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Output feedback control for mobile robot systems with significant external disturbances

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Science China(Information Sciences) 2020年第9期63卷 277-280页

作者： Kang WU jinya su Changyin suN Key Laboratory of Measurement and Control of CSE Ministry of EducationSchool of Automation Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

Dear editor,The control of nonholonomic systems, like those describing mobile robots, has attracted considerable research attention in recent decades owing to its theoretical and practical importance. Brockett’s theo... 详细信息

关键词： Nonlinear system Nonholonomic system Adaptive control Extended state observer(ESO) Disturbance rejection

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具有执行器饱和的不确定系统的干扰抑制控制

Journal of Southeast University(English Edition) 2024年第2期40卷 120-128页

作者：苏金亚谢鹏飞李世华东南大学自动化学院南京210096

针对传统的干扰观测器(DO)设计并未考虑实际系统中普遍存在的执行器饱和问题,提出了一种具有抗饱和补偿的基于干扰观测器的控制(DOBC)策略.首先探究传统DOBC在执行器饱和时产生积分饱和现象的原因;然后通过最小化性能指标设计了一种抗... 详细信息

针对传统的干扰观测器(DO)设计并未考虑实际系统中普遍存在的执行器饱和问题,提出了一种具有抗饱和补偿的基于干扰观测器的控制(DOBC)策略.首先探究传统DOBC在执行器饱和时产生积分饱和现象的原因;然后通过最小化性能指标设计了一种抗积分饱和的补偿器,并将其与DO结合,使得修改后的DOBC能够有效处理执行器饱和;最后对所得闭环系统进行局部渐近稳定性分析.比较仿真结果表明,存在执行器饱和时,所提出控制方法具有更小的位置跟踪误差和干扰估计误差,并且设计的补偿器可以保持DO的状态尽可能接近没有执行器饱和时的状态,进而验证了所提出控制方法在抗干扰和抗饱和方面具有优越性.

关键词：抗积分饱和干扰观测器动态补偿器执行器饱和

Segmentation of field grape bunches via an improved pyramid scene parsing network

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International Journal of Agricultural and Biological Engineering 2021年第6期14卷 185-194页

作者： Shan Chen Yuyang Song jinya su Yulin Fang Lei Shen Zhiwen Mi Baofeng su College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services Yangling 712100ShaanxiChina College of Enology Northwest A&F UniversityYangling 712100ShaanxiChina School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

With the continuous expansion of wine grape planting areas,the mechanization and intelligence of grape harvesting have gradually become the future development trend.In order to guide the picking robot to pick grapes more efficiently in the vineyard,this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network(PSPNet)deep semantic segmentation network for different varieties of grapes in the natural field environments.To this end,the Convolutional Block Attention Module(CBAM)attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability.Meanwhile,the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers(with more contextual information)extracted by the backbone network.The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset,and it was shown that the improved PSPNet model had an Intersection-over-Union(IoU)and Pixel Accuracy(PA)of 87.42%and 95.73%,respectively,implying an improvement of 4.36%and 9.95%over the original PSPNet model.The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+and U-Net in terms of IoU,PA,computation efficiency and robustness,and showed promising performance.It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments,which provides a certain technical basis for intelligent harvesting by grape picking robots.

关键词： grape bunches semantic segmentation deep learning improved PSPNet

Development of a mobile application for identification of grapevine(Vitis vinifera L.)cultivars via deep learning

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International Journal of Agricultural and Biological Engineering 2021年第5期14卷 172-179页

作者： Yixue Liu jinya su Lei Shen Nan Lu Yulin Fang Fei Liu Yuyang Song Baofeng su College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling 712100ShaanxiChina College of Enology Northwest A&F UniversityYangling 712100ShaanxiChina College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China

Traditional vine variety identification methods usually rely on the sampling of vine leaves followed by physical,physiological,biochemical and molecular measurement,which are destructive,time-consuming,labor-intensive and require experienced grape phenotype analysts.To mitigate these problems,this study aimed to develop an application(App)running on Android client to identify the wine grape automatically and in real-time,which can help the growers to quickly obtain the variety information.Experimental results showed that all Convolutional Neural Network(CNN)classification algorithms could achieve an accuracy of over 94%for twenty-one categories on validation data,which proves the feasibility of using transfer deep learning to identify grape species in field environments.In particular,the classification model with the highest average accuracy was GoogLeNet(99.91%)with a learning rate of 0.001,mini-batch size of 32,and maximum number of epochs in 80.Testing results of the App on Android devices also confirmed these results.

关键词： deep learning mobile phone grapevine cultivar vine leaf image identification Vitis vinifera L

Finite-time disturbance rejection control for robotic manipulators based on sliding mode differentiator 25th

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Finite-time disturbance rejection control for robotic manipu...

Bayesian Calibration of Aqua Crop Model

第25届中国控制与决策会议

作者： jinya su Jun Yang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education

ISBN: (纸本)9781467355339

The continuous finite-time high-precision anti-disturbance control problem for robotic manipulators （RM） under external disturbances and parameter uncertainties is investigated in this article. Firstly, a continuous terminal sliding mode control （CTSMC） is introduced to stabilize the nominal dynamics of the RM in finite time. Secondly, a sliding mode differentiator is employed to estimate the lumped disturbances to its nominal dynamics in finite time. By feedforward compensating the estimation of the lumped disturbances, a continuous composite controller is thus obtained for the RM system. The composite controller enjoys the following superior properties: 1) it is continuous and thus no chattering phenomenon appears;2) the finite-time convergence property of the RM system under the proposed controller is guaranteed even in the presence of disturbances and uncertainties;3) the nominal control performance of the proposed method is recovered to the baseline CTSMC method in the absence of disturbances and uncertainties. The excellent properties of the proposed algorithm is demonstrated by simulation studies.

关键词： Continuous finite-time anti-disturbance control Terminal sliding mode Robotic manipulators

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Bayesian Calibration of Aqua Crop Model

Wheat Drought Assessment by Remote Sensing Imagery Using Unmanned Aerial Vehicle

第37届中国控制会议

作者： Tianxiang Zhang jinya su Cunjia Liu Wen-Hua Chen Department of Aeronautical and Automotive Engineering Loughborough University

The Aqua Crop simulation model, modelling the dynamic change of crop growth status, is an important crop management tool for quantifying crop yield response to water. To effectively simulate the soil water balance and the crop growth process, a number of system parameters and canopy state variables are inevitably adopted. As a result, certain key parameters need to be calibrated so that the Aqua Crop model can achieve a better performance of prediction for various scales of regions.This paper aims to apply Bayesian technique to calibrate the Aqua Crop model. In this approach, the prior information regarding the system parameters is expressed in the form of a uniform probability distribution. Then with the advent of output variable measurement（e.g. biomass） by remote sensing techniques, the parameter distributions are iteratively updated by using Bayesian Markov Chain Monte Carlo（MCMC） method. The calibrated system parameters are expressed by the posterior distributions and gained by distribution mean value. Finally, the Bayesian calibration is compared with the conventional optimisation based calibration in terms of biomass and canopy cover, where simulated annealing is chosen as the optimisation approach, indicating a better calibration performance can be achieved by using Bayesian methods. Consequently, it is recommended that Bayesian calibration is one promising approach to the problem of crop model calibration.

关键词： Bayesian method Aqua Crop model parameterisation data assimilation

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Wheat Drought Assessment by Remote Sensing Imagery Using Unm...

基于多结构特征提取和轨迹跟踪的车辆远光识别

第37届中国控制会议

作者： jinya su Matthew Coombes Cunjia Liu Lei Guo Wen-Hua Chen Department of Aeronautical and Automotive Engineering Loughborough University Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education School of Electrical Engineering and Automation Beihang University

This work aims at evaluating the usability of remote sensing RGB imagery by an Unmanned Aerial Vehicle（UAV） in assessing wheat drought status. A UAV survey is conducted to collect high-resolution RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. The soil moisture for different plots of the experimental filed is kept at an approximately constant level for the whole growing season in a well controlled environment, where field measurements are performed just after the UAV survey to obtain the soil water content for each plot. A machine learning based wheat drought assessment framework is proposed in this work. In the proposed framework, wheat pixels are first segmented from the soil background using the classical normalized excess green index（NEx G）. Rather than using pixel-wise classification,a pixel square of appropriate dimension is defined as the samples, based on which various features are extracted for the wheat pixels including statistical features and spectral index features. Different classification algorithms are experimented to identify a suitable one in terms of classification accuracy and computation time. It is discovered that support Vector Machine with Gaussian kernel can obtain an accuracy over 90%, which demonstrates the usefulness of RGB imagery in wheat drought assessment.

关键词： Classification Remote sensing UAV imagery Wheat drought

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电子测量与仪器学报 2018年第10期32卷 103-110页

作者：苏金亚张伟伟吴训成宋晓琳上海工程技术大学湖南大学汽车车身先进设计制造国家重点实验室

为识别夜间车辆远光,提出了车灯特征提取算法及多车灯跟踪匹配模型。首先,基于各光源成像差异,设计区分度显著的多结构特征描述子,结合随机森林判断车灯类别。其次,根据车灯帧间运动规律,提出车灯轨迹极限值拟合模型,以生成跟踪搜索区,... 详细信息

为识别夜间车辆远光,提出了车灯特征提取算法及多车灯跟踪匹配模型。首先,基于各光源成像差异,设计区分度显著的多结构特征描述子,结合随机森林判断车灯类别。其次,根据车灯帧间运动规律,提出车灯轨迹极限值拟合模型,以生成跟踪搜索区,并结合车灯轨迹预测和特征相似度完成多车灯跟踪匹配。最后,据跟踪周期中远光开启距离或持续时间验证其远近光属性。实验采集多段城区道路交通视频数据,统计实现远光识别的频数,与主观判断进行比较。结果表明,该方法能以91.2%的准确率实时识别车辆远光。

关键词：车辆远光识别多结构特征轨迹预测多目标跟踪

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基于卷积神经网络的车道线语义分割算法

电子测量与仪器学报 2018年第7期32卷 89-94页

作者：徐国晟张伟伟吴训成苏金亚郭增高上海工程技术大学汽车工程学院徐工消防安全装备有限公司

针对车道线检测环境复杂,光照变化复杂等特点,提出了一种新型车道线检测方法。首先运用可变形卷积神经网络提取特征,然后通过对白天、夜晚、雨天等复杂光照条件下的KITTI道路数据集进行联合训练,端到端获取车道线上下文信息。建立结构... 详细信息

针对车道线检测环境复杂,光照变化复杂等特点,提出了一种新型车道线检测方法。首先运用可变形卷积神经网络提取特征,然后通过对白天、夜晚、雨天等复杂光照条件下的KITTI道路数据集进行联合训练,端到端获取车道线上下文信息。建立结构化道路车道线网络模型,进而对车道线进行图像语义分割,并判断车道线类型。该模型预测车道线像素所属的场景语义类别,实现车道线实时检测。实验结果表明,该方法具有较好的准确性和实时性,在多场景结构化道路上的车道线识别率可达96.83%。

关键词：语义分割车道线特征卷积神经网络结构化道路网络模型