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Fuzzy predictive Stanley lateral controller with adaptive prediction horizon 具有自适应预测视界的模糊预测Stanley横向控制器
Pub Date : 2023-03-31 DOI: 10.1177/00202940231165257
Ahmed Abdelmoniem, Abdullah Ali, Youssef Taher, M. Abdelaziz, S. Maged
The challenge of trajectory tracking of autonomous vehicles (AVs) is a critical aspect that must be effectively addressed. Recent studies are concerned with maintaining the yaw stability to guarantee the customers’ comfort throughout the journey. Most of the geometrical controllers solve this task by dividing it into consecutive point stabilization problems, limiting the controllers’ ability to handle sudden trajectory changes. One research presented a predictive Stanley lateral controller that uses a discrete prediction model to mimic human behavior by anticipating the vehicle’s future states. That controller is limited in its use, as the parameters must be manually tuned for every change in the maneuver or vehicle velocity. This article presents a novel approach for trajectory tracking in autonomous vehicles, by introducing a fuzzy supervisory controller that automatically adapts to changes in the vehicle’s velocity and maneuver by estimating the prediction horizon’s length and providing different weights for each controller. The proposed method overcomes the limitations of traditional controllers that require manual tuning of parameters, making it ready for real-world experiments. This is the main contribution of the research in this paper. The suggested technique demonstrated an advantage over the Basic Stanley controller and the manually tuned predictive Stanley controller in terms of the total lateral error and the model predictive control (MPC) in terms of computational time. The performance is determined by performing various simulations on V-Rep and hardware-in-the-loop (HIL) experiments on an E-CAR golf bus. A broad selection of velocities is used to validate the behavior of the vehicle while working on different maneuvers (double lane change, hook road, S road, and curved road).
自动驾驶汽车(AVs)的轨迹跟踪挑战是必须有效解决的关键问题。最近的研究关注的是保持偏航稳定性,以保证客户在整个旅程中的舒适性。大多数几何控制器通过将其划分为连续的点稳定问题来解决这一任务,限制了控制器处理突然轨迹变化的能力。一项研究提出了一种预测性斯坦利横向控制器,该控制器使用离散预测模型通过预测车辆的未来状态来模仿人类行为。该控制器的使用受到限制,因为必须手动调整参数以适应机动或车辆速度的每次变化。本文提出了一种自动驾驶汽车轨迹跟踪的新方法,通过引入模糊监督控制器,该控制器通过估计预测视界的长度并为每个控制器提供不同的权值来自动适应车辆速度和机动的变化。该方法克服了传统控制器需要手动调整参数的局限性,为实际实验做好了准备。这是本文研究的主要贡献。所建议的技术在总横向误差和模型预测控制(MPC)计算时间方面优于基本Stanley控制器和手动调谐预测Stanley控制器。通过在一辆E-CAR高尔夫总线上进行V-Rep仿真和硬件在环(HIL)实验,确定了该系统的性能。广泛的速度选择用于验证车辆在不同机动(双车道变换,钩路,S路和弯曲道路)下的行为。
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引用次数: 0
SF-YOLOv5: Improved YOLOv5 with swin transformer and fusion-concat method for multi-UAV detection SF-YOLOv5:改进的YOLOv5,带有旋转变压器和融合连接方法,用于多无人机检测
Pub Date : 2023-03-27 DOI: 10.1177/00202940231164126
Jun Ma, Xiao Wang, Cuifeng Xu, Jing Ling
When dealing with complex trajectories, and the interference by the unmanned aerial vehicle (UAV) itself or other flying objects, the traditional detecting methods based on YOLOv5 network mainly focus on one UAV and difficult to detect the multi-UAV effectively. In order to improve the detection method, a novel algorithm combined with swin transformer blocks and a fusion-concat method based on YOLOv5 network, so called SF-YOLOv5, is proposed. Furthermore, by using the distance intersection over union and non-maximum suppression (DIoU-NMS) as post-processing method, the proposed network can remove redundant detection boxes and improve the efficiency of the multi-UAV detection. Experimental results verify the feasibility and effectiveness of the proposed network, and show that the mAP trained on the two datasets used in experiments has been improved by 2.5 and 4.11% respectively. The proposed network can detect multi-UAV while ensuring accuracy and speed, and can be effectively used in the field of UAV monitoring or other types of multi-object detection applications.
传统的基于YOLOv5网络的检测方法在处理复杂的飞行轨迹以及无人机自身或其他飞行物的干扰时,主要针对一架无人机,难以有效检测多架无人机。为了改进检测方法,提出了一种将swin变压器块与基于YOLOv5网络的融合连接方法相结合的新算法SF-YOLOv5。此外,该网络采用距离交联和非最大抑制(DIoU-NMS)作为后处理方法,可以去除冗余检测盒,提高多无人机的检测效率。实验结果验证了该网络的可行性和有效性,实验中使用的两个数据集上训练的mAP分别提高了2.5%和4.11%。该网络能够在保证精度和速度的前提下对多架无人机进行检测,可有效应用于无人机监控领域或其他类型的多目标检测应用。
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引用次数: 0
Single-layer neural-network based control of agricultural mobile robot 基于单层神经网络的农业移动机器人控制
Pub Date : 2023-03-27 DOI: 10.1177/00202940231164125
C. Gökçe
In this study, we propose a novel controller architecture and design for the automatic control of agricultural mobile robots to be used in farms and greenhouses. There are two novelties of this study. The first novelty is a completely new type of controller architecture proposed in which reference inputs and measured outputs are fed separately independent from each other to the controller. The controller architecture currently used in the literature uses only the difference between reference and measurement which is the error signal. The proposed architecture in this study is completely novel in the sense that not only the error information is used in the controller but also the information in reference inputs and information in measured outputs are used separately. This means a completely new type of look to control system by utilizing the information maximally in order to achieve superior performance. This performance boost is shown in the paper where the proposed architecture achieves up to 2000% better performance compared with state-of-the-art controllers. Second, controller architecture is grown to a complex structure from an initially simple PID structure. Using the maximal information comes with the cost of computational complexity to design the controller. The second novelty of growing the controller from initially simple PID equivalent controller tackles this difficulty by making the problem tractable and efficient to compute. This way the proposed novel controller can be designed within minutes in a commercially available laptop computer. The proposed controller is tested on a simulated agricultural mobile robot and results are compared with a previous state-of-the-art optimal controller. It is believed that the proposed architecture will be dominant in future automatic controllers and make current state-of-the-art controllers obsolete. This is because of the full utilization of information in controller design which results in robust disturbance rejection performance.
在这项研究中,我们提出了一种新的控制器架构和设计,用于农场和温室中使用的农业移动机器人的自动控制。这项研究有两个新奇之处。第一个新颖之处是提出了一种全新类型的控制器架构,其中参考输入和测量输出分别独立地馈送到控制器。目前在文献中使用的控制器架构仅使用参考和测量之间的差异,即误差信号。本研究中提出的架构是完全新颖的,因为不仅在控制器中使用误差信息,而且参考输入中的信息和测量输出中的信息是分开使用的。这意味着一种全新的外观控制系统,通过最大限度地利用信息来实现卓越的性能。这种性能提升在论文中显示,与最先进的控制器相比,所提出的架构实现了高达2000%的性能提升。其次,控制器结构由最初简单的PID结构发展为复杂的结构。利用最大的信息来设计控制器是以计算复杂度为代价的。从最初简单的PID等效控制器发展控制器的第二个新颖之处通过使问题易于处理和有效计算来解决这一困难。这样,所提出的新型控制器可以在几分钟内在商用笔记本电脑上设计出来。在一个模拟农业移动机器人上对所提出的控制器进行了测试,并与已有的最优控制器进行了比较。人们认为,所提出的架构将在未来的自动控制器中占据主导地位,并使当前最先进的控制器过时。这是因为在控制器设计中充分利用了信息,具有较强的抗扰性能。
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引用次数: 0
Compressive equivalent source method based on particle velocity measurements for near-field acoustic holography 基于粒子速度测量的近场声全息压缩等效源方法
Pub Date : 2023-03-27 DOI: 10.1177/00202940231160569
Wen-Qian Jing, Guang-Sheng Liu
In order to solve the problems of spatial resolution limit and reduce the measurement cost, the compressive sensing theory and the sparse regularization are applied to the near-field acoustic holography (NAH) technology. In the previous works, the Equivalent Source Method (ESM)-based NAH has been extended in the sparsity framework by sampling the sound pressure signals sparsely. However, this Compressive ESM (CESM) mode would suffer from the low precision problem in the particle velocity reconstruction. To improve the reconstruction accuracy of the particle velocity, this paper is going to take the sparse particle velocity as the input of NAH to establish a new CESM mode based on the particle velocity measurement. In the meantime, the number of sampling points will be reduced greatly without losing the reconstruction accuracy when compared to the conventional ESM with the Tikhonov regularization based on the particle velocity measurement. Several numerical simulation experiments have been carried out to examine the performance of the proposed model. The results show that the proposed model delivers a satisfactory performance in the reconstruction of both the pressure and particle velocity on the condition that the number of sampling points is much smaller than that of the conventional ESM, and the proposed model performs much better than the existing CESM mode based on the sound pressure measurement especially when reconstructing the particle velocity.
为了解决空间分辨率限制和降低测量成本的问题,将压缩感知理论和稀疏正则化技术应用于近场声全息技术。在以往的工作中,基于等效源方法(ESM)的声压信号稀疏采样在稀疏框架中得到了扩展。但是,压缩ESM (CESM)模式在粒子速度重建中存在精度低的问题。为了提高粒子速度的重建精度,本文将稀疏粒子速度作为NAH的输入,建立一种基于粒子速度测量的新的CESM模式。同时,与传统的基于粒子速度测量的Tikhonov正则化ESM相比,在保证重构精度的前提下,大大减少了采样点的数量。为了验证所提出模型的性能,进行了一些数值模拟实验。结果表明,该模型在采样点数量远少于传统ESM的情况下,对声压和粒子速度的重构都有较好的效果,且在重构粒子速度方面明显优于现有的基于声压测量的CESM模型。
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引用次数: 0
Terminal super-twisting sliding mode based on high order sliding mode observer for two DOFs lower limb system 基于高阶滑模观测器的末端超扭滑模二自由度下肢系统
Pub Date : 2023-03-26 DOI: 10.1177/00202940231156195
T. Tran, V. T. Duong, Huy Hung Nguyen, T. Nguyen
In this paper, a novel backstepping terminal super-twisting sliding mode (TSTSM) with high order sliding mode observer (HOSMO) is proposed to control the two degrees of freedom (DOFs) Serial Elastic Actuator (SEA), inspired by a lower limb of humanoid robots. First, the dynamic model, extended from our previous study, is presented for developing the control algorithm. Secondly, the backstepping technique is utilized to separate the overall system into two subsystems. One of the challenges of SEA is to deal with the evident oscillations caused by the elastic element, which might lead to the degrading performance of load position control. In order to reduce this adverse effect, a TSTSM is proposed to control the position tracking of two subsystems. The advantages of TSTSM are the finite-time convergence despite the bounded perturbation and the dramatic reduction of the chattering phenomenon. To construct and implement the TSTSM controller, it requires the knowledge of all states, which is not available in the current lower limb system setup. Therefore, a HOSMO is utilized to estimate the unknown states. Finally, experiment results are carried out to assess the effectiveness of the proposed controller and compare it with those of different control schemes.
本文以仿人机器人下肢为灵感,提出了一种具有高阶滑模观测器(HOSMO)的反步末端超扭转滑模(TSTSM)来控制两自由度串联弹性作动器(SEA)。首先,在前人研究的基础上,提出了用于开发控制算法的动态模型。其次,利用回溯技术将整个系统分离为两个子系统。SEA的挑战之一是如何处理由弹性元件引起的明显的振荡,这可能导致载荷位置控制性能的下降。为了减少这种不利影响,提出了一种TSTSM来控制两个子系统的位置跟踪。TSTSM的优点是在有界扰动的情况下具有有限时间收敛性和显著减少抖振现象。为了构造和实现TSTSM控制器,需要了解所有状态,这在目前的下肢系统设置中是不具备的。因此,利用HOSMO来估计未知状态。最后,通过实验验证了所提控制器的有效性,并与不同控制方案进行了比较。
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引用次数: 0
Wear debris measurement in lubricating oil based on inductive method: A review 基于归纳法的润滑油磨损碎片测量研究进展
Pub Date : 2023-03-25 DOI: 10.1177/00202940231159117
Shimin Yang, Nan Cao, Bing Yu
Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.
机器运行过程中产生的磨损碎屑几乎全部悬浮在循环润滑油中。对润滑油中的磨损碎屑进行分析,可以有效地监测机器的磨损状态,提供故障预警。介绍了用于测量润滑磨损碎片的电感式传感器的概况。首先,阐述了分析润滑油中磨损屑的意义,阐述了电感式磨损传感器的工作原理。此外,总结了电感式磨损传感器的发展及其主要局限性。最后讨论了影响传感器性能的其他因素以及感应信号混叠的处理方法,并对未来的发展趋势进行了展望。指出开发高灵敏度磨损屑电感传感器、提高传感器吞吐量、解决检测信号混叠问题是今后需要进一步研究的问题。
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引用次数: 1
Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern 一类基于多维运动模式的复杂生产过程系统鲁棒镇定
Pub Date : 2023-03-22 DOI: 10.1177/00202940231161573
Jinxia Wu, Jianwei Li, Chuang Liu, Wei-Tzen Yang
This paper studies robust stabilization problem and state feedback controller design problem for a class of complex production process systems by pattern class variable instead of state variable or output variable. Firstly, the maximum entropy clustering method is introduced and modeling method based on multi-dimensional moving pattern is given. Nonlinear state space models of systems with input-delay are established by using the trajectories of pattern class variable. Then sufficient conditions for robust stabilization of the system are obtained by Linear Matrix Inequality. In addition, a state feedback controller is designed based on this condition. Finally, the effectiveness of the methods is illustrated by taking the actual working conditions data as an example.
研究了一类复杂生产过程系统用模式类变量代替状态变量或输出变量的鲁棒镇定问题和状态反馈控制器设计问题。首先介绍了最大熵聚类方法,并给出了基于多维运动模式的建模方法。利用模式类变量的轨迹,建立了具有输入延迟系统的非线性状态空间模型。然后利用线性矩阵不等式得到了系统鲁棒镇定的充分条件。在此基础上,设计了状态反馈控制器。最后以实际工况数据为例,说明了方法的有效性。
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引用次数: 0
Performance characterization of network timing with remote traceability via GNSS time transfer 基于GNSS时间传输的远程跟踪网络授时性能表征
Pub Date : 2023-03-20 DOI: 10.1177/00202940231161568
K. Liang, Zhiqiang Yang, Wei Fang, Jian Wang
One credible method for performance evaluation of network timing service was proposed, based on one kind of disciplined time standard with GNSS time transfer, NIMDO. The performance of the NTP timing service were characterized over multiple baselines on both the Internet and the Intranet, and the network delay and the timing offset were analyzed in detail. The results show that on the Internet the averaged timing offsets via the NTP servers are roughly hundreds of μs to several ms level, and on the Intranet the averaged timing offsets via the NTP servers are at about 20 μs level. NTP timing service was mainly affected by white phase noise, and flicker phase noise at the different sites. The maximum change of the timing offset was one-half of the maximum change of RTDelay. The uncertainties have been separately evaluated as less than 30 ms on the Internet and less than 60 μs on the Intranet for NTP timing service.
提出了一种可靠的网络授时服务性能评估方法,该方法基于GNSS授时规范NIMDO。在Internet和Intranet的多个基线上对NTP定时服务的性能进行了表征,并对网络延迟和定时偏移进行了详细分析。结果表明,在Internet上,通过NTP服务器的平均时间偏移量大致为数百μs到几ms级别,而在Intranet上,通过NTP服务器的平均时间偏移量约为20 μs级别。NTP定时服务主要受不同站点的白相位噪声和闪烁相位噪声的影响。时序偏移量的最大变化是RTDelay最大变化的一半。对于NTP定时服务,不确定度分别在Internet上小于30ms和Intranet上小于60 μs进行了评估。
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引用次数: 0
Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties 一类具有时变不确定性的非线性压电微定位系统的鲁棒高精度跟踪控制
Pub Date : 2023-03-20 DOI: 10.1177/00202940231159928
Long Li, Shengzheng Kang, Dongming Bai, Hongtao Wu, Jiangli Yu
Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.
压电微定位系统(pms)在高精度操作中得到了广泛的应用,但它也受到了非线性的影响,如滞后和参数不确定性。针对这一问题,针对一类具有时变不确定性的非线性pms系统,提出了一种新的鲁棒滑模控制方案。与传统的滑模控制(SMC)不同,该控制器进一步将基于傅立叶级数的函数估计技术、模糊逻辑系统和自适应学习算法相结合,实现了对系统时变不确定性的在线估计和补偿。利用李雅普诺夫稳定性理论得到了傅里叶系数和模糊可调参数的自适应规律。与现有的SMC控制方法相比,所提出的控制方法有效地消除了控制抖振问题,在存在时变不确定性的情况下,保证了跟踪误差在有限时间内收敛。理论分析和数值仿真结果表明,所提出的控制策略能够满足微纳操作中pms高速、高精度的鲁棒跟踪性能要求。
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引用次数: 0
Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack 基于自适应未知输入观测器的智能交通恶意攻击检测与识别方法
Pub Date : 2023-03-20 DOI: 10.1177/00202940231159115
P. Cheng, Jinyan Pan, Yi Zhang
This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked ith residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system.
本文旨在开发一种针对智能交通中恶意攻击的新型检测和识别方法。由于通信和先进传感器技术的发展和应用,智能交通面临着新的安全风险。特别是新出现的恶意攻击,如虚假数据注入攻击,可以通过篡改层内信息来掩盖对物理动态的破坏,从而骗过现有的检测方法。为此,提出了一种基于自适应未知输入观测器的检测与识别方法。首先,考虑实际物理状态,建立了车联网系统的物理动力学模型;针对虚假数据注入攻击的欺骗特征,提出了一种基于未知输入观测器的检测方法。通过自适应未知输入观测器参数的设计,可以通过减小状态估计误差来提高检测性能。仿真结果表明,与基于ui的检测方法相比,该方法的误报率可降低0.1%。基于状态残差对带残差攻击不敏感,但对其他残差敏感的特点,提出了一种新的状态残差识别准则。最后,在Matlab上进行仿真实验,验证了所提出的检测识别算法在智能交通系统中的性能。
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引用次数: 0
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Measurement and Control
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