Functional Neuromuscular Stimulation for articular angle control with an Inverse Dynamics Model tuned by a neural network

A Functional Neuromuscular Stimulation (FNS) system was developed to restore the motor function of people with central nervous system disorders. An FNS articular angle control system with an Inverse Dynamics Model (IDM) tuned by a neural network was proposed. The system was designed to control the elbow joint angles by learning the characteristics of the neuromuscular system of controlled limbs. Three kinds of control schemes using the IDM were proposed. In a simulation experiment, the system was able to learn the abnormal characteristics of a limb and any changes in these characteristics, and then allow for better control of the limb. The results of experiments using human participants showed that the system had ability to control both normal and paralysed limbs.     

Iterative learning feedback control of human limbs via functional electrical stimulation

A high-order iterative learning controller (ILC) is proposed for the tracking control of an electrically stimulated human limb that is repeatedly required to perform a given task. The limb is actuated by the muscles, which are out of the control of the central nerve systems (CNS), through functional electrical stimulation (FES) or functional neuromuscular stimulation (FNS). By using the proposed discrete-time high-order P-type ILC updating law and the PD-type feedback controller, it is shown that the proposed control strategy, which learns from repetitions, provides strong robustness in tracking control of the uncertain time-varying FES systems, which is essential for the adaptation and customization of FES applications. The effectiveness of the proposed control scheme is demonstrated by simulation results on a one-segment planar system. Some experimental results are also presented to validate the proposed control method.     

下肢康复机器人的自适应人机交互控制策略

针对康复机器人运动过程中的人机交互性问题,提出一种下肢康复机器人自适应人机交互控制策略.提取伸屈运动中下肢表面肌电信号（Surface electromyography,sEMG）和足底压力特征,分别用于表征下肢运动意图和人机交互力（Interaction force,IF）信息,建立基于sEMG-IF的人机交互信息融合模型,实现下肢康复机器人运动轨迹的在线规划;考虑主动康复运动过程中的人机交互作用,建立具有时变动态特性的人机系统动力学模型,设计间接模糊自适应控制器对期望轨迹进行跟踪控制,实现下肢康复机器人自适应人机交互控制.通过对5名被试者进行下肢康复机器人运动控制实验研究,验证所提方法的可行性和有效性.     

D型迭代学习控制及其在FNS肢体运动控制系统中的应用

给出了离散系统D－型迭代学习控制算法收敛的一种充分条件,并加以证明。采用D型迭代学习控制算法,对基于功能性神肉电刺激的曲腕和曲肘运动进行了临床实验研究,结果表明,D型迭代学习算法改善了FNS肢体运动控制的跟踪性能,曲肘和曲腕运动轨迹平滑、稳定,并且刺激控制脉冲变化平缓,受试者无任何不良生理反应。     

Automated generation of a model view definition from an information delivery manual using idmXSD and buildingSMART data dictionary

The development of an integrated information delivery manual (IDM) and model view definition (MVD) using the buildingSMART Data Dictionary (bSDD) as a lexicon has been identified as an ideal way of developing IDMs and MVDs. Several previous studies proposed methods to integrate the IDM and MVD development process. However, they were inherently limited in various ways because they were proposed before the core relevant technologies were ready. This study proposes an integrated IDM and MVD development method using bSDD as a lexicon based on three international standards—ISO 12006-3, ISO 16739-1, and ISO 29481-3. In particular, ISO 29481-3 is deployed as a core data schema to integrate the entire process. ISO 29481-3, of which the authors were the main developers, is a new ISO standard that specifies the information delivery manual XML schema definition (idmXSD). In addition, the concept-based MVD generation algorithm was adopted to generate a syntactically valid MVD. The proposed method was validated against the basic Facility Management (FM) Handover MVD and the Construction–Operations Building Information Exchange (COBie) MVD. The proposed approach is expected to help facilitate the development of IDM and MVD by allowing developers to utilize bSDD as a common lexicon and share and repurpose existing IDMs and their relationships to MVDs in a machine-readable format using idmXSD.     

A relational framework for smart information delivery manual (IDM) specifications

This study proposes a relational framework for standardized machine-applicable, readable, and transferable (smart) information delivery manual (IDM) specifications. As the demand for projects using building information modeling (BIM) increases, there is a more acute awareness and need for clearly defined information requirements to support the BIM projects. The ISO 29481-1 IDM standard defines how to specify exchange requirements (ERs) and their use cases (UCs) using a process map (PM). However, IDM specifications are currently not easily sharable or reusable due to the lack of a commonly accepted standard data schema. This study overcomes this problem by identifying definitions and relationships of IDM components to develop the relational framework for an IDM data schema. An extensive review of existing IDM-related documents and standards, and iterative international meetings, were conducted by 46 international IDM experts from 16 countries. A consensus on the relational framework was then reached through three Delphi survey rounds. The formalized relational IDM framework lays a foundation for further developing an IDM data schema as an international standard.     

Fuzzy predictive control based multiple models strategy for a tubular heat exchanger system

This work deals with the problem of controlling the outlet temperature of a tubular heat exchanger system by means of flow pressure. The usual industrial case is to try to control the outlet temperature by either the temperature or the flow of the fluid, which flows through the shell tube. But, in some situations, this is not possible, due to the fact that the whole of system coefficients variation cannot quite be covered by control action. In this case, the system behavior must precisely be modeled and appropriate control action needs to be obtained based on novel techniques. A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has been proposed, in this paper. The main idea of the proposed control strategy is to represent the operating environments of the system, which have a wide range of variation with respect to time by multiple explicit linear models. In this strategy, the best model of the system is accurately identified, at each instant of time, by an intelligent decision mechanism (IDM), which is organized based on both new recursive weight generator and fuzzy adaptive Kalman filter approaches. After that, the adaptive algorithm is implemented on the chosen model. Finally, for having a good tracking performance, the generalized predictive control is instantly updated and its control action is also applied to the system. For demonstrating the effectiveness of the proposed approach, simulations are all done and the results are also compared with those obtained using a nonlinear GPC (NLGPC) approach that is realized based on the Wiener model of the system. The results can verify the validity of the proposed control scheme.     

Task parameters affecting individual differences in pursuit and compensatory tracking performance

Abstract

Two tracking tasks, the Control-Velocity Tester and the Sensory-Motor Apparatus (both of which are currently used as aptitude tests for the selection of candidates for pilot training), provide relatively independent measures of performance. As part of the programme of research related to the production of the Micropat automated testing system, a number of studies were carried out to identify, the parameters accounting for the functional dissimilarity of these two tasks. The variables examined were: display mode (pursuit versus compensatory), number of dimensions (one versus two), control law, and assignment of controls to limbs. While varying control law mixture had a relatively small effect on task intercorrelations, both variation in display mode and number of dimensions substantially reduced them. The effects of limb assignment were complex, though in general they showed that for two-dimensional tasks the important factor was whether control was carried out using a single limb rather than using separate limbs for each dimension. After practice, the actual control devices and limbs used were relatively less important.     

MISO OFDM链路中的IDM空时编码

在链路级别上,分析研究了采用交织复用(IDM)空时编码的多进单出(MISO)正交频分多址(OFDM)系统.介绍了一种达代接收机结构,并在准静态信道条件下通过仿真对其性能进行评估.结果表明,在相似环境下与采用其它空时码的系统比较,采用交织复用空时编码的系统在性能上具有一定的优越性.     

基于功能性电刺激的上肢康复系统条件反馈控制

功能性电刺激是脑卒中患者有效的康复方法,然而上肢肌肉的动态模型存在着强非线性、多源扰动、模型不精确和参数变化大等控制难点.为提高基于功能性电刺激的上肢康复系统的跟踪速度和扰动抑制能力,本文提出了一种基于条件反馈的比例–积分控制策略,条件反馈的设计用于提高系统的跟踪性能,通过比例–积分控制器抑制外部扰动与模型不确定性.通过单一变量法分析了控制器参数对控制效果的影响,提出了一种简单、实用的参数整定规则.基于Hammerstein肌骨系统模型,通过仿真验证了所提方法在保证闭环系统鲁棒性的前提下,与常规的比例–积分控制器相比在跟踪和扰动抑制性能方面的优势.最后,基于搭建的上肢康复系统控制平台,对不同实验者在不同目标输出力下进行对比实验,实验结果表明本文所提基于条件反馈的比例–积分控制策略的平均峰值量占比为9.22%,而对比控制策略的平均峰值量占比为17.80%,所提控制策略的平均单位误差绝对值积分指标是对比控制策略平均单位误差绝对值积分指标的70.43%,验证了本文所设计的控制策略在保证闭环系统鲁棒性的前提下具有更好的跟踪效果.     

Design of intention-based assistive robot for upper limb

Robotic assistive devices for rehabilitation have proven to be beneficial for many individuals with impaired limbs by providing accessible, consistent and cost-effective therapy. However, it is important to acknowledge that many people need an alternative form of assistance for physical impairments, both while undergoing rehabilitation and in the common scenario of rehabilitation providing insufficient improvements. The aim of this paper is to present an intention-detecting assistive robot for upper limb motion, which may serve as a complement to rehabilitation procedures. The proposed system collects real-time data from an eye-tracker and force-sensing resistors to determine the intended movement of the arm of a user and physically assists the said movement. We show that the system is capable of moving an impaired upper limb to a desired position with average errors of less than 5 cm, and in this manner may assist motion required by various activities of daily living for someone suffering from physical impairments in their upper limbs.     

An EMG-based variable impedance control for elbow exercise: preliminary study

As the medical field continues to evolve, the average life expectancy of the people increases. However, due to natural deterioration, the average muscle force of the arm decreases in respect to age. So, there are some needs for the people to exercise his/her body as he/she is getting older. There are many researches about the robotic rehabilitation of the upper limb for patients. However, only a few focus on the system of the upper limb muscle exercise for normal people. So, in this study, an electromyography (EMG)-based variable impedance control method is developed for elbow exercise in normal people. Four EMG sensors were attached to the designated right arm and their average EMG level was controlled by the proposed algorithm during an elbow flexion-extension exercise. A total of twenty nine experiments with 15 subjects were performed to verify the proposed control algorithm with NCCEES, an elbow exercise system developed by the authors for the elbow exercise. The experimental results showed that the proposed control algorithm could control the EMG level in approximately 24.1% percent error during the elbow exercise. However, the proposed control algorithm could not control the EMG level fluctuation which depended on the muscular characteristics of the subject. The effect of training with the proposed algorithm will be future work.     

康复机器人患肢参数的在线辨识

根据患肢训练时力和位置等信息反馈,提出用解超定方程组的方法在线辨识患肢的动力学参数,实现患肢动力学模型的在线辨识,为远程康复训练机器人系统的实时控制提供较为准确的依据.仿真实验验证,该方法能较好地改善系统的动态性能,使系统具有较好的稳定性和鲁棒性.     

A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification

The paper deals with the problem of controlling artificial limbs in cases where several limb functions require control. These situations are of importance, especially to above-elbow upper-extremity amputees. Here, classical EMG (myoelectric) controllers have failed in the past, since they were based on only determining existence or nonexistence of an EMG signal. Recent work by Lyman et al. at UCLA has approached this multifunctional (MF) control problem via using a large number of electrodes, though still considering only a limited part of the EMG spectrum. The present approach is based on earlier work by Graupe et al. [4], considering the whole spectrum of the EMG signal via identifying its time series model such that several limb functions can be controlled from a single signal site. However, the present work subsequently employs parallel filtering to discriminate between the various limb functions of interest to achieve fast discrimination and control as required for practical applications, since this allows the identification itself to be performed off line. Real-time on-line results are presented in the paper, as recently obtained from tests carried out on a 1969 Vietnam above-elbow amputee who had most severe (90 percent) nerve and muscle loss at his stump, and could therefore not use more than one or two electrode pairs. The results, where complete discrimination was achieved within 0.15 to 0.2 seconds using 8-bit Intel 8080 microprocessors at double precision (incorporating hardware multipliers), have yielded an 85 percent success rate in discrimination between four to five limb functions using a single electrode pair. It is noted that the amputee mentioned had no previous EMG actuation training whatsoever.     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors

In this study, a novel approach is described to the design of an interval type‐2 fuzzy neural system (IT2 FNS). It differs from the classical IT2 FNS in its use of parameterized conjunctors. In the optimization of the IT2 FNS, the membership functions are kept fixed and only the parameters of the conjunctors and the parameters in the consequent are tuned. In this study, the gradient based learning algorithm is used. The approach is tested for the modeling of a benchmark nonlinear function and for the wheel slip control of a quarter car model (QCM). In the stated applications, in the absence of any expert knowledge, some knowledge about the system is gained by the use of the interval type‐2 fuzzy c‐means (IT2 FCM) clustering algorithm. Nevertheless, this requires the number of classes to be known beforehand. To alleviate this problem, some validity indices that have been suggested in the literature and a novel validity index that carries less computational burden are considered to determine the number of classes and the number of fuzzy rules. Simulation studies are presented and compared with the results from the literature.     

网联车辆自适应巡航控制算法验证平台设计

自适应巡航控制系统是一种车辆高级辅助驾驶系统，不仅可以减轻驾驶员工作负担，还能增强车辆的行车安全和驾乘舒适性。基于MATLAB和Python混合编程，在网络协议环境下设计智能车巡航控制算法验证平台；该平台能够模拟多种不同典型行车场景，其实时的信息采集与动画演示功能能够直观有效地展现车辆状态。结合所开发的平台软件，设计出增量式模型预测控制器（IMPC）和智能驱动驾驶（IDM）控制器，所提出的IMPC算法不仅能综合考虑网联车巡航多目标的特点，同时还满足巡航系统快速性和准确性的要求。最后结合典型驾驶工况，开展智能车的车辆自适应巡航控制实验。实验结果表明，基于MATLAB和Python混合编程的软件系统能有效模拟各种驾驶情景，并能结合智能车验证该自适应巡航控制算法的结果。     

Classification with decision trees from a nonparametric predictive inference perspective

An application of nonparametric predictive inference for multinomial data (NPI) to classification tasks is presented. This model is applied to an established procedure for building classification trees using imprecise probabilities and uncertainty measures, thus far used only with the imprecise Dirichlet model (IDM), that is defined through the use of a parameter expressing previous knowledge. The accuracy of that procedure of classification has a significant dependence on the value of the parameter used when the IDM is applied. A detailed study involving 40 data sets shows that the procedure using the NPI model (which has no parameter dependence) obtains a better trade-off between accuracy and size of tree than does the procedure when the IDM is used, whatever the choice of parameter. In a bias-variance study of the errors, it is proved that the procedure with the NPI model has a lower variance than the one with the IDM, implying a lower level of over-fitting.     

基于Internet的远程控制康复训练机械臂

针对上肢受伤和中风病人康复医疗的需要,将力反馈遥操作机器人技术应用于上肢受伤和中风病人的康复训练,研制了一种新颖的基于Internet的远程康复训练机械臂,使得医生可远程设定康复机械臂的训练模式和控制参数,从而控制机械臂,帮助在家中或社区医院的中风病人或上肢残障人员进行康复训练,医生利用反馈的视频图像及力反馈信息监控患者的训练状况.该系统可以大大提高医生的工作效率,方便广大中风病人和上肢残障人员.