弧焊过程神经网络模糊控制

提出一种将FLC与神经网络技术相结合的方法对钨极氩弧焊（GTAW）过程进行控制,它克服了模糊规则产生对专家的依赖及模糊集非自适应性的问题。隶属函数的自适应及模糊规则的自组织通过神经网络的自学习和竞争获得。该方法实现了弧焊过程中模糊规则的自动确定和隶属度函数在线调度。 以GTAW过程焊缝几何参数调节为对象,验证了算法的有效性。计算机仿真表明,采用该方法的系统性能有较大的提高。     

一种准小孔弧焊接模型的自适应控制

本文针对一种非线性的准小孔弧焊接模型,介绍了一种自适应控制算法。给出了这种算法的具体推导过程。仿真结果验证了算法的有效性。     

Neural network methods for the modeling and control of welding processes

While welding processes are of great importance in manufacturing, their modeling and control is still subject of research. The highly nonlinear, strongly coupled, and multivariable nature of these processes renders the use of analytical tools practically impossible. In this article a novel approach is presented which employs networks of simple nonlinear units: a neural network. A widely used welding process, the Gas Tungsten Arc Welding is presented and the problem of its modeling and control is exhibited. A very brief introduction to neural networks is followed by presenting the experimental results for modeling the static and dynamic behavior of the process, as well as some practical recommendations regarding the use of the neural network techniques for controlling these processes.     

脉冲GTAW熔池动态过程模糊神经网络建模与控制

展示了模糊推理与神经网络结合在脉冲GTAW熔池动态过程智能控制中的应用研究 结果.建立了脉冲GTAW平板对接动态过程特征:正反面熔池的最大宽度、长度与面积等参数 的神经网络模型,基于实验数据采用模糊辨识方法提取焊接过程的模糊控制规则,进而设计了 具有自学习适应能力的模糊神经网络控制器.建立了脉冲GTAW熔池动态过程智能控制系统, 焊接实验验证了所设计的模糊神经网络控制器具有智能控制效果.     

Self-tuning and stable adaptive control of a batch polymerization reactor

Two adaptive control techniques are evaluated by application to a realistic mathematical model of a suspension polyvinyl chloride (PVC) reactor. Both techniques, the self-tuning regulator and a globally stable adaptive control algorithm, prove to be very robust and give excellent control of the temperature or the rate of conversion in the PVC reactor by manipulating the heat removal rate from the reactor jacket. Satisfactory regulation and setpoint changes in the temperature and the conversion rate are obtained in each case even in the presence of measurement noise and highly nonlinear reactor dynamics. The performance of the two adaptive techniques is compared with the performance of a classical, PID controller. The adaptive controllers are shown to always outperform the PID controller.     

Engineering applications of neural networks

This paper describes several prototypical applications of neural network technology to engineering problems. The applications were developed by the authors as part of a graduate-level course taught at the University of Illinois at Urbana-Champaign by the first author (now at Carnegie Mellon University). The applications are: an adaptive controller for building thermal mass storage; an adaptive controller for a combine harvester; an interpretation system for non-destructive evaluation of masonry walls; a machining feature recognition system for use in process planning; an image classification system for classifying land coverage from satellite or high-altitude images; and a system for designing the pumping strategy for contaminated groundwater remediation. These applications are representative of many of the engineering problems for which neural networks are applicable: adaptive control, feature recognition, and design.     

基于神经网络的模糊自适应PID控制方法

提出一种基于BP神经网络的模糊自适应PID控制器。该控制器综合模糊控制、神经网络与PID调节各自的优点，既具有模糊控制的简单和有效的非线性控制作用，又具有神经网络的学习和适应能力，同时具备PID控制的广泛适应性，仿真实验表明该控制器对模型、环境具有较好的适应能力和较强的鲁棒性。     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

一种基于模糊逻辑神经网络的自适应控制及其应用

本文提出了一种模糊逻辑神经网络自适应控制器．这种控制器由一个模糊高斯神经网络和一个多层神经网络组成．它具有自适应和学习能力．计算机仿真和实际的伺服直流电机调速实验的结果表明本文提出的这种控制器是切实可行的，其系统响应和鲁棒性优于常规的Ｆｕｚｚｙ控制．     

Intelligent welding system technologies: State-of-the-art review and perspectives

Welding systems are being transformed by the advent of modern information technologies such as the internet of things, big data, artificial intelligence, cloud computing, and intelligent manufacturing. Intelligent welding systems (IWS), making use of these technologies, are drawing attention from academic and industrial communities. Intelligent welding is the use of computers to mimic, strengthen, and/or replace human operators in sensing, learning, decision-making, monitoring and control, etc. This is accomplished by integrating the advantages of humans and physical systems into intelligent cyber systems. While intelligent welding has found pilot applications in industry, a systematic analysis of its components, applications, and future directions will help provide a unified definition of intelligent welding systems. This paper examines fundamental components and techniques necessary to make welding systems intelligent, including sensing and signal processing, feature extraction and selection, modeling, decision-making, and learning. Emerging technologies and their application potential to IWS will also be surveyed, including Industry 4.0, cyber-physical system (CPS), digital twins, etc. Typical applications in IWS will be surveyed, including weld design, task sequencing, robot path planning, robot programming, process monitoring and diagnosis, prediction, process control, quality inspection and assessment, human-robot collaboration, and virtual welding. Finally, conclusions and suggestions for future development will be proposed. This review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they modernize their traditional welding stations, systems, and factories.     

基于包络特征的IDC网络自适应流量调控方法

传统流量调控方法不能自动调整约束规则,导致面对不同流量密度时,容易出现较大的带宽损失率,因此基于包络特征,研究一种全新的IDC网络自适应流量调控方法.该方法构建IDC网络流量能耗模型,提取包络特征并获取流量空间分布状态,根据网络内部信息流向设置调控自适应约束规则,应用BP神经网络改进IDC网络自适应流量调控的实现.实验...     

晶体管式弧焊逆变器参数控制的研究

在对晶体管弧焊逆变器的基本结构、工作原理、特点及应用进行简单介绍的基础上,通过对各种逆变主电路形式的分析,提出了其应用范围.同时,讨论了驱动电路的两种方式.为了实现弧焊逆变器的外特性、调节性能、动特性及输出波形的控制与调节,重点推出了晶体管式弧焊逆变器采用“定频调脉宽”的调制方式,该调制方式的控制电路具有工作频率高;可以无级调节焊接工艺参数,不必分档调节,操作方便;控制性能好等特点.     

基于神经网络的弹性连杆机构振动主动控制

研究基于神经网络的弹性连杆机构振动主动控制方法.介绍了双隐层动态递归神经 网络的数学模型,利用实验数据离线设计了神经网络辨识器与神经网络控制器.采用基于神 经网络的间接自适应控制策略对弹性连杆机构实施了振动主动控制,机构的动力学品质得到 显著改善.实验结果证明了该方法的有效性.     

采用动态神经网络的多模型自适应重构控制方法

针对复杂的系统,提出一种基于多模型结构的自适应重构控制方法,使得系统可以在不同的运行环境下跟踪给定的信号,并且对特定的故障情况具有控制重构的能力;首先,由多个线性模型和一个模糊模型构成多模型控制结构,并设计多模型自适应控制器的权值调整规则,以获得当前最佳的控制输入,再引入动态自适应神经网络以保证系统的稳定性,并避免模型切换等噪声干扰;最后,对某型歼击机进行正常和故障状态下的控制仿真,结果表明所提重构控制方法是可行有效的。     

如何通过动态电阻曲线的自适应控制来优化焊接飞溅率

本文通过在AudiB8前底板车身焊接应用实例,系统地阐述了博世力士乐的具有动态电阻自适应控制的中频电阻产品:PSI6100.352L1,是如何实现焊接飞溅率的优化过程的。     

Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.     

Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.     

Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature

Laser welding has been widely utilized in various industries. Effective real-time monitoring technologies are critical for improving welding efficiency and guaranteeing the quality of joint-products. In this paper, the research findings and progress in recent ten years for real-time monitoring of laser welding are critically reviewed. Firstly, different sensing techniques applied for welding quality monitoring are reviewed and discussed in detail. Then, the advanced technologies based on artificial intelligence are summarized which are exploited to realize varied objectives of monitoring such as process parameter optimization, weld seam tracking, weld defects classification, and process feedback control. Finally, the potential research problems and challenges based on real-time intelligent monitoring are discussed, such as intelligent multi-sensor signal acquisition platform, data depth fusion method and adaptive control technology. This fundamental work aims to review the research progress in laser welding monitoring and provide a basis for follow-on research.     

基于模糊神经网络的模型参考自适应控制

用模糊神经网络作为控制器,依靠参考模型产生理想的控制系统闭环响应,从而随时得 到控制系统的输出误差.用梯度法实时修正模糊控制器的输入和输出隶属度参数,得到一种 在线模糊自适应控制的新方法.通过倒立摆的仿真实验表明,该方法是可行的并能适应对象 特性的大范围变化.