Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Precision Motion Control of Hydraulic Actuator Using Adaptive Back-Stepping Sliding Mode Controller

Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances.These unfavorable factors adversely affect the control performance of the hydraulic actuator.Although various control methods have been employed to improve the tracking precision of the dynamic system,optimizing and adjusting control gain to mitigate the hydraulic actuator model uncertainties remains elusive.This study presents an adaptive back-stepping sliding mode controller(ABSMC)to enhance the trajectory tracking precision,where the virtual control law is constructed to replace the position error.The adaptive control theory is introduced in back-stepping controller design to compensate for the model uncertainties and time-varying disturbances.Based on Lyapunov theory,the finite-time convergence of the position tracking errors is proved.Furthermore,the effectiveness of the developed control scheme is conducted via extensive comparative experiments.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

On-Chip Micro Temperature Controllers Based on Freestanding Thermoelectric Nano Films for Low-Power Electronics

Multidimensional integration and multifunctional com-ponent assembly have been greatly explored in recent years to extend Moore’s Law of modern microelectronics.However,this inevitably exac-erbates the inhomogeneity of temperature distribution in microsystems,making precise temperature control for electronic components extremely challenging.Herein,we report an on-chip micro temperature controller including a pair of thermoelectric legs with a total area of 50×50μm^(2),which are fabricated from dense and flat freestanding Bi2Te3-based ther-moelectric nano films deposited on a newly developed nano graphene oxide membrane substrate.Its tunable equivalent thermal resistance is controlled by electrical currents to achieve energy-efficient temperature control for low-power electronics.A large cooling temperature difference of 44.5 K at 380 K is achieved with a power consumption of only 445μW,resulting in an ultrahigh temperature control capability over 100 K mW^(-1).Moreover,an ultra-fast cooling rate exceeding 2000 K s^(-1) and excellent reliability of up to 1 million cycles are observed.Our proposed on-chip temperature controller is expected to enable further miniaturization and multifunctional integration on a single chip for microelectronics.

Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative(NPD)controller and a genetic algorithm,in which the proportional-derivative(PD)parameters are updated online based on the tracking error and the preset error threshold.In addition,the genetic algorithm is employed to adaptively select initial controller parameters,contributing to system stability and improved control accuracy.The proposed controller is basic in design yet simple to implement.The ANPD controller has the advantage of being computationally lightweight and providing high robustness against external forces.The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov theory,providing theoretical assurance of its robustness.Simulations and experimental results show that the TWSB robot with the proposed ANPD controller achieves quick balance and tracks target values with very small errors,demonstrating the effectiveness and performance of the proposed controller.The proposed ANPD controller demonstrates significant improvements in balancing and tracking performance for two-wheeled self-balancing robots,which has great applicability in the field of robot control systems.This represents a promising solution for applications requiring precise and stable motion control under varying external conditions.

Contact detumbling toward a nutating target through deformable effectors and prescribed performance controller

Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.

Hierarchical Controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications.Based on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis problem.Second,we develop a dynamic quantization based approach to verify the realization of the accepting path.The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design.Third,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy.Both abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational complexity.Since each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods.Finally,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

基于PLC构建电力巡检机器人自动化控制系统

该文对电力巡检机器人的自动化控制系统进行以PLC为基础的设计,提升电力调度的效率与安全性。基于PLC构建电力巡检机器人自动化控制系统总体架构,通过上位机接收操纵台传递的操作者信息以及下位机传递的温度、角度等状态信息,通过OPC通信程序将指令下发至下位机,下位机依据上位机下达指令,通过PID驱动控制器完成电力巡检机器人运动控制,利用模糊C均值聚类方法判别电力巡检机器人故障,将结果实时传送到上位机操纵台的显示器,及时反馈机器人的工作状态。实验结果表明,该系统可以控制机器人完成电力巡检,该系统应用后可有效检测机器人故障,使其具备优秀的避障能力,可实现上下位机实时通信。

电力巡检机器人路径规划方法综述

随着电力系统规模和复杂性的不断增加,传统的人工巡检方式已很难满足当下电力巡检要求。巡检机器人因具有安全性、可靠性和智能性已被逐步应用到电力巡检中。机器人在运行过程中通过提前预测、自主决策或智能导航的方式完成全局遍历与避障。电力巡检的核心任务是规划出一条安全且最优的运行轨迹。系统地分析和梳理了巡检机器人巡检过程中的各类方法;阐述了在变电站和输电线路等不同场景中算法的应用;综述了各类路径规划算法的研究现状和改进方法。在此基础上,对未来电力巡检机器人路径规划的研究和发展方向做出了展望。

CT引导下经皮肺穿刺活检术并发症发生的影响因素分析

背景与目的 计算机断层扫描引导下经皮肺穿刺活检术(computed tomography guided percutaneous lung biopsy,CT-PLB)是目前临床广泛应用的肺部病变诊断方式,但其为有创检查,最常见的并发症为气胸和肺出血,严重时可危及生命。本研究旨在分析影响CT-PLB不同并发症发生的独立危险因素,以降低并发症发生率。方法回顾性分析2018年5月至2019年12月于我院行CT-PLB且临床资料完整的605例患者的资料。依病变位置分为胸膜下组和非胸膜下组,根据并发症分为气胸组、肺出血组、气胸合并肺出血组及无气胸/肺出血组,计算各并发症的发生率。分析影响不同并发症发生的危险因素及各并发症的独立危险因素。结果气胸发生率为34.1%,肺出血发生率为28.1%,同时发生气胸及肺出血63例,发生率为10.8%。影响胸膜下组气胸发生的独立危险因素为病变大小(P=0.002)。影响非胸膜下组气胸发生的独立危险因素为穿刺针走行区域平扫CT值(P=0.035)、穿刺针经过肺组织长度(P=0.003)、穿刺针经过胸壁厚度(P=0.020);影响非胸膜下组肺出血发生的独立危险因素为穿刺针经过肺组织长度(P<0.001)、穿刺针走行区域△CT值(P=0.001)、病变大小(P=0.034)及患者体位(P=0.014)。影响气胸、肺出血同时发生的独立危险因素为穿刺针经过肺组织长度(P<0.001)、穿刺针走行区域△CT值(P<0.001)。结论 CT-PLB是一种安全、有效的诊断方式,对肺部占位性病变具有较高的诊断价值,选择合适的穿刺方案可减少气胸、肺出血等并发症,提高诊疗效率。

新疆某健康体检机构重要异常结果分析与随访结果研究

目的研究新疆某健康体检机构重要异常结果人群分布特征及随访结果。方法选取2022年在新疆医科大学健康管理中心参加健康体检的受检者65045例,检出4665例有重要异常结果的受检者,制定重要异常结果A类B类随访规则,进行电话通知及1月、6月随访,使用随访信息系统进行提醒及表单记录。结果4665例重要异常结果受检者中A类重要异常结果447例(9.58%),B类4218例(90.42%);男性2815例(60.34%)、女性1850例(39.66%),男性高于女性(P<0.05);年龄与重要异常结果存在相关性(P<0.05);41~50岁(1114例)、51~60岁(1211例)这两个年龄段重要段异常结果人数占比高。有效通知4457例(95.54%),通知后有效随访4397例(98.65%),失访60例(1.35%);电话随访后就诊2118例(48.17%),未就诊2279例(51.83%);未就诊男性1562(58.57%)高于女性717(41.45%),性别与是否就诊有统计学意义(P<0.05)。就诊后非肿瘤性疾病1194例(56.40%),恶性肿瘤性疾病97例(4.5%),未明确827例(39.1%)。结论重要异常结果男性高于女性,随年龄增长重要异常结果发生率增高。随访路径实施中通知及随访成功率还应提高。受检者对重要异常结果指标理解不够及重视程度不高(60.44%),未按随访建议进行及时就诊,需加强健康宣教提高其健康意识,督促其重视及时治疗,对提高疾病“早筛查,早评估,早干预”有重要意义。

健康体检人群甲状腺结节检出情况及发病的影响因素

目的调查健康体检人群甲状腺结节(TNs)检出情况,并分析其影响因素,为早期防治TNs提供参考依据。方法选取2022年1月1日至2023年4月30日15393例健康体检人群,均进行多普勒超声检查,按年龄分层及性别统计TNs患病率,通过单因素及多因素logistic回归方程分析TNs影响因素。结果15393例健康体检者,共检查出5001例TNs,检出率为32.49%(5001/15393)。男性不同年龄组TNs检出率均低于女性(P<0.05)。健康人群随着年龄增长TNs检出率呈升高趋势(P<0.05)。两组体重指数、收缩压、舒张压、空腹血糖(FPG)、总胆固醇(TC)、低密度脂蛋白胆固醇(LDL-C)、性别、年龄、吸烟、饮酒、高脂血症、糖尿病、代谢综合征、脂肪肝、焦虑、抑郁比较,差异有统计学意义(P<0.05)。年龄、性别、糖尿病、脂代谢综合征、脂肪肝、焦虑、抑郁均为TNs独立影响因素(P<0.05)。结论健康体检人群TNs检出率较高,主要与年龄、性别、糖尿病、脂代谢综合征、脂肪肝、焦虑、抑郁等因素有关,临床应在健康体检中普及甲状腺彩超检查项目,以实现对甲状腺疾病的尽早防治。

高速铁路动静态轨检数据里程对齐与误差修正

轨道几何动、静检测数据间的精确匹配对探明高速铁路线路服役状态和制定准确可靠的养护维修策略具有关键作用。针对动静里程匹配算法研究较少的现状,提出利用动、静态实测数据波形匹配,建立基于互相关函数与动态时间规划相结合的两阶段修正算法,并以距离误差作为评价指标。结合某高铁线路轨道几何检测数据的案例分析,以静态检测数据作为参考基准,对动态检测数据进行里程误差评估与修正。结果表明,两阶段算法修正效果显著,累积距离误差降幅超过93%,修正后的动、静态检测数据严格对齐保证了动态检测数据的可靠性与有效性。

医疗器械检验报告审核和质量管理探讨

目的:医疗器械具有复杂性、特殊性、专业性、涉及门类广等特点,面对医疗器械开放自检及检验市场竞争日益加剧的形势,如何提升检验能力,适应产业的迅速发展,确保检验结果的科学性、公正性、权威性,是各医疗器械检验机构面临的共同挑战。方法:从医疗器械检验面临的挑战、法律法规要求、质量管理体系关注要点等方面进行阐述。结果:通过对报告审核中发现的问题,提出质量改进措施及建议。结论:医疗器械检验机构应持续提升检验能力,发展特色优势领域,积极应对医疗器械快速发展的需要及检验新形势,提质增效,应在医疗器械的质量检验工作中,引入风险管理理念并加强过程控制,从人、机、料、法、环等方面构建起全方位的保障体系,提高检验质量和效率,助力医疗器械高质量发展。

应用综合干预措施对促进住院患者抗菌药物治疗前病原学送检的效果评价

目的探讨应用综合干预措施对促进住院患者抗菌药物治疗前病原学送检的效果。方法选取2020年1月—2021年12月某三甲医院治疗性使用抗菌药物的住院患者为研究对象,2021年1月开始实施综合干预措施,比较干预前组(2020年1—12月)和干预后组(2021年1—12月)抗菌药物治疗前病原学送检率、送检分类和重点监管科室送检率。结果共纳入治疗性使用抗菌药物的住院患者10239例,其中干预前组4526例,干预后组5713例。干预后的抗菌药物治疗前、限制级抗菌药物治疗前、特殊级抗菌药物治疗前病原学送检率分别为94.56%、94.72%、96.03%,高于干预前的83.74%、84.47%、84.95%,差异均有统计学意义(均P<0.05)。干预后的指向性病原体送检率为64.87%,高于干预前的28.04%,差异均有统计学意义(均P<0.05)。干预后的重症医学科、呼吸与危重症医学科、儿科、神经外科和普通外科治疗性使用抗菌药物病原学送检率分别为93.20%、91.17%、92.20%、94.12%、91.15%,高于干预前的85.00%、82.19%、83.20%、83.33%、83.03%,差异均有统计学意义(均P<0.05)。结论应用综合干预措施可提高住院患者抗菌药物治疗前病原学送检率,应密切关注医院感染诊断相关病原学送检指标和无菌体液送检指标。

基于深度学习的巡检机器人避障轨迹自动控制系统设计

为了提高巡检机器人在复杂环境下的避障能力,使机器人能够安全地完成巡检任务,设计基于深度学习的巡检机器人避障轨迹自动控制系统;设计由CCD传感器、信号处理芯片等设备组成的工业智能视觉CCD相机,基于FPGA和USB2.0的视频采集卡传输采集数据,完成硬件部分的设计;在软件设计中,对采集的图像实施目标分割、双目目标匹配等预处理,通过对摄像头实施双目视觉标定获取障碍物空间位置三维信息,基于深度学习中的CRNN设计机器人自主避障规划网络模型,并设计模糊轨迹控制器,实现避障中的轨迹自动控制;系统测试结果表明,设计系统最终成功避开了3个动态障碍物,最大轨迹控制误差的最大值为1.45°,最小轨迹控制误差的最大值为0.62°,动态避障巡检速度始终在3.5 m/s左右,表现出了精准而稳定的轨迹控制效果。

基于图像感知的输电线路智能巡检综述

输电线路是电网建设的重要组成部分,定期巡检对于确保输电线路的稳定运行至关重要。文中围绕输电线路智能巡检需求,首先梳理了输电线路不同的巡检方法,介绍了智能巡检中所需的系统框架设备以及智能算法;重点总结了基于传统图像处理和深度学习的智能巡检方法;其次针对输电线路中存在的各种不同故障总结了相关的智能巡检方法,并且通过评价指标进行了对比分析;最后梳理了研究难点,对基于深度学习方法的输电线路巡检发展趋势进行了展望,所提出和总结的输电线路智能巡检方法具有多方面的指导意义。