High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach

This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of guaranteeing,under some proper non-restrictive initial conditions,the protection constraints control raised by the distance-to-go(moving authority)curve and automatic train protection in practice.A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains.The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability,no adaptations of unknown parameters,function approximation of unknown nonlinearities,and attenuation of external disturbances in the proposed control strategies.Finally,rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.

Quantum speed limit time of a two-level atom under different quantum feedback control

We investigate the quantum speed limit time （QSLT） of a two-level atom under quantum-jump-based feedback control or homodyne-based feedback control. Our results show that the two different feedback control schemes have different influences on the evolutionary speed. By adjusting the feedback parameters, the quantum-jump-based feedback control can induce speedup of the atomic evolution from an excited state, but the homodyne-based feedback control cannot change the evolutionary speed. Additionally, the QSLT for the whole dynamical process is explored. Under the quantum-jump-based feedback control, the QSLT displays oscillatory behaviors, which implies multiple speed-up and speed-down processes during the evolution. While, the homodyne-based feedback control can accelerate the speed-up process and improve the uniform speed in the uniform evolution process.

Crash Modification Factors for Dynamic Speed Feedback Signs on Rural Curves

A large number of crashes occur on curves even though they account for only a small percentage of a system’s mileage. Excessive speed has been identified as a primary factor in both lane departure and curve-related crashes. A number of countermeasures have been proposed to reduce driver speeds on curves, which ideally result in successful curve negotiation and fewer crashes. Dynamic speed feedback sign (DSFS) systems are traffic control devices that have been used to reduce vehicle speeds successfully and, subsequently, crashes in applications such as traffic calming on urban roads. DSFS systems show promise, but they have not been fully evaluated for rural curves. To better understand the effectiveness of DSFS systems in reducing crashes on curves, a national field evaluation of DSFS systems on curves on rural two lane roadways was conducted. Two different DSFS systems were selected and placed at 22 sites in seven states. Control sites were also identified. A full Bayes modeling methodology was utilized to develop crash modification factors (CMFs) for several scenarios including total crashes for both directions, total crashes in the direction of the sign, total single-vehicle crashes, and single-vehicle crashes in the direction of the sign. Using quarterly crash frequency as the response variable, crash modification factors were developed and results showed that crashes were 5% to 7% lower after installation of the signs depending on the model.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

Improvement for consensus performance of multi-agent systems based on delayed-state-derivative feedback

The delayed-state-derivative feedback （DSDF） is in- troduced into the existing consensus protocol to simultaneously improve the robustness to communication delay and accele- rate the convergence speed of achieving the consensus. The frequency-domain analysis, together with the algebra graph the- ory, is employed to derive the sufficient and necessary condition guaranteeing the average consensus. It is shown that introduc- ing the DSDF with the proper intensity in the existing consensus protocol can improve the robustness to communication delay. By analyzing the effect of DSDF on the closed-loop poles, this pa- per proves that for a supercritical-delay multi-agent system, this strategy can also accelerate the convergence speed of achieving the consensus with provided the proper intensity of the DSDE Simulations are provided to demonstrate the effectiveness of the theoretical results.

Asymptotic Tracking Control for Wind Turbines in Variable Speed Mode

This paper presents a variable speed control strategy for wind turbines in order to capture maximum wind power.Wind turbines are modeled as a two-mass drive-train system with generator torque control.Based on the obtained wind turbine model,variable speed control schemes are developed.Nonlinear tracking controllers are designed to achieve asymptotic tracking for a prescribed rotor speed reference signal so as to yield maximum wind power capture.Due to the difficulty of torsional angle measurement,an observer-based control scheme that uses only rotor speed information is further developed for global asymptotic output tracking.The effectiveness of the proposed control methods is illustrated by simulation results.

Collaborative beamforming based on directional perturbation using one bit feedback

A novel collaborative beamforming algorithm is proposed in a wireless communication system with multiple transmitters and one receiver. All transmitters take part in the collaboration and the weighted message is transmitted simultaneously. In order to maximize the beamforming gain, the transmitters use one bit feedback information to adjust the phase offset. It tracks the direction in which the signal strength at the receiver can increase. The directional search and perturbation theory is used to achieve the phase alignment. The feasibility of the proposed algorithm is proved both experimentally and theoretically. Simulation results show that the proposed algorithm can improve the convergent speed of the phase alignment.

CSI Intelligent Feedback for Massive MIMO Systems in V2I Scenarios

With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel state information(CSI)feedback can effectively support URLLC communication in 5G vehicle to infrastructure(V2I)scenarios.Existing research applies deep learning(DL)to CSI feedback,but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect.However,the actual channel still has the influence of additive noise and nonlinear effects,especially in the high-speed V2I scene,the channel characteristics are more complex and time-varying.In response to the above problems,this paper proposes a CSI intelligent feedback network model for V2I scenarios,named residual mixnet(RM-Net).The network learns the channel characteristics in the V2I scenario at the vehicle user(User Equipment,UE),compresses the CSI and sends it to the channel;the roadside base station(Base Station,BS)receives the data and learns the compressed data characteristics,and then restore the original CSI.The system simulation results show that the RM-Net training speed is fast,requires fewer training samples,and its performance is significantly better than the existing DL-based CSI feedback algorithm.It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise.At the same time,the network still has good performance under high compression ratio and low signal-to-noise ratio(SNR).

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

低载波比下基于状态反馈和大林算法的永磁同步电机高性能电流控制策略

受到功率器件开关频率的限制,高速永磁同步电机驱动系统通常需要低载波比运行,此时系统延时相对很大,对电流环设计要求很高。常规在连续域设计的复矢量PI控制器由于离散化实现存在误差,dq轴解耦不完全,而且扰动传递函数包含电机自身低阻尼比极点,使系统抗干扰能力差,尤其是负基波频率附近的扰动。因此,文中提出状态反馈大林控制算法。首先,利用延时环节电压进行反馈来使系统完全解耦;然后,通过输出电流反馈提升系统阻尼来抑制扰动;最后,对解耦且阻尼后的系统设计大林控制器来提升稳定裕度。实验结果表明,相较于复矢量PI控制器,所提出的状态反馈大林控制器dq轴解耦性能好,电流超调小,系统振荡小,且抗扰能力显著提升。

基于精确反馈线性化的PMSM自抗扰控制

为了提升永磁同步电机PMSM在负载变化和其他不确定干扰情况下的速度跟踪性能和抗干扰能力,提出了一种基于精确反馈线性化控制的自抗扰控制方法。首先,采用精确反馈线性化方法,通过坐标变化和状态反馈,将永磁同步电机模型解耦为独立的电流子系统和转速子系统,对转速子系统设计一种自抗扰控制器,对电机系统在运行中的不确定干扰进行实时估计并补偿,提升系统的速度跟踪性能和抗干扰能力。其次,针对自抗扰控制中非线性函数fal在拐点处不平滑的问题,对fal函数进行改进,减小了系统的高频抖振。最后,对基于精确反馈线性化、精确反馈线性化-自抗扰控制两种控制方法在MATLAB/Simulink仿真平台搭建相应的模型进行仿真结果对比,结果显示,精确反馈线性化-自抗扰控制速度跟踪调节时间缩短了58.33%,突加负载后恢复时间提升95.45%,表明该方法能够快速跟踪转速和负载转矩变化,具有良好的抗干扰能力,验证了所提方法的优越性。

一种高速多级全差分放大器设计

设计了一款基于互补双极工艺的高带宽和低噪声全差分放大器,可实现全差分或单端转差分功能。提出了一种H型桥式输入结构,可实现高摆率、低失真;此外,放大器通过内部共模反馈环路将共模信号反馈到两个增益级,实现差分输出共模电平的调整,使高速全差分放大器特别适合于高速高精度模数转换器(ADC)输入信号的缓冲和放大。放大器内部跨导线性环为AB类输出级提供有效偏置,减小输出级失真的同时提供高动态范围。基于EDA工具进行了放大器电路和版图设计,后仿结果表明,在±5 V电源供电下,放大器-3 dB带宽达到366 MHz,压摆率大于1150 V/μs,输入电压噪声小于10nV/√Hz,输入失调电压在±1 mV内。

基于直接转速控制的风电机组最大功率点跟踪策略研究

通过分析各风速下风轮气动转矩特性及发电机在各最大功率跟踪点转矩-转速运行轨迹,依据风电机组在最大功率点上风轮气动转矩与发电机转矩平衡的原理,提出基于发电机电磁转矩的动态最优转速的计算方法,然后以该最优转速为控制目标,设计基于转速偏差的反馈控制器,并做恒转速区与变转速区的控制效果分析。仿真与现场测试表明,该直接转速控制策略可实现最大功率点跟踪,在恒转速区与变转速区具有统一的控制方式,控制逻辑简单实用,有望在风电行业推广,有较好的应用前景。

基于YOLOv5和状态反馈的内燃机转速自动闭环跟踪控制

为了减少内燃机转速自动闭环跟踪控制时间,提出基于YOLOv5和状态反馈的内燃机转速自动闭环跟踪控制方法。通过传感器采集内燃机的转速信息,并结合对应的下降沿关系分析变化信息;分析内燃机的电机结构图,通过电机参数获得转速上限;基于YOLOv5跟踪闭环信息,建构自动闭环策略,通过状态反馈控制转速闭环流程;结合YOLOv5和状态反馈,实现内燃机转速自动闭环跟踪控制。实验结果表明,本文方法的转速跟踪控制收敛时间为13.7s,具有较快的收敛速度。

无反馈通道自激扫掠喷嘴工作特性

无反馈通道自激扫掠喷嘴仅在压力驱动下,通过两股射流在耦合腔内部的相互作用,就能在出口实现液柱喷射方向的自激发高频变化。为了探索此新型喷嘴在航空发动机内燃油喷射的应用潜力,本研究综合采用高速阴影成像法和二维数值模拟方法,研究了不同工作介质、不同缩比尺寸下,无反馈通道自激扫掠喷嘴的特征流速、工作频率、扫掠张角等参数随工作压降的响应变化情况。结果表明,无反馈通道自激扫掠喷嘴在较宽的工作压力范围(0.1~6 MPa)和特征尺寸范围(0.2~7 mm)内均能够实现稳定的自激发扫掠振荡喷射,产生30°以上的扫掠张角和5 kHz以上的振荡频率。无反馈通道自激扫掠喷嘴的扫掠张角基本不随压力和等比缩放特征尺寸而变化,但是其工作频率与其喉道处的特征流速成正比,与喉道宽度成反比,且其频率响应存在双模态现象。

基于反相回差电路的转速输入电路

介绍一种柴油发电机组控制器的转速传感器输入电路,依据柴油发电机组调速控制器的原理,利用反相回差比较的原理,设计输入整形电路、磁头故障电路等,电路简单,操作方便,关键技术是转速传感器的安装,经过多种型号产品的应用,效果不错,很有工程使用价值。

基于振动速度反馈的风电机组塔架左右加阻控制器研究及应用

为抑制风电机组塔架左右(侧向)振动,提出在控制器的设计中,利用振动速度反馈来增加塔架左右方向结构阻尼的方法。首先对塔顶机舱左右振动加速度信号积分,得到振动速度信号,然后使用二阶带通滤波器得到塔架频率主成分,最后乘以增益系数并附加到发电机转矩中。以上海电气3.45MW高速永磁同步风力发电机组为研究对象,基于Bladed软件建模和Matlab平台对该控制策略进行调参和仿真验证,并首次应用于现场控制系统中。结果表明,所提出的速度反馈能够有效降低塔架左右振动加速度,在一定程度上降低了机组故障率,延长了风机寿命。该研究为风电机组主动减振方法设计及应用提供了实际参考。

新能源专用车辆的速度监控策略研究

随着环保需求的增加和新能源技术的发展,新能源专用车辆在我国得到了广泛应用。但是,新能源专用车辆的速度控制策略仍存在研究空白。本文选取新能源专用车辆为研究对象,研究其速度监控策略,首先,根据车辆的动力学模型和驾驶行为模型建立了车速预测模型;然后,设计了一种新的速度控制策略,该策略采用了混合反馈-前馈控制结构,并结合模型预测控制和滑模控制等多种控制方法;最后,在各种典型的驾驶工况下进行了仿真验证。研究结果表明,新的速度控制策略可以有效地改善车辆的速度跟踪性能和能源利用效率,同时还能保证驾驶者的舒适性。该研究为新能源专用车辆的研发和应用,特别是其控制策略的选择和优化提供了重要的参考。

基于加速度反馈的任意道路和车速跟随控制驾驶员模型

建立一个高效的能够适应复杂汽车行驶工况的驾驶员模型是进行'人—车—路'闭环仿真的关键。利用离散的数表方式对驾驶员跟随的任意道路路径和车速进行描述。以此为基础,提出任意路径下的预瞄点搜索算法,使'预瞄—跟随'驾驶员建模理论可应用于任意道路路径和车速的跟随控制。根据车速变化不断更新侧向加速度增益,实现驾驶员模型方向控制和速度控制的解耦。通过引入加速度反馈,建立一个简单而有效的跟随任意道路路径和车速的方向与速度综合控制驾驶员模型。仿真表明该驾驶员模型具有良好的路径与车速跟随精度。