Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Low Crosstalk Three-Color Infrared Detector by Controlling the Minority Carriers Type of InAs/GaSb Superlattices for Middle-Long and Very-Long Wavelength

We report a type-Ⅱ InAs/GaSb superlattice three-color infrared detector for mid-wave （MW）, long-wave （LW）, and very long-wave （VLW） detections. The detector structure consists of three contacts of NIPIN architecture for MW and LW detections, and hetero-junction NIP architecture for VLW detection. It is found that the spectral crosstalks can be significantly reduced by controlling the minority carriers transport via doping beryllium in the two active regions of NIPIN section. The crosstalk detection at MW, LW, and VLW signals are achieved by selecting the bias voltages on the device. At 77K, the cutoff wavelengths of the three-color detection are 5.3μm （at OmV）, 141μm （at 300mV） and 19μm （at -20mV） with the detectivities of 4.6xlO11 cm.Hzl/ZW-1, 2.3×10^10 cm.Hzl/2W-1, and 1.0×10^10cm.Hzl/2W-1 for MW, LW and VLW. The crosstalks of the MW channel, LW channel, and VLW channel are almost 0, 0.25, and 0.6, respectively.

The Formation Mechanism and Control Methods of High Temperature Infrared Radiant Coating Defects

Using high aluminum refractory material as substrate at 1400℃, we studied the connections between several oxides such as Fe203, MnOv CuO, and the formation of defects such as coating crack, exfoliation, blistering, erosion, and fading away appeared in the application of high temperature infrared radiation coating. Analyses showed that thermal stress formed during the heating process due to the thermal expansion coefficient differential between the coating and the substrate, and volume effect caused by the crystal transferred when the temperature changed, which resulted in the coating crack and exfoliation. The gas produced by the reactions between components and binder or the components themselves during the heating process caused the coating blistering. The EMPA and XRD analyses show that oxides with low melting point in the penetrating area of the substrate may form eutectic with low melting point and produced thermal defects, which leads to the erosion by penetrating to the substrate. The valent changes of Fe2O3 and MnO2 during the heating process cause the volatilization of the oxides or the pulverization of the coatings, resulting in the coating fades away easily at high temperature for a long time.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Neuron self-learning PSD control for backside width of weld pool in pulsed GTAW with wire filler

In this paper, the weld pool shape control by intelligent strategy was studied. A neuron self-learning PSD controller for backside width of weld pool in pulsed GTAW with wire filler was designed. The PSD control arithmetic was analyzed, simulating experiment by MATLAB software was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were successfully implemented. The study results show that the neuron self-learning PSD control method can attain a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy:A review

Near infrared （NIR） spectroscopy as a rapid and nondestructive analytical technique, integrated with chemometrics, is a powerful process analytical tool for the pharmaceutical industry and is becoming an attractive complementary technique for herbal medicine analysis. This review mainly focuses on the recent applications of NIR spectroscopy in species authentication of herbal medicines and their geo- graphical origin discrimination.

Developing selective mining capability for longwall shearers using thermal infrared-based seam tracking

Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned within the coal seam. At present, this machine positioning task is the role of longwall personnel who must simultaneously monitor the longwall coal face and the shearer's cutting drum position to infer the geological trends of the coal seam. This is a labour intensive task which has negative impacts on the consistency and quality of coal production. As a solution to this problem, this paper presents a sensing method to automatically track geological coal seam features on the longwall face, known as marker bands, using thermal infrared imaging. These non-visible marker bands are geological features that link strongly to the horizontal trends present in layered coal seams. Tracking these line-like features allows the generation of a vertical datum that can be used to maintain the shearer in a position for optimal coal extraction. Details on the theory of thermal infrared imaging are given, as well as practical aspects associated with machine-based implementation underground. The feature detection and tracking tasks are given with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a new selective mining capability to help address a long-standing limitation in longwall mining operations.

Infrared Heating Technology for Automated Fiber Placement

Prepreg stickiness is the adhesion between prepregs or between a prepreg and a mold in lay-up process.It is critical for automated fiber placement,because the stickiness should be small for smooth transport,as well as large enough on the laying surface for a good placement performance.To ensure prepreg stickiness always being in the optimum laying window,placement temperature should be changed according to the laying speed.In our work,the relationship between laying speed and emissive power of heating lamp was studied.The heat transfer process between heating lamp and laying surface was analyzed and the control equation of dynamic temperature was derived.Finally,the infrared heating system was built and its effectiveness was verified based on placement experiment.

A Study on the Optimal Offset Distance Between a Welding Torch and the Infrared Thermometers

Detection of weld defects using real-time monitoring and controlling algorithm is of the significant task in manufacturing industries due to the increased production and liability costs that result when weld defects are not identified early in the production cycle.Monitoring and controlling for robotic arc welding process employed should be reliable,flexible and cost-effective in non-clean,high-volume production environments.Also,the robotic welding system has been utilized a complex jigging and mechanical devices to move the workpiece which related to the stationary welding head for getting higher efficiency and lower costs.To develop the fully robotic welding system,people make use of their senses of sound and/or sight to collect welding information,and take the necessary corrective measurements to ensure the weld quality after processing is satisfactory.Therefore,it is really required that the monitoring and controlling algorithm of sensors for increasing effectiveness in the robotic welding process has been developed.In this paper,bead-on-plate welding using an infrared thermography in the robotic GMA(Gas Metal Arc)welding process has been performed to study the effects of welding parameters on thermal profile characteristics and find the optimal offset distance which applied for monitoring and controlling of welding quality such as bead height.The analysis for correlation between temperature distributions at three offset distance and bead height which based on the regression analysis such as Standard Error of Estimate(SEE),the coefficient of correlation(R)and coefficient of determination(R2)and(Predictive Ability of Model)has been done.The infra-red sensor is useful for monitoring the isotherm radii that arise during the robotic welding process and identifying bead height as welding quality.

Controller Design for a 3 D.O.F Haptic Interface Device

This paper presents a 3 D.O.F haptic interface which is designed to meet the interaction requirement of teleoperation tasks and virtual reality applications. The mechanism design takes the operability into consideration such as adopting steel cable as transmission component and mass balance to eliminate the gravity effect. The dynamics of haptic interface including actuating device is studied. In order to provide operator with fidelity kinesthetic information, a force controller using self-learning fuzzy logic control is designed. The simulation results verify the effectiveness of the control method.

Infrared Image Small Target Detection Based on Bi-orthogonal Wavelet and Morphology

An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.

基于ADRC的高精度红外热成像仪温度控制系统设计

为了可以更加精准地控制红外热成像仪温度,设计一种基于ADRC的高精度红外热成像仪温度控制系统;由系统供电模块、探测器驱动模块、模数转换模块、视距估算模块、视频信号处理模块、外部存储模块、以太网传输模块和温度控制模块组成温度控制系统;利用自抗扰控制设计一种单轴控制器,将由时延带来的不确定性建模作为系统综合扰动的一部分,对扩张状态观测器以及总和扰动展开估计,同时在前馈通道对其展开补偿,消除时延对控制器产生的影响,将控制器应用于红外热成像仪温度控制中,获取高精度的红外热成像仪温度控制结果;通过实验分析证明,设计系统的帧频、响应波段、启动时间、供电范围和功耗测试结果分别为55 Hz、11μm、3.4 s、12 VDC和0.56 W,具有良好的红外热成像仪温度控制性能,可以满足提取高质量的红外图像的要求。

一体化大型压铸模具温度控制方法

新能源汽车的发展使一体化压铸完成了从0到1的突破,给压铸模具发展带来了新的机遇。压铸模具是一个热交换器,压铸模具成型中的热平衡问题至关重要。以压铸模具的温度控制为基础,介绍了压铸模具的基本概况与压铸成型加工过程中热平衡的重要性,阐述了目前一体化压铸模模温控制所采用的模温控制岛、异形水路、红外线成像技术、模内传感器、一体化压铸模模温控制集成化等方案。模具温度的控制有利于提高铸件质量,延长模具使用寿命,提高压铸效率。

近红外光谱技术在茶叶生产质控与品质评价中的研究进展

茶是世界上最受欢迎的饮料之一,在国内外均有庞大的市场。市场的发展对茶叶的品质要求不断提高,而快速、可靠的茶叶质量控制与品质评价技术是辅助茶叶品质提升的重要手段,一直是研究者关注的热点。传统的茶叶质量与品质检测方法费力、耗时或极具主观性。近红外光谱技术以其简单、无损、快速的特点,在茶叶生产质控与品质评价中越来越受到人们的欢迎。基于近红外光谱的检测技术是一种间接的检测方法,其检测结果容易在数据传递过程中受到各种影响因素的干扰,本文总结了近红外光谱技术在茶叶生产质控与品质评价中的重要影响因素,分析了近红外光谱技术在茶叶生产质控与品质评价中的应用状况,并对研究前景进行展望,为应用近红外光谱技术于茶叶生产质控与品质评价中的研究方向提供参考。

不同干燥方式对红托竹荪干燥特性和品质的影响

为提高红托竹荪干燥品质并获得最佳干燥工艺,采用真空红外干燥(vacuum infrared drying,VID)、气流冲击干燥(air impingement drying,AID)、控湿干燥(moisture control drying,MCD)等不同干燥方式对红托竹荪进行对比研究,以热风干燥(hot air drying,HAD)作为对照组,研究不同干燥方式及温度对红托竹荪干燥品质的影响。试验结果表明不同干燥方式对竹荪宏观品质产生了显著影响,其中MCD可获得最小的色差∆E和收缩率,AID则能够保证较高的复水比;干燥速率方面,MCD在前期能够获得较高的干燥速率,但后期干燥速率会放缓,而AID在整个干燥过程都具有较高的干燥速率,干燥时间较短;在成分保留上,MCD可以保留较高含量的多糖、三萜和黄酮,而采用VID可以有效保护多酚。单位能耗随干燥温度的升高明显降低,不同方式下VID的干燥能耗值整体偏大,MCD的单位能耗最低(18.82 kW·h/kg)。通过主成分分析法,上述干燥方式对红托竹荪综合评分后得到的结果排序为:MCD>AID>VID>HAD,MCD干燥方式中采用60℃相对湿度为40%,保湿时间15min的干燥工艺综合品质较佳,在此加工方式下的红托竹荪综合评分为2.94,该研究可为红托竹荪高品质干燥提供一定参考。

航天器可展开式热辐射器的设计方法与应用研究

受限于运载火箭的包络尺寸,航天器体装式辐射器的散热面积有限,为进一步提高大功率航天器的系统级散热能力,提出了基于单相流体回路的可展开式热辐射器的热控方案。建立热辐射器的热分析模型,通过肋参数选取、红外辐射影响分析、工质选用对比等方法,完成可展开式热辐射器的设计优化,提高辐射器的散热效率及散热能力,为可展开式热辐射器的在轨首飞应用奠定基础。结果表明,在地球同步轨道航天器的恶劣外热流条件下,单幅辐射器的散热能力可达254 W/m^(2)以上,采用多幅可展开式热辐射器的方法,可有效解决航天器散热能力不足的问题。

钢包在线监测报警技术的研究及应用

为实现某钢厂钢包在线监测,以西门子S7-1200PLC为控制器,通过现场检测元器件、执行元件及PLC之间的控制与反馈,高效、安全地给出有效判断预警和反馈处理。通过分析解决日常生产过程中存在的钢包实际问题,完成了在线监测系统的搭建和功能的优化,提高了对钢包耐材的监控能力,实现了对钢包漏钢等事故的预防,提升了设备维护人员的技能,强化了现场的安全管理,同时降低了作业整体成本。

一种移动平台红外搜索跟踪系统的稳定控制方法

基于移动平台的红外搜索跟踪系统已成为新一代光电搜索跟踪系统的主流趋势,小型化和轻量化是其高机动性的保证。载体运动姿态变化耦合的角速度扰动和系统内部力矩干扰将对其搭载的光电载荷视轴稳定控制带来严峻挑战,基于多轴多框架和高精度陀螺反馈控制相结合的传统视轴稳定方法已无法适用。本文针对两轴两框架移动平台红外搜索跟踪系统的光电载荷视轴稳定控制,提出了基于平方PI和Luenberger扰动观测前馈的双速度闭环同阶串级控制方法。仿真和实验表明,相对于传统的单陀螺闭环和双速度闭环稳定控制方法,该稳定控制方法有效提升了载体运动低频扰动下的视轴稳定精度。1°/1 Hz扰动下,仿真稳定精度达到2.7817μrad,实验稳定精度达到35.85μrad。1°/2 Hz扰动下,仿真稳定精度达到38.199μrad,实验稳定精度达到119.1μrad。最终,利用本文提出的稳定控制方法,两轴两框架移动平台红外搜索跟踪系统有效克服了行进间载体运动姿态变化耦合的低频角速度扰动,实现了高平稳高动态的光电载荷视轴指向控制性能。