脉冲GTAW焊缝成形智能控制方法

在作者前期工作的基础上,进一步展示了模糊神经网络与专家系统等智能化方法在电弧焊动态过程与焊缝成形控制中的应用结果.在模糊推理与神经网络结合的单人单出焊接电流控制器FNNC设计的基础上,设计了专家系统控制器用于调节焊接速度,完成了焊接熔池动态过程双人双出智能控制器的设计,实现了脉冲GTAW正反面熔宽与焊缝成形的同时智能控制.与前期工作一并构成了对电弧焊接熔池动态过程和焊接质量智能控制途径的成功探索.     

不确定对象的人工神经网络自学习控制方法

利用模糊控制的思想,将误差、误差变化及加速度作为输入,建立一个神经网络控制器,对具有不确定性控制对象提出了一种自学习模糊神经网络控制方法.并将此方法用于焊接熔池动态过程控制的有效性试验.     

具有时滞的不确定性系统神经网络模糊自学习控制

本文对具有时滞的不确定性控制对象提出了一种神经网络时滞补偿模糊自学习控制方法．模糊控制器采用误差、误差变化及误差加速度的加权和的解析描述形式，利用人工神经网络直接对过程建模，实现对时滞补偿预报以及对模糊加权因子的自学习优化调整．将上述方法用于焊接熔池动态过程控制试验，结果表明本文提出的自学习神经网络时滞补偿模糊控制方案有效．     

基于模糊神经网络的温度控制系统设计

实验室温度控制系统要求精度高,并且具有非线性、大惯性及数学模型难建立等特性,这使得用常规PID控制器以及一般模糊控制器无法很好地满足系统要求,而本文在一般模糊控制器的基础上,融合神经网络技术,设计出模糊神经网络控制器,它既有模糊控制鲁棒性好、动态响应好、上升时间快、超调小的优点,又具有神经网络的在线自学习能力,可以实现温度的智能控制,在实际应用中取得良好的效果。     

具有时滞的不确定性系统神经网络模型自学习控制

本文对具有时滞的不确定性控制对象提出了一种神经网络时滞补偿模糊自学习控制方法，模糊控制器采用误差、误差变化及误差加速度的加权和的解析描述形式，利用人工神经网络直接对过程建模，实现对时滞补偿预报以及对模糊加权因子的自学习优化调整，将上述方法用于焊接熔池动态过程控制试验，结果表明本文提出的自学习神经网络时滞补偿模糊控制方案有效。     

电火花加工智能控制系统的建模及仿真

为增强电火花加工过程的稳定性,基于模糊RBF神经网络智能控制,同时利用模糊控制的解耦性,以在电火花加工过程中统计的空载率、正常放电率、不正常放电率(包括电弧及短路放电)、相对于脉冲周期和他抬刀周期的正常放电率为输入,设计了以伺服参考电压、脉冲间隔和抬刀周期为输出的电火花加工模糊RBF神经控制器,并利用MATLAB对设计的模糊RBF神经控制器进行了建模及仿真.仿真结果表明了模糊RBF神经网络控制系统在电火花加工中对于稳定加工状态的有效性.     

基于DSP的防空火箭炮模糊神经网络位置控制器设计

针对多管火箭炮发射时恶劣的负载特性,设计了一种模糊神经网络自适应位置控制器.用梯度下降法实时修正模糊控制器的输入输出隶属度参数,以使模糊神经网络能根据火箭炮跟踪发射过程中的负载特性实时调整速度给定值,从而减小系统参数变化和外部干扰对火箭炮性能的影响.采用对空间分区建立索引表的方法,建立了一种基于TMS320F2812的新型模糊神经网络位置控制器的编程实现方法.仿真及实验结果表明该方法可有效提高火箭炮位置伺服系统的动态响应性能、稳定性和鲁棒性.     

基于遗传算法优化模糊神经网络的倒立摆智能控制

提出了一种利用遗传算法来优化模糊神经网络的倒立摆智能控制,利用RBF神经网络与模糊推理过程具有函数等价性.设计了基于模糊系统的RBF网络结构。同时采用改进的遗传算法优化了神经网络的参数和权值。其中利用一种动态的交叉率和变异率.有效地加快了收敛的速度。最后,利用Matlab软件对倒立摆进行仿真.仿真结果表明.该控制具有较好的通用性和控制效果。     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

基于神经网络的聚氯乙烯汽提过程自适应解耦控制

聚氯乙烯汽提过程具有高度非线性和时变性等特点,是一类复杂的非线性工业过程.首先基于动态模糊神经网络建立了数据驱动的聚氯乙烯树脂（PVC）汽提过程的被控对象模型;然后采用一种神经网络分散式解耦控制器对汽提过程进行解耦,得到浆料流量-塔顶温度和蒸汽流量-塔底温度两个单变量系统;最后采用BP神经网络PID控制器对系统进行控制.仿真实验结果验证了所提出集成控制策略的有效性.     

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.     

人工神经网络GTAW建模及控制

给出一种用于钨极气体保护电弧焊（ＧＴＡＷ）建及控制的人工神经网络（ＡＮＮ），重点论述利用ＡＮＮ建立焊接参数模型的方法以及在熔深控制方面的应用，通过实验证明，所提出的智能方法具有良好的系统控制性能。     

Intelligent modelling and optimization of the gas tungsten arc welding process

This paper describes an application of neural networks and simulated annealing (SA) algorithm to model and optimize the gas tungsten arc welding (GTAW) process. The relationships between welding process parameters and weld pool features are established based on neural networks. In this study, the counter-propagation network (CPN) is selected to model the GTAW process due to the CPN equipped with good learning ability. An SA optimization algorithm is then applied to the CPN for searching for the welding process parameters with optimal weld pool features. Experimental results have shown that GTAW performance can be enhanced by using this approach.     

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。     

Intelligent process control using neural fuzzy techniques

In this paper, we combine the advantages of fuzzy logic and neural network techniques to develop an intelligent control system for processes having complex, unknown and uncertain dynamics. In the proposed scheme, a neural fuzzy controller (NFC), which is constructed by an equivalent four-layer connectionist network, is adopted as the process feedback controller. With a derived learning algorithm, the NFC is able to learn to control a process adaptively by updating the fuzzy rules and the membership functions. To identify the input–output dynamic behavior of an unknown plant and therefore give a reference signal to the NFC, a shape-tunable neural network with an error back-propagation algorithm is implemented. As a case study, we implemented the proposed algorithm to the direct adaptive control of an open-loop unstable nonlinear CSTR. Some important issues were studied extensively. Simulation comparison with a conventional static fuzzy controller was also performed. Extensive simulation results show that the proposed scheme appears to be a promising approach to the intelligent control of complex and unknown plants, which is directly operational and does not require any a priori system information.     

Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters

In manual welding process, skilled welders can ensure the weld quality through compensating for deviation observed from the weld pool surface. In this paper a three dimensional vision sensing system was used to mimic the human vision system to observe the three-dimensional weld pool surface in pipe GTAW process. Novel characteristic parameters containing information about the penetration state specified by its back-side weld pool width and height were proposed based on the reconstructed three dimensional weld pool surfaces. Then, variation in characteristic parameters and their relationships with the back-side parameters were studied through experiments under different welding conditions. Direct measurement of penetration is not preferred in a manufacturing site, soft-sensing method was thus proposed as an alternative to obtain it in real time due to established soft-sensing model and auxiliary variables which can be sensed in real time. In order to obtain the penetration status in real time conveniently, back-propagation neural network, principle component analysis based back-propagation neural network and global best adaptive mutation particle swarm optimization based back-propagation neural network models were established to estimate the penetration based on the proposed characteristic parameters. It was found that the top-side characteristic parameters proposed can reflect the back-side weld pool parameters accurately and the models are capable of predicting the penetration status in real time by observing the three-dimensional weld pool surface.     

基于模糊RBF神经网络的PID及其应用

针对传统的PID控制器参数固定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp, ki, kd参数。将算法运用到电机控制系统的PID参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。     

Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.