QFT control based on zero phase error compensation for flight simulator

To improve the robustness of high-precision servo systems, quantitative feedback theory （QFT） which aims to achieve a desired robust design over a specified region of plant uncertainty is proposed. The robust design problem can be solved using QFT but it fails to guarantee a high precision tracking. This problem is solved by a robust digital QFT control scheme based on zero phase error （ZPE） feed forward compensation. This scheme consists of two parts： a QFT controller in the closed-loop system and a ZPE feed-forward compensator. Digital QFT controller is designed to overcome the uncertainties in the system. Digital ZPE feed forward controller is used to improve the tracking precision. Simulation and real-time examples for flight simulator servo system indicate that this control scheme can guarantee both high robust performance and high position tracking precision.

ZERO PHASE ERROR REAL TIME CONTROL FOR FLIGHT SIMULATOR SERVO SYSTEM

Flight simulator is an important device and a typical high performanceposition servo system used in the hardware-in-the-loop simulation of flight control system. Withoutusing the future desired output, zero phase error controller makes the overall system's frequencyresponse exhibit zero phase shift for all frequencies and a very small gain error at low frequencyrange can be achieved. A new algorithm to design the feed forward controller is presented, in orderto reduce the phase error, the design of proposed feed forward controller uses a modified plantmodel, which is a closed loop transfer function, through which the system tracking precisionperformance can be improved greatly. Real-time control results show the effectiveness of theproposed approach in flight simulator servo system.

Quantitative feedback theory and zero phase error tracking control combined robust control for radar truck leveling simulator

Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effiectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.

Zero phase error control based on neural compensation for flight simulator servo system

Using the future desired input value, zero phase error controller enables the overall system＇s frequency response exhibit zero phase shift for all frequencies and a small gain error at low frequency range, and based on this, a new algorithm is presented to design the feedforward controller. However, zero phase error controller is only suitable for certain linear system. To reduce the tracking error and improve robustness, the design of the proposed feedforward controller uses a neural compensation based on diagonal recurrent neural network. Simulation and real-time control results for flight simulator servo system show the effectiveness of the proposed approach.

Robust Iterative Learning Controller for the Non-zero Initial Error Problem on Robot Manipulator

Industrial robot system is a kind of dynamic system w ith strong nonlinear coupling and high position precision. A lot of control ways , such as nonlinear feedbackdecomposition motion and adaptive control and so o n, have been used to control this kind of system, but there are some deficiencie s in those methods: some need accurate and some need complicated operation and e tc. In recent years, in need of controlling the industrial robots, aiming at com pletely tracking the ideal input for the controlled subject with repetitive character, a new research area, ILC (iterative learning control), has been devel oped in the control technology and theory. The iterative learning control method can make the controlled subject operate as desired in a definite time span, merely making use of the prior control experie nce of the system and searching for the desired control signal according to the practical and desired output signal. The process of searching is equal to that o f learning, during which we only need to measure the output signal to amend the control signal, not like the adaptive control strategy, which on line assesses t he complex parameters of the system. Besides, since the iterative learning contr ol relies little on the prior message of the subject, it has been well used in a lot of areas, especially the dynamic systems with strong non-linear coupling a nd high repetitive position precision and the control system with batch producti on. Since robot manipulator has the above-mentioned character, ILC can be very well used in robot manipulator. In the ILC, since the operation always begins with a certain initial state, init ial condition has been required in almost all convergence verification. Therefor e, in designing the controller, the initial state has to be restricted with some condition to guarantee the convergence of the algorithm. The settle of initial condition problem has long been pursued in the ILC. There are commonly two kinds of initial condition problems: one is zero initial error problem, another is non-zero initial error problem. In practice, the repe titive operation will invariably produce excursion of the iterative initial stat e from the desired initial state. As a result, the research on the second in itial problem has more practical meaning. In this paper, for the non-zero initial error problem, one novel robust ILC alg orithms, respectively combining PD type iterative learning control algorithm wit h the robust feedback control algorithm, has been presented. This novel robust ILC algorithm contain two parts: feedforward ILC algorithm and robust feedback algorithm, which can be used to restrain disturbance from param eter variation, mechanical nonlinearities and unmodeled dynamics and to achieve good performance as well. The feedforward ILC algorithm can be used to improve the tracking error and perf ormance of the system through iteratively learning from the previous operation, thus performing the tracking task very fast. The robust feedback algorithm could mainly be applied to make the real output of the system not deviate too much fr om the desired tracking trajectory, and guarantee the system’s robustness w hen there are exterior noises and variations of the system parameter. In this paper, in order to analyze the convergence of the algorithm, Lyapunov st ability theory has been used through properly selecting the Lyapunov function. T he result of the verification shows the feasibility of the novel robust iterativ e learning control in theory. Finally, aiming at the two-freedom rate robot, simulation has been made with th e MATLAB software. Furthermore, two groups of parameters are selected to validat e the robustness of the algorithm.

ASYMPTOTIC EXPANSIONS OF ZEROS FOR KRAWTCHOUK POLYNOMIALS WITH ERROR BOUNDS

Krawtchouk polynomials are frequently applied in modern physics. Based on the results which were educed by Li and Wong, the asymptotic expansions of Krawtchouk polynomials are improved by using Airy function, and uniform asymptotic expansions are got. Furthermore, the asymptotic expansions of the zeros for Krawtchouk polynomials are again deduced by using the property of the zeros of Airy function, and their corresponding error bounds axe discussed. The obtained results give the asymptotic property of Krawtchouk polynomials with their zeros, which are better than the results educed by Li and Wong.

Adaptive Zero Phase Error Feedforward Method for Robotic Force Control

In order to remove the time delay between the input and the output signals of a robot force control system,adaptive zero phase error feedforward(AZPEF)control method is presented and applied to PUMA 560 industrial robot,which has six degree of freedom(6-DOF).The whole adaptive force control algorithm is implemented on TMS320C30 micro-processor whose instruction cycle is 60ns.The results of the force control experiments prove that AZPEF force control makes robot have good robustness and quick response ability.

因瓦水准标尺零点差和分划误差的一次检测方法

因瓦水准标尺是国家长度标准体系的载体,广泛应用于我国大地测量、高精度水准测量、变形监测、数字城市等领域。针对目前国内外水准标尺依赖激光干涉仪及检测中存在的分划误差检测、米间隔长度平均值与标称值之差检测、一幅标尺零点差之差检测需要分多次测量且处理复杂等问题,本文研制出一套水准标尺零点差检测附件,将其安装在LSS-2水准标尺测量系统上,通过最小二乘线性回归计算,一次扫描检测即可获得水准标尺分划误差,米间隔长度平均值与标称值之差、单支标尺零点差的检测和校准,方便了水准标尺检测工作。实验结果表明,该方法与传统野外三测回方法相比,零点差之差误差为1.5μm,满足水准标尺检定规范要求,且工作效率显著提高,具有重要的工程应用价值。

太阳能光伏发电并网逆变器控制技术的应用

该文阐述光伏发电并网逆变器的控制方法,而后提出基于正弦波脉宽调制逆变技术的并网逆变器控制策略。该文旨在合理化解太阳能利用效率低下的问题,探寻出最佳的逆变器控制方法,实现零静态误差运行,并提升逆变器运行稳定性,从而将太阳能资源有效转化成为电能。

基于图像深度先验和鲁棒马尔可夫随机场的有遮挡人脸识别

由遮挡所引发的测试数据和训练数据之间的差异,是人脸识别技术面临的重要挑战。现有的基于深度神经网络的有遮挡人脸识别方法大多需要使用大规模的有遮挡的人脸图像来训练网络模型。然而,现实世界中的任何外界物体都有可能成为遮挡,有限的训练集数据很难穷尽所有的可能性,并且使用大规模的有遮挡人脸图像训练网络模型的做法与人类视觉机制是相违背的,人眼对于遮挡区域的感知在本质上与遮挡本身并没有关系,仅依赖于无遮挡的人脸图像。为了模拟人类视觉的遮挡检测机制,将图像深度先验和鲁棒马尔可夫随机场模型结合起来,构建基于小样本数据的遮挡检测模型DIP-rMRF,并提出了一致性零填充方法以有效利用DIP-rMRF的遮挡检测结果进行后续的人脸识别。在Extended Yale B,AR和LFW这3个人脸数据库上,针对VGGFace, LCNN,PCANet, SphereFace, InterpretFR,FROM这6种CNN模型的实验结果表明,DIP-rMRF能够有效地处理遮挡以及由极端光照所引发的“类遮挡”,从而极大提升现有的深度神经网络模型对有遮挡人脸识别的性能。

Zero-Max型无级变速器连杆机构的误差分析

通过分析Zero-Max型无级变速器连杆机构误差的影响因素,得出输出角误差是各原始误差的线性组合。根据机构运动学理论,对连杆机构传动误差进行分析,把可调六杆机构拆成2个串连的四杆机构,建立了Zero-Max型无级变速器连杆机构的输出角误差方程。

压力表检定中的常见问题及解决办法探析

压力表是工业生产中的重要检测仪器,其计量精准性对企业的安全生产具有重要的影响。为有效处理压力表在检定过程中可能遇到的问题,提高压力表的准确性和可靠性,对压力表的工作原理及检定内容进行分析,在此基础上,该文讨论压力表计量检定过程中可能出现的问题及其形成原因,并制定切实可行的解决方案。分析结果显示,零点误差超差、轻敲位移、跳针或滞针、示值误差超差和回程误差超差等是压力表计量检定中的常见问题。为此,应通过分析误差或故障的发生原因并按照解决方案进行检修,以提高计量检定的水平和效率,促使压力表能够在工业生产中得以准确应用。

基于参数辨识的磁聚焦式扭矩传感器模型研究

为解决磁聚焦式扭矩传感器模型未知参数问题,实现其更精确地测量,文中通过对此传感器机理分析建立了数学模型与非零初始条件下的系统模型,确定了辨识参数矩阵。提出了一种预测误差法与自适应粒子群算法相结合的参数辨识方法。搭建正弦激励校准实验系统对其进行验证,结果表明,该方法有效地实现了传感器参数的辨识,得到的传感器模型具有较高的精度,为其动态特性分析与误差补偿提供了可靠依据。

基于固定桥式坐标测量机的条码水准标尺校准装置

为了校准条码水准标尺,建立了校准装置。校准装置系统由固定桥式三坐标测量机、平面反射镜和数字水准仪构成,经置中调整后,形成精密测量系统,通过平面反射镜将水平放置条码水准标尺转置为垂直立像,并经数字水准仪读取和解码,得到条码尺示值,与标准位移值形成数据列,经分段叠加最小二乘拟合,以直线斜率作为被测条码尺的每米真长补偿因子。校准装置实现方式简便,占地空间小,测量精度高,数据评价科学。

400 Hz中频电源波形多重比例谐振控制策略

针对比例积分(PI)控制器难以实现输出电压无静差控制及低次谐波抑制困难等问题,提出一种单电压环多重比例谐振(PR)控制策略。分析了PR控制器的特性,给出了PR参数的设计方法,通过MATLAB进行了仿真验证,并在2 kVA样机上进行了实验验证。结果表明,所提策略可实现特定谐波的完全消除,输出电压波形总谐波失真小于1%,具有良好的稳态和动态性能。

排放源废气CO_(2)连续监测分析方法解析

为解决我国CO_(2)连续监测在排放源废气监测体系中的缺失,完成碳中和、碳达峰远景目标,系统的梳理了NDIR、TDLAS、FTIR等常见的排放源废气中CO_(2)连续监测分析方法,同时分析了国内外相关的监测标准的差异性,提出了加强CO_(2)连续监测设备研发和优化设备选型,以设备性能约束指标为依托,尽快建立完善的排放源CO_(2)连续监测体系等方面的建议。

求解常微分方程的两类零化神经网络

设计了两类求解常微分方程的零化神经网络.把常微分方程重写为向量值不确定误差函数,将向量值不确定误差函数代入零化神经网络设计公式中,提出了一类求解常微分方程的连续时间零化神经网络,其指数收敛于零.针对潜在的数字硬件实现,通过对连续时间零化神经网络进行离散化,设计了一类离散时间零化神经网络,给出了充要条件来保证离散时间神经网络生成的序列以截断误差为O(τ^(2))(τ>0表示采样周期)收敛到零.通过两个数值实验验证了连续时间零化神经网络和离散时间零化神经网络的有效性.

基于位移传感器的磁环沉降和水位检测尺零值误差校准装置

分析了磁环沉降和水位检测尺零值误差校准方式的发展现状,研究了该类仪器零值误差已有校准装置及校准方式的特性。结合前述的研究以及磁环沉降和水位检测尺的现实使用状态,针对此种计量器具基于位移传感器提出了一种零值误差校准方式并研制了一套专用的零值校准装置,最后进行的不确定度评定表明校准装置的研制能够满足相关计量器具零值误差溯源的需求。

基于零相位拟合模型的野值识别及剔除方法

通道相位不一致性是影响干涉仪测向精度的重要因素之一,修正零相位校正数据能够提高测向精度。本文提出了基于零相位拟合模型的野值识别方法,通过推导零相位的线性拟合模型,建立拟合相位误差,分析出拟合相位误差服从正态分布,从而将零相位野值识别转换为拟合相位误差的粗大误差识别,最后采用3σ准则和格拉布斯准则进行粗大误差的判别及剔除。通过本方法能够有效识别并剔除零相位野值,试验计算分析表明,零相位野值识别及剔除方法能够有效提高干涉仪测向精度,并已经在工程中得到应用。

A LOW EVM ZERO-IF RF TRANSMITTER FOR COGNITIVE RADIO APPLICATION

A zero-IF transmitter for Cognitive Radio(CR) application is presented.To effectively reduce the interference between Power Amplifier(PA) and Voltage Controlled Oscillator(VCO),two VCOs are adopted,one is 450 MHz and the other is from 1148 MHz to 1252 MHz with an 8 MHz step,so the frequency of them are different from the operational frequency of PA.The Local Oscillator(LO) of the modulator generated by mixing the signals of the two VCOs has a low phase noise of-82 dBc/Hz with an offset of 1 kHz.The measurement result of the transmitter shows that the Adjacent Channel Power Ratio(ACPR) is less than-47.5 dBc at 27 dBm output,and the Error Vector Magnitude(EVM) is less than 1.7%.