural network (NN1) and the parameter self-tuning neural network (NN2), are adopted in the study. The present control technique can save the time for searching the optimal PID gains in any sea states. The simulation results show that the present developed self-tuning PID control scheme based on the neural network theory is indeed quite practical and sufficient for the ship roll reduction in the realistic sea. 相似文献
7.
基于模糊神经网络理论对水下拖曳体进行深度轨迹控制 总被引：2，自引：0，他引：2
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。 相似文献
8.
9.
提出一种基于序贯预测误差方法（ＳＰＥ）的多层神经网络（ＭＮＮ）的学习算法。经模拟计算，它比传统的基于最陡下降方法的误差反传（ＳＤＢＥＰ）算法具有更好的收敛性能。并对这两种算法进行了模拟计算的比较． 相似文献
10.
An application of neural networks for the identification and correction of transmission errors in binary messages is described. The network is used as a classifier of detected hydroacoustic signals. It converts the signals into one of a possible alphabet of symbols. The algorithm used is a Hamming-type neural network classifier associated with the transmission of a Hamming code. This system can detect and correct all transmission errors if the number of errors is less than or equal to half the Hamming distance between transmitted symbols minus one. Symbols to be transmitted are chosen and associated to messages, assuring that bit-to-bit nonsimilarities result on the prescribed Hamming distance. The auto-associative error correcting scheme can be used to generate a teaching signal to a supervised learning equalizer tracking the channel nonstationary characteristics. The proposed system is intended for use in hydroacoustic communication applications and is undergoing sea tests 相似文献
11.
This paper proposes a saturated tracking controller for underactuated autonomous marine surface vehicles with limited torque. First, a second-order open-loop error dynamic model is developed in the actuated degrees of freedom to simplify the design procedure. Then, a saturated tracking controller is designed by utilizing generalized saturation functions to reduce the risk of actuator saturation. This, in turn, improves the transient performance of the control system. A multi-layer neural network and adaptive robust control techniques are also employed to preserve the controller robustness against unmodeled dynamics and environmental disturbances induced by waves and ocean currents. A Lyapunov stability analysis shows that all signals of the closed-loop system are bounded and tracking errors are semi-globally uniformly ultimately bounded. Finally, simulation results are provided for a hovercraft vehicle to illustrate the effectiveness of the proposed controller as a qualified candidate for real implementations in offshore applications. 相似文献
12.
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. 相似文献
13.
14.
This paper presents an on-line trained neural net work controller for ship track-keeping problems. Following a brief review of the ship track-keeping control development since the 1980's, an analysis of various existing backpropagation-based neural controllers is provided. We then propose a single-input multioutput (SIMO) neural control strategy for situations where the exact mathematical dynamics of the ship are not available. The aim of this study is to build an autonomous neural controller which uses rudder to regulate both the tracking error and heading error. During the whole control process, the proposed SIMO neural controller adapts itself on-line from a direct evaluation of the control accuracy, and hence the need for a “teacher” or an off-line training process can be removed. With a relatively modest amount of quantitative knowledge of the ship behavior, the design philosophy enables real time control of a nonlinear ship model under random wind disturbances and measurement noise. Three different track-keeping tasks have been simulated to demonstrate the effectiveness of the training method and the robust performance of the proposed neural control strategy 相似文献
15.
冯天瑾 《中国海洋大学学报(自然科学版)》1994,(Z2)
介绍用神经网络实现图象边缘检测的实验研究结果。一幅数字图象用一个规模不大的ＢＰ网络边缘检测器处理小邻区，再用扫描此图象的方法进行边缘检测。此方法之最大优点是设计简单，网络边缘检测器的性能也令人满意．欠缺是：学习时间长，神经网络隐层中权重包含的信息难于解释清楚。 相似文献
16.
This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation.The formation is achieved by the follower to track a virtual target defined relative to the leader.A robust adaptive target tracking law is proposed by using neural network and backstepping techniques.The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces,nonlinear damping,unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning.Based on Lyapunov analysis,the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin.Simulation results demonstrate the effectiveness of the control strategy. 相似文献
17.
1 .IntroductionWiththedevelopmentofoceantechnology ,moreandmoreextremelylargeandlongflexibleoff shoreplatformsusedforoilexplorationanddrillingoperationarebuiltinhostileoceanenvironments .Ingeneral,thiskindofplatformsisanonlineardistributedparametersystemanditsnaturalfrequencyfallsclosertothedominantwavefrequencieswiththeincreaseofwaterdepth .Besides ,itsstructureisverycomplexandtheexternalwaveforceontheplatformisuncertain .Thus ,theseplatformsarepronetoexcessivewave inducedoscillationsunderbot… 相似文献
18.
An increasing number of experiments are being conducted to study the design and performance of wave energy converters. Often in these tests, a real-time realization of prospective control algorithms is applied in order to assess and optimize energy absorption as well as other factors. This paper details the design and execution of an experiment for evaluating the capability of a model-scale WEC to execute basic control algorithms. Model-scale hardware, system, and experimental design are considered, with a focus on providing an experimental setup capable of meeting the dynamic requirements of a control system. To more efficiently execute such tests, a dry bench testing method is proposed and utilized to allow for controller tuning and to give an initial assessment of controller performance; this is followed by wave tank testing. The trends from the dry bench test and wave tank test results show good agreement with theory and confirm the ability of a relatively simple feedback controller to substantially improve energy absorption. Additionally, the dry bench testing approach is shown to be an effective and efficient means of designing and testing both controllers and actuator systems for wave energy converters. 相似文献
19.
Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献
20.
针对误差逆向传播BP (back propagation)神经网络在GNSS水准拟合中存在梯度消失、陷于局部最小点的问题,通过使用深度学习中的分段线性整流函数Relu(rectified linear units)作为神经元激活函数,自适应矩估计Adam (adaptive moment estimation)算法作为网络优化函数,提出了一种基于深度学习的BP神经网络模型。研究结果表明:改进后的BP神经网络内外符合精度分别提高近50%和25%,可达0.9 cm和2.4 cm,为GNSS水准拟合提供了新的思路。 相似文献