Closed-loop control of weld penetration in keyhole plasma arc welding

To improve the penetrating ability and the welding quality of keyhole plasma arc welding, a novel penetration closed loop control system was established. In the system, welding current and plasma gas flow rate were selected as adjusting variables. The wavelet method was used to detect penetration status from welding arc voltage in real time. The control strategy of one keyhole per pulse was adapted to fulfill stable and high quality welding process. Experimental results show that the developed system can apparently increase the penetrating force of plasma arc and keyhole plasma arc welding is realized successfully in stainless steel with 10 mm in thickness. Moreover, the disturbances of gradual change and break change from 3 mm to 6 mm in thickness are come over due to the good response property of the developed system.

A NEW PENETRATION CONTROL APPROACH FOR APPLICATION TO VARYING ROOT THICKNESS

The influence of plate thickness on the penetration in GTAW has been investigated and a front pool width penetration control approach has been studied, which controls the penetration under the variation of plate thickness. A theoretical heat flow model for a plate of a finite thickness has been utilized to investigate the relationship of the pool widths on both sides. A penetration control system for autogenous GTA welding has been utilized which uses a coaxial weld pool imaging system and computer vision processing to sense the width of the GTA weld pool. Empirical results show the good performance of this penetration control approach in the presence of variations in a joint root thickness of 1 mm (0.04 inch) to 3 mm (0.125 inch).

Real-time measurement of welding temperature field and closed loop control of penetration

Colormetric method of images by using two different wavelength images is a new measuring method for welding temperature field on the basis of ordinary colorimetric method, which depends little on the measuring distance, emissivity of body etc. In this paper the real time measuring system and measuring principle of welding temperature field are described, the whole welding temperature field is real time measured, so the temperature distribution at the welding direction and its cross section is obtained, then parameters of thermal cycle. With data from the temperature closed loop control system of the parameters of temperature field is developed and tested. Experimental results prove that it has high measurement speed (time of a field within 0.5 s ) and good dynamic response quality. Weld penetration can be controlled satisfactorily under the variation of welding condition such as welding thickness, welding speed and weldment gap etc.

A NEURAL NETWORK FOR WELD PENETRATION CONTROL IN GAS TUNGSTEN ARC WELDING

Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set （including 973 groups of geometrical parameters of the weld pool and back-side weld width） and the verifying data set （108 groups）. Two data sets are used for training and verifying the neural network, respectively. The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.

Review of Field Weakening Control Strategies of Permanent Magnet Synchronous Motors

Due to high power density,high efficiency,and accurate control performance,permanent magnet synchronous motors(PMSMs)have been widely adopted in equipment manufacturing and energy transformation fields.To expand the speed range under finite DC-bus voltage,extensive research on field weakening(FW)control strategies has been conducted.This paper summarizes the major FW control strategies of PMSMs,which are categorized into calculation-based methods,voltage closed-loop control methods,and model predictive control related methods.The existing strategies are analyzed and compared according to performance,robustness,and execution difficulty,which can facilitate the implementation of FW control.

基于ControlVAE的约束嵌入稠密时变阵列构建

在对探地雷达(Ground Penetrating Radar,GPR)数据进行三维建模与可视化分析过程中,其所依赖的三维时变阵列通常是由一维数据道或二维阵列间接生成的.由于采集到的数据往往比较稀疏,且存在不规则偏移,需要进行稠密处理,以获得高密度三维时变阵列.本文提出了一种基于可控变分自编码(Controllable Variational AutoEncoder,ControlVAE)的约束嵌入稠密时变阵列构建方法 .使用基于ControlVAE的时变数据重构网络,通过隐空间采样插值和深浅层特征信息的融合,生成接近真实分布的伪道数据来增加阵列密度.构建了基于尺度不变特征变换的数据配准模块,提取道间梯度特征与结构对称性特征,可以在时空域内完成数据配准.同时将浅层特征作为约束信息嵌入数据重构网络,以消除数据道偏移对阵列生成的影响.实验结果表明,本文方法仅使用单测线二维B-scan数据集即可重建稠密时变阵列,可以有效降低重构模型累积误差,提高面对复杂真实数据时的鲁棒性.

Experimental confirmation of physical metal penetration generation and press casting production considering molten metal's pressure control

This paper presents a technique for controlling the pressure of a molten metal when using a new type of iron casting method called sand mold press casting to realize high productivity and obtain high-quality products.The past test results using this method showed a casting yield of 90% to 95%,while conventional methods only show a casting yield of 60% to 70%.Although the press casting method does not require a sprue cup or runner channel casting defects such as metal penetration are often caused by the high pressure in the high-velocity pressing part of this casting process.Therefore,we proposed a pressure control method with a mathematical model of molten metal pressure,and with it we achieved experimental confirmation of the successful production of brake drums at different pressing temperatures.Results show that the proposed pressing control method can realize sound,penetration-free casting production.However,the theoretical analysis and design of this pressing process had not previously been studied sufficiently,and therefore this paper presents the theoretical design algorithm for the process as well as its experimental confirmation.

Fuzzy neural networks for control of penetration depthduring GTAW

An intelligent system including both a neural network(NN) and a self adjusting fuzzy controller(FC) for modeling and control of the penetration depth during gas tungsten arc welding(GTAW) process is presented in this paper. The discussion is mainly focused on two parts. One is the modeling of the penetration depth with NN. A visual sensor CCD is used to obtain the image of the molten pool. A neural network model is established to estimate the penetration depth from the welding current, pool width and seam gap. It is demonstrated that the proposed neural network can produce highly complex nonlinear multi variable model of the GTAW process that offer the accurate prediction of welding penetration depth. Another is the control for the penetration depth with FC.A self adjusting fuzzy controller is proposed,which used for controlling the penetration depth.The control parameters are adjusted on line automatically according to the controlling errors of penetration and the errors can be decreased sharply. The effectiveness of the proposed intelligent methods is demonstrated by the real experiments and the improved performance results are obtained.

Investigation of front pool width penetration control for application to varying plate thickness

The influence of plate thickness on the penetration in GTAW was investigated and a theoretical heat flow model for a plate of finite thickness has been utilized to investigate the relationship between front and back pool widths.Then,front pool width penetration control approach approach has been studied, which controls penetration under the variation of plate thickness.A penetration control system for autogenous GTA welding has been utilized which includes a coaxial weld pool imaging system and computer vision processing system to sense the front width of the GTA weld pool.Empirical results show the good performance of this penetration control approach in the presence of variations in joint plant thickness of 1 mm 0.04 inch) to 3 mm(0.125 inch)

Penetration control by weld pool resonance during gas tungsten arc welding

Presents penetration control by weld pool resonance which occurs when the natural frequency of weld pool is equal to the frequency of sine wave current while the weld pool is excited into oscillation by superimposing sine wave current with definite frequency or regular frequency on DC current, and experiments carried out on detecting resonance signals during both stationary and travelling arc welding with variant frequency pulse current, and concludes with experimental results that penetration control can be realized by weld pool resonance when welding speed is lower than 80mm/min, and this control method is applicable to welding thin (0.5～3.0 mm) plates of carbon steel, low alloy steel, high strength steel and superhigh strength steel, and suitable for alternating polarity welding of stainless steel, titanium alloy steel and aluminum alloy.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

A closed-loop particle swarm optimizer for multivariable process controller design

Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter(Pressure)Closed-loop Process Control Systems

A type of single neuron adaptive PID regulator with auto-tuning gain is proposed and applied to the work control of fans, waterpumps and air-pressers etc. in Handan Iron & Steel Compel China. The robusthess of induStrial parameter closed-loop process controlsystems is improved, and the work quality of the systems bettered.

Solving position-posture deviation problem of multi-legged walking robots with semi-round rigid feet by closed-loop control

The semi-round rigid feet would cause position-posture deviation problem because the actual foothold position is hardly known due to the rolling effect of the semi-round rigid feet during the robot walking. The position-posture deviation problem may harm to the stability and the harmony of the robot, or even makes the robot tip over and fail to walk forward. Focused on the position-posture deviation problem of multi-legged walking robots with semi-round rigid feet, a new method of position-posture closed-loop control is proposed to solve the position-posture deviation problem caused by semi-round rigid feet, based on the inverse velocity kinematics of the multi-legged walking robots. The position-posture closed-loop control is divided into two parts: the position closed-loop control and the posture closed-loop control. Thus, the position-posture control for the robot which is a tight coupling and nonlinear system is decoupled. Co-simulations of position-posture open-loop control and position-posture closed-loop control by MATLAB and ADAMS are implemented, respectively. The co-simulation results verify that the position-posture closed-loop control performs well in solving the position-posture deviation problem caused by semi-round rigid feet.

Interference Management for DS-CDMA Systems through Closed-Loop Power Control, Base Station Assignment, and Beamforming

In this paper, we propose a smart step closed-loop power control (SSPC) algorithm and a base station assignment method based on minimizing the transmitter power (BSA-MTP) technique in a direct sequence-code division multiple access (DS-CDMA) receiver with frequency-selective Rayleigh fading. This receiver consists of three stages. In the first stage, with constrained least mean squared (CLMS) algorithm, the desired users’ signal in an arbitrary path is passed and the inter-path interference (IPI) is reduced in other paths in each RAKE finger. Also in this stage, the multiple access interference (MAI) from other users is reduced. Thus, the matched filter (MF) can use for more reduction of the IPI and MAI in each RAKE finger in the second stage. Also in the third stage, the output signals from the matched filters are combined according to the conventional maximal ratio combining (MRC) principle and then are fed into the decision circuit of the desired user. The simulation results indicate that the SSPC algorithm and the BSA-MTP technique can significantly reduce the network bit error rate (BER) compared to the other methods. Also, we observe that significant savings in total transmit power (TTP) are possible with our methods.

The integrated intelligent system for welding seam error and penetration depth identification

A integrated intelligent system for seam tracking and penetration control is given. The system received information of welding seam error and penetration depth from only one sensor, then, it realized seam tracking and penetration control simultaneously. This paper introduces constitution of the system, methods of information recognition, design of the neural fuzzy controller and results practically.

Penetration analytical model of three dimensional welding temperature field and experiment rectification

A three dimensional analytical model for heat conduction in a plate of finite size with a Gaussian distributed moving heat source, is obtained using the Heat Cumulating Principle and the Method of Image in arc welding, and an estimated method of back width of seam is introduced by making use of the model and the measured top face information of temperature field . To prove the validity of the model, a series of GTA bead on plate welding were performed on a medium carbon steel under various welding conditions, the experimental results show that the theoretical prediction can provide acceptable accuracy, so the next penetration control will be based on the model.

Research of the algorithm of the closed-loop control system to control the piezoelectric actuator

The piezoelectric actuator has been widely used in precision instruments and precision control. However, hysteresis, nonlinearity and creep exist in the actuator, which limit actuator applications and affect accuracy. This thesis introduces fuzzy control as the algorithm of a closed-loop control system to control the piezoelectric actuator. Fuzzy control can make this closed-looped system not only have high linearity, repeatability, accuracy and few overshoot, but isalso easily used.

Informative Property of the Data Set in a Single-input Single-output (SISO) Closed-loop System with a Switching Controller

Closed-loop identification is important and necessary to various model-based advanced process control strategies, whose performance depends greatly on the informative property of the data set. Switching control is an important method in process control. Therefore, this paper studies the informative property of a data set in a single-input single-output (SISO) closed-loop system with a switching controller. It is proved that this data set is informative if the controller switches among at least two modes (i.e., feedback laws). Our result does not require any assumption on the way of switch and removes the constraints on the switching manner required in some classical literature. Finally, simulation case studies based on a continuous stirred-tank reactor (CSTR) process are given to validate the results.

A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems

In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.