Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method.Through a series of novel design concepts,including the integration of a detecting circuit and an analog-to-digital converter,a miniaturized functional electrical stimulation circuit technique,a low-power super-regeneration chip for wireless receiving,and two wearable armbands,a prototype system has been established with reduced size,power,and overall cost.Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects,the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy.Test results showed that wrist flexion/extension,hand grasp,and finger extension could be reproduced with high accuracy and low latency.This system can build a bridge of information transmission between healthy limbs and paralyzed limbs,effectively improve voluntary participation of hemiplegic patients,and elevate efficiency of rehabilitation training.

Simulation of optical and electrical synaptic functions in MoS_(2)/α-In_(2)Se_(3) heterojunction memtransistors

Memtransistors combine memristors and field-effect transistors, which can introduce multi-port control and have significant applications for enriching storage methods. In this paper, multilayer α-In2Se3and MoS2were transferred to the substrate by the mechanical exfoliation method, then a heterojunction MoS_(2)/α-In_(2)Se_(3) memtransistor was prepared. Neural synaptic simulations were performed using electrical and optical pulses as input signals. Through measurements, such as excitatory/inhibitory post-synaptic current(EPSC/IPSC), long-term potentiation/depression(LTP/LTD), and paired-pulse facilitation/depression(PPF/PPD), it can be found that the fabricated device could simulate various functions of neural synapses well, and could work as an electronic synapse in artificial neural networks, proposing a possible solution for neuromorphic storage and computation.

Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying Delays

An observer-based adaptive iterative learning control （AILC） scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality （LMI） method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative （PID） feedback term in time domain. The learning law of unknown constant parameter is differential-difference-type, and the learning law of unknown time-varying parameter is difference-type. It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized. By constructing a Lyapunov-Krasovskii-like composite energy function （CEF）, we prove the boundedness of all closed-loop signals and the convergence of tracking error. A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.

An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance.To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control(ILC), a new boundary layer function is proposed by employing MittagLeffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function(CEF)containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

Design and application of electric oil pump in automatic transmission for efficiency improvement and start–stop function

For the purpose of improving efficiency and realizing start–stop function, an electric oil pump(EOP) is integrated into an 8-speed automatic transmission(AT). A mathematical model is built to calculate the transmission power loss and the hydraulic system leakage. Based on this model, a flow-based control strategy is developed for EOP to satisfy the system flow requirement. This control strategy is verified through the forward driving simulation. The results indicate that there is a best combination for the size of mechanical oil pump(MOP) and EOP in terms of minimum energy consumption. In order to get a quick and smooth starting process, control strategies of the EOP and the on-coming clutch are proposed. The test environment on a prototype vehicle is built to verify the feasibility of the integrated EOP and its control strategies. The results show that the selected EOP can satisfy the flow requirement and a quick and smooth starting performance is achieved in the start–stop function. This research has a high value for the forward design of EOP in automatic transmissions with respect to efficiency improvement and start–stop function.

Additive Fault Tolerant Control Applied to Delayed Singularly Perturbed System

The additive fault tolerant control (FTC) for delayed system is studied in this work. To design the additive control, two steps are necessary;the first one is the estimation of the sensor fault amplitude using a Luenberger observer with delay, and the second one consists to generate the additive fault tolerant control law and to add it to the nominal control of delayed system. The additive control law must be in function of fault term, then, in the absence of fault the expression of additive control equal to zero. The generation of nominal control law consist to determinate the state feedback gain by using the Lambert W method. Around all these control tools, we propose an extension of the additive FTC to delayed singularly perturbed systems (SPS). So, this extension consists to decompose the delayed SPS in two parts: delayed slow subsystem (delayed SS) and fast subsystem (FS) without time delay. Next, we consider that the delayed SPS is affected at its steady-state, and we apply the principal of FTC to the delayed SS and finally we combine them with the feedback gain control of FS by using the principal of composite control.

Analysis of Control Characteristic for the Drive System of Electric Transit Bus

The drive control system of the permanent magnetic direct current motor with the enhanced magnetism windings used in the electric transit bus is developed. The mathematics model of the drive control system for this motor is established. The new mode that the added exiting magnetism field could be weakened and the speed of the motor could be controlled automatically is proposed and realized. The method of root locus design is applied to analyze the acceleration control characteristic. The results of simulation show that the new drive motor control system has extraordinary response characteristic and adjustable performance. Experiments of vehicle running show that the drive control system's antijamming ability is strong and the adjustable performance is fast and smooth, it can meet the demand of power characteristic very well.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

A novel robust adaptive controller for EAF electrode regulator system based on approximate model method

The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.

结构-功能一体化异种复合材料壁板优化与验证

基于航空壁板结构承载-电气功能的一体化需求,对适用于智能蒙皮的异种复合材料壁板开展了优化设计与试验验证。通过在高模量碳纤维蒙皮的外侧增加玻璃纤维层,进行了以碳纤维-玻璃纤维-金属结构单元阵列-玻璃纤维复合顺序的壁板承载-电气功能一体化设计。针对异种复合材料结构的铺层优化需要考虑共胶接的分区域及铺层连续性的工艺要求,发展了基于丢层序列的优化方法,并对于加筋壁板的几何外形、筋条数量、筋条几何参数以及筋条与蒙皮的铺层角度进行了优化设计。通过压缩稳定性试验验证了该结构在承载能力和稳定性方面的收益,明晰了其压缩载荷下的屈曲模态及后屈曲失效机理。

基于转鼓转速传感的中低速新能源汽车联合制动系统控制方法

中低速新能源汽车是常见的交通工具,但是其制动控制存在响应时间长、控制效果差的问题,设计一种基于转鼓转速传感的中低速新能源汽车联合制动系统控制方法。首先,构建基于转鼓转速传感的中低速新能源汽车动力学方程,采集传感信息,然后,考虑开环控制容易导致控制系统的稳定性和鲁棒性差的问题,基于转鼓转速传感的联合制动系统传递函数推导出开环、闭环双传递函数,最后,根据系统传递函数,设计PID控制器的参数,通过PID控制器输出电-液联合制动控制信号,实现汽车联合制动系统控制。实验结果表明:所提方法的速度-位移曲线与现实速度-位移曲线基本一致,加速误差在-0.5 m/s2~0.5 m/s2范围内,平均制动控制响应时间为0.075 s,具有良好的制动控制能力。

考虑道路识别的四驱电动汽车再生制动策略

为了充分利用路面附着条件分配再生制动力,提高制动能量回收率,根据双电动机四驱结构电动汽车复合制动系统的特点,对其制动能量回收控制策略进行了研究.分析了驱动轮与地面附着特性,设计了道路识别器来计算每个轮胎与当前路面之间的峰值附着系数,并使用模糊控制策略确定每个车轮与8种道路的滑移率和附着利用率的相似输入.根据双电动机外部特性的制动力分配关系,提出了使更多制动能量回馈给电池的策略.结果表明:制动强度为0.3时,能量回收率为65.55%,具有较高的回收效率.

乘用车双电机冗余电动助力转向系统研究

为了提高电动助力转向系统的安全性以及可靠性,基于乘用车提出一种双电机冗余电动助力转向技术,采用转向执行双助力电机,可以从硬件上实现冗余,避免任意单电机发生故障时导致转向系统的失效,不仅可以满足转向系统对于轻便型、助力跟随性的要求,还可以减轻单个助力电机的负载提高电机的使用寿命。针对提出的问题,介绍了对双电机阻力转向系统整体结构及工作原理,并对其关键零部件进行分析论述。搭建双电机冗余系统仿真模型,双电机电流分配采用“一主一辅”的控制方式,并进行方向盘多种工况仿真验证。针对助力转向出现抖动问题,结合“扫频试验”,设计输出转矩超前滞后补偿方式进行相位校正补偿。通过实车试验验证了该算法的准确性和有效性。实验结果表明双冗余电动助力转向系统极大提高了转向力矩的稳定性,抑制其扰动性,有较好的助力效果,极大提高系统的可靠性和安全性。

基于小波变换-模糊控制的复合电源能量管理

以锂电池-超级电容构成的复合电源电动汽车为研究对象,在满足动力性能的前提下,为实现超级电容在合理的荷电状态(SOC)下承担高频率信号,且锂电池承担低频率信号的目标,建立了实时小波变换-模糊控制的能量管理控制策略。基于Matlab/Simulink和ADVISOR软件搭建整车模型,并在NEDC循环工况下进行仿真测试。仿真结果表明,与单一锂电池相比,在小波变换-模糊控制策略下,复合电源锂电池的驱动峰值电流降低了20.68%,寿命提高了16.74%。搭建了按一定比例缩小的复合电源系统试验平台,并在NEDC工况下进行试验验证。结果表明,小波变换-模糊控制策略对复合电源电动汽车的能量管理具有良好的控制效果。

大型新能源电动船舶电池快速充电控制方法

电池动力输出的持久性和稳定性,会影响大型新能源电动船舶的航行状态,因此,研究一种电池快速充电控制方法。结合实际充放电数据以及循环工况拟合结果,构建大型新能源电动船舶电池等效电路,分析电池的运行状态以及输出情况;通过确定电池快速充电控制的目标函数和约束条件,将目标函数输入模糊控制器中,分析目标函数对于电池电压的影响,在模糊规则清晰化处理后计算目标函数。通过改进布谷鸟算法进行计算结果寻优,获取最佳的控制结果。测试结果显示,在瞬态响应和负载动态响应2种运行工况下,该方法可以有效完成电池充电控制,使充电输出电压最大程度地接近理想输出电压,保证电流处于允许的持续充电最大电流范围内。

电动装载机电液复合制动协同控制策略研究

针对电动装载机的电液复合制动系统,为满足多工况制动需求以及保障制动安全性,本文提出了一种基于再生制动自由行程液压制动阀的电动装载机液压制动系统。结合电动装载机的理想前后轮制动力分配曲线以及制动意图识别得到的制动强度,制定了制动强度与整车制动力矩需求的分配曲线;为进一步提高再生制动力与液压制动力分配的协调性,同时兼顾制动能量回收效率,提出了一种基于行走再生制动和液压制动的电液复合制动协同控制策略,降低了整车总制动力矩波动,保证了制动模式切换的平顺性。最后,搭建了基于AMESim-Matlab/Simulink联合仿真模型,并搭建试验样机,验证了电动装载机复合制动协同控制策略的可行性,结果表明,该系统能量回收效率可达71.6%,制动回收率可达44.5%,一个工作循环实现节能7.6%,说明本文提出的控制策略具有良好的制动性能和能量回收效率。

ECSP制备及电控燃烧技术研究进展

电控固体推进剂(Electrically controlled solid propellant,ECSP)具有点火、燃速及熄火可由馈入电能进行调控的良好性能,而成为一种备受关注的新型推进剂。针对ECSP制备及电控燃烧技术的研究现状,以主氧化剂为分类依据介绍了已公开报道的三类ECSP配方及性能;概述了熔融浇注法和凝胶固化法两种ECSP制备技术路线并分析了其各自的优缺点;从导电机理、电化学分解、热分解三方面分析了ECSP电控燃烧机理;总结了ECSP电控燃烧性能表征试验及ECSP电控燃烧特点;分析了当前公开报道的ECSP电控点火装置的优缺点;最后总结和展望了ECSP在制备技术和电控燃烧机理研究方面的不足和可以深入开展的研究方向。

对侧功能性电刺激与任务导向性训练不同方式联用对脑卒中手功能的影响

目的:研究对侧控制型电刺激(CCFES)与任务导向性训练不同方式联用对脑卒中患者手功能恢复的影响。方法:选取我院病程在1个月内的60例脑卒中患者,将其按随机数字法分为同步组即CCFES与任务导向性训练同步进行(30例)与分步组即CCFES治疗后序贯进行任务导向型训练(30例)。于入组前、治疗4周后分别采用Fugl-Meyer运动功能评分(FMA)、Wolf运动功能测试量表(WMFT)、表面肌电图(sEMG)及Barthel指数(BI)对患者的上肢、腕手运动功能及日常生活独立性进行评定。结果:治疗后2组RMS比值、FMA、WMFT及BI评分均较治疗前明显提高(P<0.01,0.05),且同步组各项评定较分步组更高(P<0.01,0.05)。结论:CCFES与任务导向性训练同步组在改善脑卒中患者的上肢及手功能更有优势。

一种智能采摘机器人的电控系统设计研究

为进一步提升我国农业采摘的作业效率及实现采摘装备的深度智能化,针对通用型采摘机器人的电控系统展开研究。以采摘机器人的作业原理及核心部件组成为设计基础,搭建电控系统输入参数与机构组件执行之间的数学模型,进行电控系统的软件功能优化与硬件配置实现。同时,展开基于电控系统设计优化的采摘机器人整机作业试验,结果表明:采摘机器人的系统动作响应迅速,系统响应率与采摘成功率分别可达98.53%及96.40%,结构协调度良好,实现了综合采摘效率相对提升5.69%的设计目标,且各采摘机构及装置运行稳定,具有较强的实践与推广价值。