Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm

This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC's online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption.     

Modeling of abrasive flow rotary machining process by artificial neural network

Abrasive flow machining (AFM) is one of the non-traditional machining processes applicable to finishing, deburring, rounding of edges, and removing defective layers from workpiece surface. Abrasive material, used as a mixture of a polymer with abrasive material powder, has reciprocal motion on workpiece surface under pressure during the process. In the following study, a new method of AFM process called henceforth abrasive flow rotary machining (AFRM) will be proposed, in which by elimination of reciprocal motion of abrasive material and the mere use of its stirring and rotation of workpiece, the amount of used material would be optimized. Furthermore, AFRM is executable by simpler tools and machines. In order to investigate performance of the method, experimental tests were designed by the Taguchi method. Then, the tests were carried out and the influence of candidate effective parameters was determined and modeled by artificial neural network (ANN) method. To evaluate the ANN results, they were compared with reported results of AFM. An agreement between our ANN results on predictions of AFRM material removal value and surface roughness was observed with AFM data. The results showed through AFRM, in addition to saving of abrasive material, surface finish is achievable same as AFM’s.     

人工神经网络在机械加工中的应用

介绍神经网络技术在机械加工领域的应用现状,包括人工神经网络在工艺规程编制中的应用、在加工参数优化中的应用及在工况监测及预报中的应用。并对这项技术的应用作了进一步展望。     

神经网络在仿昆微型飞行机器人控制中的应用

分析了拍翅式仿昆微型飞行机器人控制系统的特点,探讨了人工神经网络在飞行机器人控制系统中的应用,提出了一种具有神经辨识器NNI和神经PID控制器NNC的飞行机器人控制系统。     

基于PSO神经网络在动态联盟制造伙伴选择中应用

制造伙伴选择是组建动态联盟的一个关键问题.在对已有制造伙伴选择方法总结和分析的基础上,给出了制造伙伴选择综合评价参考体系,提出了一种基于神经网络的制造伙伴选择模型,采用基于连接结构优化的粒子群优化算法(SPSO)对神经网络进行训练,实验证明,该算法不仅使训练的收敛速度大大提高,且其训练的神经网络的性能也显著增强,为制造伙伴选择问题提供了有效的解决途径.     

人工神经网络在设备故障诊断中的应用

介绍了神经网络技术在设备故障诊断中应用的２个主要方向———故障模式识别和诊断专家系统,对应用的方法、特点及存在的问题也作了概略分析。     

基于神经网络的复杂结构映射建模和优化方法

运用结构力学方法建立易于解析的大型结构平面模型；采用商业有限元软件建立能精确反映结构真实受力的空间模型。求解得到2种模型各单元内力，将平面模型单元内力作为输入，空间模型的内力作为输出而构造样本集。利用神经网络的非线性映射能力，通过样本训练提取其特征和本质联系，以获得具有内插和泛化能力的映射模型，为后续优化设计奠定了基础。     

供暖热网神经网络预测模型的研究

将人工神经网络应用于供热网实时预报,建立起可用于热网供暖预报的外时延反馈型BP网络模型,及内时延反馈型Elman网络。且利用实际热网数据对所建立的网络进行训练和检验,结果表明两种预报模型均具有较好的动态跟踪能力和预报特性。而Elman网络在节点结构上比外时延反馈型BP网络更简单,在确定网络节点结构上更快捷,更具有实际推广和应用价值。     

基于人工神经网络的多学科优化设计研究

多学科优化设计的两大难点是子学科间的信息交换和系统分析计算的复杂性。为此，在一致性约束算法和并行子空间算法基础上，提出了一种基于人工神经网络响应面的多学科优化设计算法，它是一种二级结构的优化方法，即学科层仅满足局部约束，系统层提供一种协调学科间冲突的机制，保证在相关变量和耦合变量上的一致性，使设计方案不断改进。通过某型号飞航导弹系统的优化实例，验证了算法的有效性。     

基于人工神经网络的起重机安全评价

依据起重机的特点,建立了安全性评价指标体系,并将神经网络理论应用于起重机安全评价之中,提出了基于此理论的系统安全评价模型和优点。根据实际收集的安全状况数据构建安全评价网络,并利用MATLAB软件对网络进行训练,得出可以对起重机安全状况进行评价的人工神经网络模型。评价实例验证了此方法的可行性,为起重机安全评价提供了一种新的途径。     

Intelligent condition monitoring of a gearbox using artificial neural network

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. In this research, vibration signals were recognised as the most reliable source to extract the feature vector which were synchronised by piecewise cubic hermite interpolation (PCHI) and pre-processed using the standard deviation of wavelet packet coefficients.     

基于人工神经网络的铣削参数优化

探讨了金属切削加工的优化问题.并以铣削为例,建立最高生产率为目标的数学模型,通过人工神经网络的方法进行优化.通过实例表明,用人工神经网络优化方法可降低加工成本和提高劳动生产率.     

人工神经网络在机器人学中的应用探讨

简要介绍常用于机器人学中的基本网络模型,并着重探讨了人工神经网络在机器人运动学和运动控制中的应用。     

粒子群优化人工神经网络在高速铣削力建模中的应用

将粒子群优化人工神经网络理论应用于高速铣削力的建模研究中.采用粒子群算法与反向传播算法相结合的方法,对反向传播神经网络模型进行优化.用粒子群算法训练网络参数,直到误差趋于一稳定值,然后用优化的权值进行反向传播算法运算,以实现高速铣削力的预测.充分发挥了粒子群算法的全局寻优能力和反向传播算法的局部搜索优势.仿真结果表明,与其他几种反向传播算法相比较,粒子群算法与反向传播算法的学习算法训练的神经网络,不仅训练时间明显缩短,而且其预报精度也得到了较大的提高,能够有效地建立铣削力模型,并对铣削力进行准确的预测.     

基于人工神经网络的机床刚度建模

将人工神经网络应用于机床刚度建模,针对基本BP算法收敛速度慢、易陷入局部极小点的不足,从步长和搜索方向两方面对基本BP算法进行了改进,并引入具有全局寻优能力的粒子群算法.通过统计误差计算的次数、设定多组初始权值及方差分析等方法,对几种优化算法在机床刚度建模中的应用效果进行了比较,最后以输出误差最小时的连接权值建立了机床刚度神经网络模型.     

Force control of hybrid actuator using learning vector quantization neural network

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.     

Recognizing imperfections with an artificial neural network of a special type

Recognition of imperfections with the use of signals from nondestructive testing devices is considered. A new type of neural network that allows separation of signals from imperfections of different types is proposed. Concepts of the neural network’s operation are considered. An example of recognition of signals from an ultrasonic flaw detector is given.     

一种基于神经网络的 THD 参数快速估计算法

模数转换器(ADC)测试主要包括静态参数和动态参数两个测试过程。随着性能的提升,ADC的测试复杂度和成本也急剧增加。替代测试,即通过分析两类参数间的关系来实现一个测试过程得到两类参数,已被证明是降低ADC测试复杂度和成本的主要方案之一。本文通过构建基于人工神经网络的参数预测模型来实现替代测试,模型以总谐波失真为预测目标,以静态性能参数为输入特征。针对高维的ADC非线性曲线,文章结合统计分析和主成分分析设计了专用的特征提取方法,在降低特征维度的同时尽可能地减少了信息损失。模型在测试集上的预测结果与参考值的均方误差和拟合优度分别达到了1.15 dB和0.6,显著优于相关对比模型。此外,在SHAP解释器的框架下分析了上述模型的预测目标和特征变量之间的依赖关系,并得到了有意义的结果。     

滚动轴承故障诊断的神经网络方法

总结分析了轴承的故障形式及原因,给出了振动频率,阐述了Bp网络的结构及算法,并对实例建立BP神经网络。