Exploring the effect of fingertip aero-haptic feedforward cues in directing eyes-free target acquisition in VR

Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

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.

Application Effect of Feedforward Control in Outpatient Blood Specimen Management

Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital's outpatient clinic from January 2021 to April 2021 were selected as study subjects and divided into 600 cases in the control group and 600 cases in the observation group.The two groups of patients were compared in terms of their satisfaction with the staff,the efficiency of the nurses and the quality of nursing care,turnaround time before specimen analysis,the rejection rate of the blood specimens,and the time of result reporting.Results:After the implementation of feedforward control,patients'satisfaction with staff,nurses work efficiency and quality of care,turnaround time before specimen analysis,specimen rejection rate,and result reporting time in the observation group were significantly higher than those in the control group(P<0.05).Conclusion:The application of feedforward control in the management of outpatient blood specimens has significant effect,which effectively improves patients'satisfaction,enhances the efficiency of nurses and the quality of nursing care,shortens the turnaround time of specimens before analysis and the reporting time of results,and reduces the rejection rate of specimens.

Matrix pencil based robust control for feedforward systems with event-triggered communications and sensor/actuator faults

In this paper we address the issue of output-feedback robust control for a class of feedforward nonlinear systems.Essentially different from the related literature,the feedback/input signals are corrupted by additive noises and can only be transmitted intermittently due to the consideration of event-triggered communications,which bring new challenges to the control design.With the aid of matrix pencil based design procedures,regulating the output to near zero is globally solved by a non-conservative dynamic low-gain controller which requires only an a priori information on the upper-bound of the growth rate of nonlinearities.Theoretical analysis shows that the closed-loop system is input-to-state stable with respect to the sampled errors and additive noise.In particular,the observer and controller designs have a dual architecture with a single dynamic scaling parameter whose update law can be obtained by calculating the generalized eigenvalues of matrix pencils offline,which has an advantage in the sense of improving the system convergence rate.

A Real-time Look-ahead Trajectory Planning Methodology for Multi Small Line Segments Path

When a robot is required to machine a complex curved workpiece with high precision and speed,the tool path is typically dispersed into a series of points and transmitted to the robot.The conventional trajectory planning method requires frequent starts and stops at each dispersed point to complete the task.This method not only reduces precision but also causes damage to the motors and robot.A real-time look-ahead algorithm is proposed in this paper to improve precision and minimize damage.The proposed algorithm includes a path-smoothing algorithm,a trajectory planning method,and a bidirectional scanning module.The path-smoothing method inserts a quintic Bezier curve between small adjacent line segments to achieve G^(2)continuity at the junctions.The trajectory planning method utilizes a quartic polynomial and a double-quartic polynomial that can achieve a constant velocity at the velocity limitation.The bidirectional scanning module calculates the velocity at each trajectory planning segment point,simplifying calculation complexity and can be run in real time.The feasibility of the proposed algorithm is verified through simulations and experiments,which can be run in real time.In addition,high machining precision can be achieved by adjusting the relevant parameters.

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

单相级联型多电平变换器直流纹波电压分析及抑制策略

级联H桥型多电平变换器采用移相变压器+整流供电时具有功率因数高、输出电压谐波含量低等优点,在大功率工业领域范围内得到了广泛应用。传统的调制算法都以假设直流母线电压恒定为前提,然而在直流侧会出现低频纹波并被调制和传输到负载,导致输出电压波形畸变。目的为了探讨级联H桥单元供电侧存在大量直流纹波电压导致输出负载电压波形畸变的问题,方法本文首先采用双重傅里叶理论分析直流纹波电压,在此基础上引入一种直流纹波前馈补偿策略,通过测量每个级联H桥的直流侧实际输入电压除以电压基准值并与传统载波信号相乘,进而与调制电压进行载波移相调制;同时采用矢量控制,通过FFT变换虚拟出无功电压,以获得更快的收敛速度、更高的稳定性和更小的振荡。结果仿真和实验结果表明,所提调制算法和控制策略可有效抑制负载电压中的低次谐波,减小输出电压波形畸变,实现逆变电源的快速动态响应和无稳态误差控制。结论实验结果验证了本文所采用的调制策略和控制策略的可行性与合理性。

基于前馈—模糊PID策略的风洞控制器应用

针对2.4m跨声速风洞因强耦合、强非线性、大滞后等特性无法获得其精确数学模型,使用经典PID算法控制精度无法满足试验要求的问题,设计基于前馈—模糊PID策略的复合控制器,并应用于风洞试验。试验结果表明,该控制器的应用对于流场控制精度提高有良好的效果。

一种适应卫星姿态侧摆机动的混合轮系控制方法

针对高稳定度轮控大型卫星因动量轮力矩小而难以满足侧摆成像任务需求问题,开展了混合轮系配置方案及控制方法研究。首先,提出常规轮系配置卫星滚动轴方向串联大力矩飞轮的混合配置方案,并设计飞轮开环前馈与常规动量轮闭环反馈相结合的控制策略;其次,设计以容许角动量最大量为权重的飞轮组前馈力矩分配律和具有角动量反馈的飞轮力矩跟踪控制方法,实现飞轮力矩合理分配及其模型不确定性导致输出力矩偏差抑制;然后,设计估计器对星体内外扰动力矩进行估计与补偿,以确保卫星高稳定度性能实现;最后,给出在轨应用情况,验证所提出方案及方法的有效性。在轨应用表明,采用该控制方法,有效缩短了整星侧摆稳定时间达100 s以上,且非机动期间姿态稳定度优于3.75×10-5(°)/s。

燃料电池系统建模与供气系统控制方法

为了研究燃料电池系统的特性以及空气系统对燃料电池特性的影响,利用Simulink仿真软件对燃料电池系统进行建模,模型包括电压模型、空压机模型、阴极模型、阳极模型.分别通过前馈比例-积分-微分(proportional-integral-differential, PID)和模糊PID对燃料电池空气系统进行控制.结果表明:在负载电流变化时,前馈PID和模糊PID都能够使过氧比达到设定的常数值2.0附近,但模糊PID比前馈PID响应更快,且模糊PID控制下的燃料电池输出功率波动较小,燃料电池系统更加稳定.

基于直驱并联机构的六自由度振动校准系统研究

直驱并联机构六自由度振动校准系统可以实现高精度和多自由度的运动模拟和校准,具有良好的应用前景。为了解决平台驱动关节滑块位置跟踪精度低的问题,对该机构进行动力学力矩前馈补偿分析。利用虚功原理建立该机构动力学模型,提出一种基本伺服算法与动力学前馈补偿相结合的控制策略。在实验样机上进行力矩前馈补偿,实验结果表明,在开放伺服程序增加力矩前馈补偿后,可以降低该机构在运动时的跟随误差,进行不同幅值单自由度正弦运动时,跟随误差分别下降40.32%、39.04%、43.24%、48.19%。并对振动台进行了性能检测,在平台基础上,搭建激光测量系统和数据采集模块,进行传感器校准和性能分析,平台横向振动比较小,谐波失真度均小于2%,平台具有较好的稳定性和性能。

前馈控制理论护理在乙型肝炎肝硬化患者遵医行为及生存质量中的应用效果

目的探究前馈控制理论护理对乙型肝炎肝硬化患者遵医行为及生存质量的应用效果。方法选取2022年2月至2023年10月南通大学附属南通第三医院的110例乙型肝炎肝硬化患者作为研究对象,按照随机数字表法将其分为对照组和观察组,各55例。对照组采用常规护理,观察组采用前馈控制理论护理,干预2个月后比较两组遵医行为、心理状态评分及生存质量。结果干预后,两组遵医行为评分指标高于干预前,且观察组高于对照组,差异有统计学意义(P<0.05);干预后,两组90项症状自评量表评分低于干预前,且观察组低于对照组,差异有统计学意义(P<0.05)。干预后,两组肝病生存质量调查表评分高于干预前,且观察组高于对照组,差异有统计学意义(P<0.05)。结论乙型肝炎肝硬化患者护理中接受前馈控制护理,提高遵医行为,改善患者心理状态的同时,患者生存质量有所提升,预后效果更佳,可广泛应用于临床中。

电液振动台加速度时域波形自适应控制策略

针对电液振动台加速度时域波形复现精度低的问题,提出了一种基于内环前馈逆补偿和外环非线性滤波的在线自适应控制策略。首先,构建了电液振动台伺服驱动机构模型,采用三状态控制策略获得了初步加速度控制性能;其次,基于多新息遗忘梯度算法辨识了三状态控制下系统参数化模型,利用零幅值跟踪技术构建了内环前馈逆补偿策略,拓展了系统频宽,降低了相位滞后误差;然后,引入了外环非线性Volterra自适应滤波算法,抑制了电液振动台模型参数偏差、非线性动态特性和不确定性扰动的影响;最后,在搭建的水平单轴电液振动台上开展了时域波形复现对比实验。结果表明,提出的自适应控制策略与传统策略相比具有更优越的加速度时域波形复现精度。

孤独症者的预测编码缺陷:前馈联结异常还是反馈联结异常?

依据预测编码理论,研究者提出预测缺陷是孤独症谱系障碍(Autism Spectrum Disorder,ASD)个体感知运动、认知学习和社交言语等多领域缺陷的基础,即孤独症的预测缺陷假说(Predictive Impairment inAutism,PIA)。在PIA中,研究者基于贝叶斯和层级性推理分别提出“低先验”和“高且不灵活的预测误差精度”两个假说,然而上述假说并未得到一致证据支持。ASD个体在不同领域中不同先验的相对权重并非普遍降低,而是具有广泛的任务或情境敏感性。关于ASD个体是否具备基于环境波动调节预测误差精度的能力,也尚存分歧。此外,关于其潜在机制,是神经调节系统异常导致的自下而上前馈联结异常,还是预测脑区功能异常导致的自上而下反馈联结异常,尚无定论。由此可见,虽然该理论为ASD提供了统一的解释框架,但仍需更多研究证据进行修正和完善,以期为早期筛查和诊断、治疗和教育实践提供指导。

基于角频率暂态前馈的VSG控制策略

针对虚拟同步发电机(VSG)并网时,输出功率的稳态性能与动态性能之间存在耦合,无法同时满足抑制有功振荡和电网一次调频的需求,提出基于角频率暂态前馈的控制策略,通过补偿输出功率,减小Δω,进而减小振荡过程。该控制策略可以有效抑制有功振荡,并且实现了稳态性能与动态性能间的解耦,不会影响一次调频特性,同时提高了响应速度。首先建立VSG有功闭环小信号模型,分析了输出功率稳态性能与动态性能之间的矛盾,并根据同步发电机功角特性分析了有功振荡机理;在此基础上提出了基于角频率暂态前馈的控制策略,利用闭环零极点与Bode图分析了所提控制策略对系统的影响;最后通过仿真和RT-LAB半实物平台验证了所提控制策略的可行性与优越性。

混沌自适应非洲秃鹫优化算法训练多层感知器

针对训练多层感知器(MLP)时,算法对初始值敏感、易陷入局部最优和收敛速度慢等问题,对新型启发式算法非洲秃鹫优化算法提出改进算法IAVOA。在初始化种群时引入Logistic混沌映射,增加种群的多样性;对最优秃鹫和次优秃鹫增加自适应权重系数,自动调整这两类秃鹫对普通秃鹫的引导作用;IAVOA用于MLP的训练,采用均方误差的平均值作为适应度函数寻找MLP的连接权重和偏差的最佳组合。选取4个不同复杂度的分类数据集,比较IAVOA算法与现有启发式算法对MLP训练后,MLP对数据分类的性能,仿真结果表明,IAVOA算法训练的MLP在数据分类准确率、全局搜索能力、收敛速度和稳定性方面均具有良好的性能。

前馈控制理论下肠内营养护理模式在脓毒症患者中的应用

目的 探讨前馈控制理论下肠内营养护理模式在脓毒症患者中的应用效果。方法 通过便利抽样法选取2019年1月至2022年12月南通市第三人民医院住院治疗的脓毒症患者200例,以随机数字表法将其分为对照组和试验组,各100例。对照组采用常规肠内营养护理,试验组在对照组的基础上行前馈控制理论下肠内营养护理。比较护理前后两组机体营养状态、炎性因子水平及病情状态恢复情况。结果 护理后,试验组腹泻、腹胀、呕吐、胃潴留总不良反应发生率低于对照组(P<0.05);护理后,两组白蛋白、前白蛋白、血红蛋白均高于护理前,且试验组高于对照组(P<0.05);两组急性生理学和慢性健康状况Ⅱ评分均低于干预前,且试验组低于对照组(P<0.05)。结论 前馈控制理论下肠内营养护理模式可提升脓毒症患者的营养状态,降低炎症因子水平,促进急性病情和慢性病情的恢复。

高精度液体注配料控制方法研究

为提高香精研发过程中配方试制精度,保证配方生产一致性,提出一种液体注配料控制方法。设计撞针行程可调节注料阀,通过常开和单点双模式注料以适应配料质量要求。常开模式采用顺馈+模糊PID控制;单点质量模式依据供给压力和撞针行程对单点质量的影响,建立单点质量控制模型。并分别进行仿真验证及模型优化。试验结果表明,当撞针行程和供给压力分别为0.1 mm和0.3 MPa时,单点质量为1.8 mg,配方试制过程中,常开控制偏差在5%以内,注料整体偏差在0.2%以内,重复精度<0.2%,满足实验室配方试制研发需求。研究对保证液体注配料精度具有重要意义。