脉冲MIG焊电弧双模模糊控制

针对脉冲M IG焊焊钢时的电弧稳定性控制,采用峰值弧压反馈的方式,设计了基于修正因子的电弧双模模糊控制器,当峰值弧压偏差较大时采用基于修正因子的粗调模糊控制规则,以送丝速度Vf为控制量,而当峰值弧压偏差较小时采用基于修正因子的细调模糊控制规则,以基值时间Tb为控制量,以查表方式实现模糊控制器.最后通过试验验证了在焊接过程中弧长变化时所设计的模糊控制器可保持电弧的稳定从而保证焊接过程的稳定.     

基于模糊和PI控制的MIG焊接电源设计

针对模拟控制和单片机控制的脉冲MIG(Metal Inert Gas)弧焊电源控制系统灵活性差、控制精度低和可靠性差等缺点,设计了基于模糊和PI控制的MIG焊接电源控制系统。为了提高焊接电流的控制精度,控制焊接电流的PI参数在一个周期的不同阶段应该是不同,所以该系统的焊接电流控制采用变参数PI控制方法。在不同焊接条件下的PI参数由专家系统确定。为了提高电弧弧长的稳定性,电弧电压控制采用模糊控制方法。模糊控制和变参数PI控制算法分别由数字信号控制器(DSC)和现场可编程门阵列(FPGA)实现。最后,介绍了系统的硬件电路设计和软件流程。利用焊接铝板对该系统进行了测试,测试结果表明,基于模糊和PI控制的MIG焊接电源控制系统动态响应快、可靠性高、弧长控制稳定。     

CO_2弧焊波形控制的仿真

该文对研制的一种新型CO2 弧焊IGBT电流波形控制器的波控过程进行了计算机仿真 ,仿真结果与工艺试验是吻合的。研究表明用本文研制的仿真系统可对电流波形控制参数进行仿真试验 ,并可确定不同焊接规范对应的波控参数 ,从而为研究波控参数对焊接熔滴过渡过程的影响规律 ,确定实时电流波形控制的基本模式提供依据。     

A self-organizing fuzzy control of weld pool size in GMA welding processes

In arc welding processes, because of the complexity and nonlinearity of heat transfer phenomena, it is often difficult to design an effective control system based upon an exact mathematical model. In this paper, a self-organizing fuzzy control method is proposed to obtain a uniform weld quality by regulation of the surface temperature at a desired level. The proposed control system is designed to comprise a combined structure of the fuzzy SOC with conventional PD control for improving the system's stability. The control experiments show that the proposed controller guarantees a uniform weld quality for various welding conditions.     

Development of an automatic arc welding system using an adaptive sliding mode control

This paper develops an automatic welding control scheme for alternating current shielded metal arc welding (SMAW) system. A mathematical model of the welding control system is derived and the system parameters identified. An adaptive sliding mode controller is designed to estimate the bound of the system uncertainties and to modulate the electrode feed rate in such a way that the desired arc length and arc current are maintained as the electrode melts during the welding process. The proposed control method is suitable for any consumed electrode welding technique. The simulation and experimental results show that the automatic welding control system successfully maintains the magnitude of the arc current at the desired value and preserves the arc stability, thereby obtaining an enhanced SMAW control system performance.     

Modeling and control of carbon monoxide concentration using aneuro-fuzzy technique

Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The δ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by δ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive     

平面二级倒立摆自适应滑模控制器设计

研究平面二级倒立摆系统稳定性和速度特性优化问题,由于倒立摆系统的外界扰动的不确定性,建立平面二级倒立摆的数学模型,应用变结构控制理论(SMC)和模糊逻辑系统设计了自适应滑模控制器,把趋近律和切换控制的模糊化相结合,采用模糊系统调整趋近速率的大小,在加快趋近速度的同时用模糊逼近切换控制,为减少控制量的抖振和优化控制系统,同时倒立摆控制具有了滑模控制对外界扰动和参数摄动的不变性。进行仿真的结果验证了控制器的稳定性,表明控制器系统能保证在不同的运行条件下具有快速性和鲁棒性。     

非对称饱和扇形输入非线性系统的自适应模糊控制

针对一类具有未知不确定性,且状态不可测的非线性系统,考虑了输入端的饱和非对称扇区非线性特性影响,提出了系统模型未知情形下基于自适应模糊观测器的跟踪控制方案,采用Lyapunov-Krasovskii函数给出了滑模控制器参数和模糊逻辑的自适应调整律.所提方法不仅可保证闭环跟踪系统的稳定性,还削弱了传统方法对模型结构的依赖...     

一种新的多模自适应前馈控制器

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多个确定性扰动对象的多模自适应前馈控制器,它的特点是综合了从参考模型取状态的MRAC、多输入前馈控制和开关-比例-保持模式控制各自的优点。该控制器不仅可以消除可测干扰的影响,具有较好的控制性能,而且也适用于非最小相位系统。文中针对焊接过程的数学模型,给出了仿真实例研究。     

参数自整定模糊控制器在弧焊焊缝跟踪中的应用

本文阐述了焊缝跟踪参数自整定模糊控制器的工作原理，提出了比例因子和量化因子自动调节的方案和实现方法，介绍了模糊控制器的具体设计步骤和单片机参数自整定模糊控制系统的软硬件设计。结果表明，在焊缝跟踪中采用参数自整定模糊控制器可以明显改善系统的性能。     

基于AFSMC算法的四旋翼飞行器控制策略研究

针对四旋翼飞行器在飞行过程中,控制系统存在非线性、强耦合、不确定性和鲁棒性差的问题,建立了关于四旋翼飞行器的动力学数学模型,将自适应控制、模糊控制和滑模控制相结合,提出基于自适应模糊滑模控制(AFSMC)的快速平稳控制策略。采用模糊系统推理方法实现理想控制律的逼近。在满足李雅普诺夫稳定性条件的前提下进行控制器的设计和稳定性分析,并结合四旋翼的数学模型和给定参数进行了MATLAB仿真。仿真结果表明,AFSMC控制器相比常规PID控制器具有良好的动态性能和抗干扰能力。     

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.     

SVM-based fuzzy rules acquisition system for pulsed GTAW process

This paper proposes a support vector machine-based fuzzy rules acquisition system (SVM-FRAS) for modeling of the gas tungsten arc welding (GTAW) process. The character of SVM in extracting support vector provides a mechanism to extract fuzzy IF–THEN rules from the training data set. We construct the fuzzy inference system using fuzzy basis function. The gradient technique is used to tune the fuzzy rules and the inference system. Theoretical analysis and comparative tests are performed comparing with other fuzzy systems. Modeling is one of the key techniques in the automatic control of the arc welding process, and is still a very difficult problem. Comprehensibility is one of the required characteristics in modeling for the complex GTAW process. We use the proposed SVM-FRAS to obtain the rule-based model of the aluminum alloy pulse GTAW process. Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.     

Nonlinear interval model control of quasi-keyhole arc welding process

This paper addresses the development of a nonlinear model based interval model control system for the quasi-keyhole arc welding process, a novel arc welding process which has advantages over the laser welding process and conventional arc welding processes. The structure of the nonlinear model chosen was proposed based on an analysis of the quasi-keyhole process to be controlled. Because of the variations in the manufacturing conditions, the parameters of the nonlinear model are uncertain but bounded by fixed intervals if the range of the manufacturing conditions is specified. To determine the intervals, extreme operating conditions/parameters (manufacturing conditions) were used to conduct experiments. Each experiment gives a set of model parameters and the interval for each parameter is given by the minimum and maximum among the values obtained from different experiments. Closed-loop control experiments have verified the effectiveness of the developed system as a robust control which requires no re-adjustment and can function properly when fluctuations/variations in manufacturing conditions, and thus the process dynamics, change, vary, or fluctuate.     

埋弧自动焊系统建模与控制方案设计

针对目前尚未有精确描述埋弧焊系统的数学模型而难以达到理想控制目标的现状, 对埋弧自动焊系统按照机理建模的方法建立数学模型, 分别建立了焊丝电压降子模型、熔滴电压降子模型和电弧压降模型三个子模型, 作为埋弧焊系统中控制电弧的理论依据, 并进一步根据所建立的模型提出了相应的变速送丝控制方案, 经过控制系统的仿真验证了控制方案可以维持埋弧焊过程中送丝速度变化和焊接电弧之间的稳定性.     

Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring

The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding. However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN) model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap.     

污水处理系统溶解氧的模糊自适应PID控制

曝气生物滤池的工艺复杂,参数检测困难,并具有大滞后、干扰多等特点,所以要实现该工艺的自动控制,保证生产的稳定、高效运行并非易事。本文就影响曝气生物滤池工艺处理效果的曝气过程提出了溶解氧模糊自适应PID控制算法。仿真表明,模糊自适应PID控制器的控制性能优于常规模糊控制器和PID控制器,既可保证出水水质,又能节约动力费用。     

CO_2弧焊机器人电弧电压及焊接电流的智能控制

研制了一种以可编程控制器为核心器件 ,通过软件方式实现 CO2 弧焊机器人电弧电压及焊接电流合理匹配的智能控制系统 .系统通过具有修正因子自寻优功能的自组织模糊控制器维持所选定的焊接电流不变 .同时 ,系统针对 CO2 焊的短路、半短路和细颗粒过渡焊的电弧电压采取不同的自寻优目标和寻优方式进行寻优 .寻优后的电弧电压与焊接电流形成合理匹配 ,达到了稳定熔滴过渡过程 ,减少飞溅 ,改善成形的目的     

Dynamic switching based fuzzy control strategy for a class of distributed parameter system

In this work, a dynamic switching based fuzzy controller combined with spectral method is proposed to control a class of nonlinear distributed parameter systems (DPSs). Spectral method can transform infinite-dimensional DPS into finite ordinary differential equations (ODEs). A dynamic switching based fuzzy controller is constructed to track reference values for the multi-inputs multi-outputs (MIMO) ODEs. Only a traditional fuzzy logic system (FLS) and a rule base are used in the controller, and membership functions (MFs) for different ODEs are adjusted by scaling factors. Analytical models of the dynamic switching based fuzzy controller are deduced to design the scaling factors and analyze stability of the control system. In order to obtain a good control performance, particle swarm optimization (PSO) is adopted to design the scaling factors. Moreover, stability of fuzzy control system is analyzed by using the analytical models, definition of the stability and Lyapunov stability theory. Finally, a nonlinear rod catalytic reaction process is used as an illustrated example for demonstration. The simulation results show that performance of proposed dynamic switching based fuzzy control strategy is better than a multi-variable fuzzy logic controller.     

用模糊集合理论设计一类控制器

本文阐述把模糊集合理论用于设计一类缺乏精确数学模型的控制器.在DJS-130数字 机上,用BASIC语言实现了模糊算法.对模糊控制器的一些特性进行了数字仿真,与PI控制 器进行了比较.数字仿真的结果表明,模糊控制器对单位阶跃响应具有速度较快,精度较高以 及对参数变化不敏感等优点.用小型数字机,特别是微型计算机作为控制器,可以实现模糊控 制系统的联机实时控制和人机对话.