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.

Bayesian zero-failure reliability modeling and assessment method for multiple numerical control(NC) machine tools

A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.

CONTROL OF TWO DIMENSIONAL JETS USING MINIATURE ZERO MASS FLUX JETS

Zero mass flux jets, synthesized by acoustic actuators, have been used for the purpose of jet mixing enhancement and jet vectoring. Zero mass flux jets composed of entirely entrained fluid allow momentum transfer into the embedding flow. In the present experiments, miniature scale high aspect ratio actuator jets are placed along the long sides and near the exit plane of a primary two dimensional jet. In different modes, the primary jet can be vectored either towards or away from the actuator jets and the jet mixing is enhanced. The disturbance of the excitation frequency is developed while the unstable frequency of the primary jet is completely suppressed.

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.

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.

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.

应用于半导体激光器的高精度温控系统设计

面向半导体激光器温控系统的高精度、高速与高集成化的需求,设计了一款高精度、快速响应、高集成化、低成本的数模混合架构温控系统。该系统以FPGA为控制核心,硬件部分包括由三线制惠斯通电桥、仪表放大器、模数转换器组成的温度信号采集与调理模块,全桥降压电路驱动模块,热电制冷器模块等。针对热敏电阻和电桥的非线性误差,提出了一种可变控温零点的温度信号调理方法,该方法基于迭代与多目标最优化算法,提高了控温精度,同时降低了仪表放大器与模数转换器的指标要求,从而降低了系统成本。针对温度滞后大、延迟高的特点,控温策略采用了抗饱和积分的PID(AWPID)自动控制方法,从而降低超调,加快收敛速度。测试结果表明,该温控系统在-45~75℃的温度范围内,实现了±0.02℃的控温精度,相较于固定控温零点的温控系统最大0.1951℃的控温精度提高了89.7%。与传统PID控制算法相比,AWPID控制算法将超调从9.13%降低到1.5%,将稳定时间从41 s降低到30 s。稳定性测试表明,该温控系统能够在长时间内保持±0.02℃的控温精度,满足稳定性要求。该系统具有高精度、快速响应、高集成化、低成本的特点,为半导体激光器的复杂应用场景提供了高精度的温度保障。

实现重症医学科导管相关血流零感染不是梦

导管相关血流感染(catheter related blood stream infection, CRBSI)是重症医学科的常见并发症,可导致患者住院时间延长、医疗费用增加,甚至发生脓毒症致使死亡率增加。CRBSI的感染控制已成为重症医学科乃至医院管理的重点及质量控制的核心。目前,重症医学科的CRBSI预防方案已基本完善,并建立了中心静脉导管置入和维护的国家标准,这些规范的建立和实施可有效降低CRBSI的发生率。重症医学科的CRBSI问题广泛存在且后果严重,表明临床中心静脉置管及使用过程中的无菌操作存在缺陷,针对其操作流程进行改进,从理论、管理、教育、规范、监督等多个层面努力,完善和落实各项感染控制要求,可有效避免和减少CRBSI的发生,甚至实现零感染。

基于非线性修饰和零阶保持器的船舶航向保持控制

[目的]为了解决船舶在海上航行时控制器舵角输出大、打舵频率高、控制速度较慢以及控制精度较低的问题,利用三阶闭环增益成形算法设计鲁棒控制器。[方法]首先,利用三阶闭环增益成形算法设计出线性鲁棒控制器,然后,在控制策略中加入双曲正切非线性修饰以及零阶保持器,在不同海况下对该控制器的性能进行仿真实验。[结果]仿真结果表明,相比于基于非线性修饰的传统PID控制器,在一般海况下,所提控制器在延迟时间、控制精度以及能量输出上分别改进了36%,14%和32%;在恶劣海况下,分别改进了27%,7%和16%。此外,不同海况下该控制器的仿真结果都具有稳定的舵角输出,并可以较快地稳定在临界值附近,证明了其具有较好的鲁棒性。[结论]改进的控制器符合工程实践,对于智能船舶的控制具有较好的应用参考价值。

零碳建筑控制指标体系和科技示范工程研究

随着全球气候变化日益严峻,建筑领域碳排放受到广泛关注。为应对这一挑战,我国启动了“十四五”国家重点研发计划“零碳建筑控制指标及关键技术研究与应用”项目(以下简称“零碳建筑项目”)。截至中期,零碳建筑项目已取得阶段性成果,包括提出建筑碳排放标准化计算方法,建立低碳、近零碳、零碳建筑及区域碳排放控制指标体系,编制国家标准《零碳建筑技术标准》。零碳建筑项目开展科技示范工程,验证零碳建筑技术体系的可行性和有效性。研究成果将为我国建筑领域实现碳达峰、碳中和目标提供重要的技术支撑。

基于多人零和博弈的模块化机器人系统近似最优控制

提出一种基于多人零和博弈的模块化机器人(Modular Robot Manipulators, MRMs)系统近似最优控制方法。建立了具有交联耦合(Interconnected Dynamic Couplings, IDC)的模块化机器人系统动力学模型。将机器人系统的控制律和IDC效应作为零和博弈的参与者,MRM系统的最优跟踪控制问题转化为多人零和博弈问题。根据自适应动态规划(Adaptive Dynamic Programming, ADP)算法,通过建立评判神经网络求解哈密顿-雅克比-埃塞克斯(Hamilton-Jacobi-Issacs, HJI)方程,推导出最优控制律。基于李雅普诺夫定理,证明了闭环机器人系统是渐近稳定的,最后通过实验验证了所提控制方法的有效性。

零速修正辅助的可见光通信列车连续定位方法

精确的列车位置信息是基于通信的列车控制(CBTC)系统实现安全行车、间隔控制的基础。现有的可见光通信列车定位相关研究往往关注列车处于静止或匀速运动的情况,较少考虑惯性测量单元(IMU)的累积误差及列车静止后IMU的零速偏移。为了实现隧道环境下地铁列车连续定位,提出一种借助零速修正的可见光通信列车定位方法。根据可见光通信(VLC)原理,安装在列车头部顶端的相机连续接收隧道壁上发光二极管(LED)光源的光信号,即LED光源闪烁的频率信息,获取LED光源对应的位置信息,进而推算列车的实际位置。同时,根据列车运行的速度、加速度等参数,采用零速修正算法,判断列车处于运动或零速状态;通过无迹卡尔曼滤波,对运动区段内列车恒定速度模型和恒定加速度模型下的列车定位结果进行优化,并对列车停站时IMU的零速偏移进行校正,补偿列车接收端位置和姿态估计精度的发散,实现隧道环境下列车高精度连续定位。实验结果表明:运动区段内,列车以40、60和80 km/h的速度匀速直线运行时,最大定位误差分别为15.27、16.93和19.57 cm;以80 km/h为最大目标速度时,匀加速和匀减速运动的最大定位误差分别为18.48和18.24 cm;利用零速修正和卡尔曼滤波优化列车定位结果,列车停站时的平均定位误差由11.35 cm降低至6.05 cm。所提方法有效抑制了IMU的零速偏移,满足IEEE 1474.1—2004标准对列车定位精度的要求,可为面向车车通信的CBTC系统实现列车定位提供一定的参考。

零速信号在机车电气控制中的应用

随着机车电气控制技术的不断发展,零速信号在机车电气控制中扮演着重要的角色,零速信号是指机车制动或牵引系统中用来检测车辆是否处于零速状态的信号,它能够帮助系统实时监测车辆的运行状态,从而实现精确的控制和调节,文章探讨了零速信号在机车电气控制中的应用,希望能够为机车电气的控制发展提供借鉴。

ZVT-PWM Buck变换器的小信号建模及数字控制

首先介绍了零电压转换-脉宽调制(ZVT-PWM)Buck变换器的工作原理。接着采用等效可控源平均法推导出了变换器的小信号模型以及传递函数的表达式。对小信号模型分析表明,ZVT-PWM Buck变换器比传统Buck变换器的瞬态响应特性更好。然后依据该小信号模型,设计了数字控制器的参数。最后对原理样机进行测试,实测的功率级传递函数验证了小信号模型的正确性,闭环下的负载瞬态响应测试过冲电压为410mV,恢复时间为150μs,优于传统Buck变换器的负载瞬态响应特性。

基于双人零和博弈的孤岛微电网有界L2增益负荷频率控制

针对含不确定建模误差和新能源接入的孤岛微电网,提出了一种结合有界L2增益基线控制和扰动补偿的综合负荷频率控制(LFC)方法。首先,推导了微电网的全驱模型,将建模误差和新能源的影响总结成一个综合扰动。为提高系统的鲁棒性,该综合扰动被定义成控制信号的对手玩家。进一步,关于扰动抑制的有界L2增益问题被等价成求解综合扰动和控制信号的零和博弈Nash均衡,由此推导出有界L2增益基线控制器。为改善频率的二次控制,设计了基于五阶广义积分观测器的扰动补偿控制,并在此基础上引入了一种针对发电速率约束(GRC)的新型抗发电速率约束(anti-GRC)措施。最后,搭建了16 MW的新能源-柴油混合微电网仿真模型,并在多种工况下对所提出的综合LFC方法进行测试,验证了其有效性。

共母线开绕组永磁同步牵引电机级联模型预测电流控制

为抑制共母线开绕组永磁同步牵引电机传动系统转矩脉动和由共模电压以及三次谐波反电动势作用于零轴回路产生的零序电流,提出一种自抗扰级联模型预测电流控制方法。首先,在第1级采用第1个有效电压矢量使得q轴电流增加,第2个有效电压矢量使得q轴电流减小,这一新的筛选组合规则将2个有效矢量根据对q轴电流作用效果分组寻优和作用时间分配;其次,在第2级对第1级所选有效电压矢量的零轴分量局部寻优,在第3级对第1级和第2级优化后系统产生的零轴电流分量采用极性相反的零矢量进行矢量分类寻优抑制,并精确分配作用时间;然后,在第4级对前3级所选电压矢量进行开关状态局部寻优,确定最优电压矢量的低开关频率状态组合作用于逆变器。最后,引入自抗扰控制减少电机启动加速过程中的调速超调,并对所提策略和双矢量模型预测电流控制进行稳态和动态仿真对比。结果表明:所提策略对转矩脉动、零轴电流、总谐波畸变率、开关频率和调速超调都具有很好的抑制效果,避免了传统模型预测控制的多目标代价函数中权重系数整定,对系统的控制性能有明显的提升作用。研究结果为进一步对共母线开绕组永磁同步牵引电机传动系统弱磁控制进行究,以及将驱动控制系统应用于机车牵引提供参考。

基于自学习的LSTM网络短路电流零点预测方法

短路故障在首个大半波内快速选相开断对提升电力系统稳定性具有重要意义。为此,本文研究并提出了一种基于自学习的长短期记忆(LSTM)网络短路电流零点预测方法。构建了基于自学习优化训练的LSTM短路电流预测模型,并采用循环迭代法对短路电流波形及过零点进行预测;搭建了RTDS试验平台,验证了自学习LSTM网络对零点预测的准确性、快速性以及稳定性;讨论了不同短路故障电流的起始相角、谐波含量、信噪比、衰减直流分量时间常数等因素对自学习LSTM预测精度的影响;仿真与试验结果表明自学习LSTM网络对短路电流零点具有较好的预测能力,当采样时间为3 ms时,自学习LSTM首零点预测精度已与RLS算法采样时间5 ms时预测精度相当,为在首个大半波内实现相控开断提供了依据。

基于协同估计的三维多导弹博弈制导律

针对三维空间中多导弹协同拦截机动目标的挑战,基于广义零控脱靶量概念,提出一种新的多导弹协同博弈性能目标函数和分布式抗饱和扩张状态观测器。首先,建立一个三维多导弹拦截机动目标的非线性模型,并提出了一种精确的零控脱靶量计算方法。基于此方法,构建了多导弹协同拦截的博弈性能目标函数,并设计了基于信息融合理论的分布式抗饱和扩张状态观测器对机动目标的机动加速度进行协同估计。最后采用了哈里斯鹰粒子群优化算法来计算制导指令,旨在以最优的燃料消耗实现最优的拦截效果。通过对4种不同交战场景的仿真实验,验证了该协同博弈制导律的优越性和有效性。

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。