基于轮胎分段仿射辨识模型的车辆行驶状态估计策略研究

以车辆横向运动过程中的行驶状态精确估计为目标,提出了一种考虑轮胎非线性侧偏力学特性的行驶状态估计算法。为准确反映特殊行驶工况下车辆横向动力学行为演化规律,采用分段仿射辨识方法建立了轮胎非线性侧偏力学特性模型,进而实现整车横向动力学分段仿射模型的构建。在此基础上,基于强跟踪平方根容积卡尔曼滤波算法设计了针对系统分段仿射模型的“多模切换”行驶状态估计策略,以期当系统状态发生突变时依然能够保持良好的状态估计精度。基于CarSim和Matlab/Simulink建立了车辆行驶状态估计性能联合仿真验证平台,通过设置两种典型工况,对车辆横摆角速度和质心侧偏角的状态估计效果进行了验证。结果表明,所提出的估计算法能够实现特殊行驶工况下车辆行驶状态的高精度估计。

轮胎非线性纵滑力学特性的分段仿射辨识建模方法

针对传统轮胎力学模型因形式复杂、参数拟合困难等而不便于进行车辆系统动力学控制设计的问题,提出一种基于数据驱动的轮胎纵滑力学特性分段仿射辨识建模方法。基于平板式轮胎动态特性试验台,准确获取了反映轮胎在3124、6530、8036、9468、11760 N等5个垂向载荷,胎压880 kPa,纵向滑移率变化范围为-1~0.5,以及峰值附着系数分别为0.34和0.77的两种路面条件下的非线性纵滑力学特性试验数据。在此基础上,对试验结果进行了分析,确定了轮胎纵向力与其主要影响因素间的一般映射关系,进而引入分段仿射辨识理论完成轮胎纵滑力学特性的有效辨识。辨识主要由试验数据聚类、仿射子模型参数估计以及分解面系数矩阵求解等3个环节组成,分别采用改进的K-means、最小二乘以及模糊加权近似支持向量机等算法进行实现,最终针对低附路面和高附路面分别获取了10个仿射子模型用于拟合轮胎非线性纵滑力学特性试验数据。为验证辨识轮胎模型的精度,将模型仿真结果与试验数据进行对比。结果表明,所辨识的轮胎模型能够准确描述轮胎非线性纵滑力学特性,模型整体拟合精度高于95%。

空气悬架客车横向动力学建模和稳定性分析

针对特殊行驶工况下空气悬架客车横向动力学失稳现象的揭示问题,本文中提出一种系统非线性动力学建模和稳定性分析方法。为准确反映空气悬架客车横向动力学演化规律,建立了考虑空气弹簧和轮胎非线性力学特性的系统3自由度动力学模型。为实现复杂高维动力学系统的稳定性分析,首先基于中心流形理论对系统进行降维处理,而后对得到的约化系统进行定性性态分析,确定了系统满足鞍结分岔的充分必要条件。设置4种工况对空气悬架客车横向动力学系统在平衡点处的分岔行为进行了相平面分析,掌握了空气悬架客车在不同路面条件下随车速及前轮转角变化的横向动力学失稳演化规律,最后基于整车动力学进行仿真验证表明解析分析结果与仿真分析结果一致,证明本文提出的非线性稳定性分析方法有效可行。

An Adaptive Nonsingular Fast Terminal Sliding Mode Control for Yaw Stability Control of Bus Based on STI Tire Model

Due to the bus characteristics of large quality,high center of gravity and narrow wheelbase,the research of its yaw stability control(YSC)system has become the focus in the field of vehicle system dynamics.However,the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research.In this paper,a novel adaptive nonsingular fast terminal sliding mode(ANFTSM)control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads.Firstly,the STI(Systems Technologies Inc.)tire model,which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force,is established based on experimental data and firstly adopted in the bus YSC system design.On this basis,a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM,which has the merits of fast transient response,finite time convergence and high robustness against uncertainties and external disturbances,is designed.Thirdly,to solve the optimal allocation problem of the tire forces,whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire,the robust least-squares allocation method is adopted.To verify the feasibility,effectiveness and practicality of the proposed bus YSC approach,the TruckSim-Simulink co-simulation results are finally provided.The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved.This research proposes a more effective design method for bus YSC system based on a more accurate tire model.

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy:A review

Upper gastrointestinal(GI)cancers are the leading cause of cancer-related deaths worldwide.Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy.However,unlike GI cancers,precancerous lesions in the upper GI tract can be subtle and difficult to detect.Artificial intelligence techniques,especially deep learning algorithms with convolutional neural networks,might help endoscopists identify the precancerous lesions and reduce interobserver variability.In this review,a systematic literature search was undertaken of the Web of Science,PubMed,Cochrane Library and Embase,with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract.The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized.The challenges and recommendations targeting this field are comprehensively analyzed for future research.

Sampled-Data Asynchronous Fuzzy Output Feedback Control for Active Suspension Systems in Restricted Frequency Domain

This paper proposes a novel sampled-data asynchronous fuzzy output feedback control approach for active suspension systems in restricted frequency domain.In order to better investigate uncertain suspension dynamics,the sampleddata Takagi-Sugeno(T-S)fuzzy half-car active suspension(HCAS)system is considered,which is further modelled as a continuous system with an input delay.Firstly,considering that the fuzzy system and the fuzzy controller cannot share the identical premises due to the existence of input delay,a reconstructed method is employed to synchronize the time scales of membership functions between the fuzzy controller and the fuzzy system.Secondly,since external disturbances often belong to a restricted frequency range,a finite frequency control criterion is presented for control synthesis to reduce conservatism.Thirdly,given a full information of state variables is hardly available in practical suspension systems,a two-stage method is proposed to calculate the static output feedback control gains.Moreover,an iterative algorithm is proposed to compute the optimum solution.Finally,numerical simulations verify the effectiveness of the proposed controllers.

Static-Output-Feedback Based Robust Fuzzy Wheelbase Preview Control for Uncertain Active Suspensions With Time Delay and Finite Frequency Constraint

This paper proposes a static-output-feedback based robust fuzzy wheelbase preview control algorithm for uncertain active suspensions with time delay and finite frequency constraint.Firstly,a Takagi-Sugeno(T-S)fuzzy augmented model is established to formulate the half-car active suspension system with consideration of time delay,sprung mass variation and wheelbase preview information.Secondly,in view of the resonation between human’s organs and vertical vibrations in the frequency range of 4–8 Hz,a finite frequency control criterion in terms of H∞norm is developed to improve ride comfort.Meanwhile,other mechanical constraints are also considered and satisfied via generalized H2 norm.Thirdly,in order to maintain the feasibility of the controller despite of some state variables are not online-measured,a two stage approach is adopted to derive a static output feedback controller.Finally,numerical simulation results illustrate the excellent performance of the proposed controller.

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment:A minireview

Gastrointestinal(GI)cancers are the major cause of cancer-related mortality globally.Medical imaging is an important auxiliary means for the diagnosis,assessment and prognostic prediction of GI cancers.Radiomics is an emerging and effective technology to decipher the encoded information within medical images,and traditional machine learning is the most commonly used tool.Recent advances in deep learning technology have further promoted the development of radiomics.In the field of GI cancer,although there are several surveys on radiomics,there is no specific review on the application of deep-learning-based radiomics(DLR).In this review,a search was conducted on Web of Science,PubMed,and Google Scholar with an emphasis on the application of DLR for GI cancers,including esophageal,gastric,liver,pancreatic,and colorectal cancers.Besides,the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.