仿生機械假手的肌電控制及其力觸覺感知反饋方法研究
發(fā)布時間：2019-01-19 10:48
【摘要】：假手是一類典型的人機交互設備,對于輔助手臂截肢患者恢復手部功能有著重要的作用。早期的假手以裝飾性為目的,用以在外觀上裝飾殘疾人缺失的肢體�，F(xiàn)代智能假手以電動假手為主,它是生物醫(yī)學、計算機科學、電子學、控制科學、機器人學等眾多學科融合的產(chǎn)物,其中基于肌電信號控制的假手最受關(guān)注。本文在國家自然科學基金項目、江蘇省科技計劃項目的資助下,從提高肌電假手的可控性和可感知性出發(fā),進行了仿生機械假手的肌電控制及其力觸覺感知反饋方法研究。文章首先從機構(gòu)設計、控制策略、信息感知等方面對國內(nèi)外假手的研究現(xiàn)狀進行了分析。在此基礎上針對實際需求提出了假手的設計指標,設計了包括手爪開合機構(gòu)、手腕旋轉(zhuǎn)機構(gòu)的兩自由度機械假手并對設計的假手機構(gòu)進行了詳細分析。針對假手信息感知的需求,設計了機構(gòu)傳感器一體化的假手手指,設計的手指能夠檢測假手的握力以及被抓物體與手指的接觸位置。在假手機構(gòu)設計的基礎上設計了假手的測量控制系統(tǒng),包括硬件測控電路和上位機測控軟件。針對手部運動意圖的識別,文章在設計表面肌電信號傳感器的基礎上對肌電信號進行了采集和特征提取,采用支持向量機(SVM)進行了手部/腕部8個動作的識別分類、采用廣義回歸神經(jīng)網(wǎng)絡(GRNN)實現(xiàn)了手部握力、手部三維推拉力的估計。由于肌電信號存在顯著的個體差異,實際使用時需要根據(jù)不同佩戴者的肌電信號對假手的控制參數(shù)進行調(diào)整,而且佩戴者往往需要經(jīng)過一段較長時間的適應訓練才能控制假手運動。針對這一問題,本文進行了肌電信號解耦自適應學習的動作識別方法研究。首先建立了雙通道肌電信號的解耦模型用以消除雙路信號間的重疊干擾,隨后設計了肌電信號自適應學習器用以自適應調(diào)節(jié)兩路肌電信號的歸一化比例因子,從而消除肌電信號的個體差異,實現(xiàn)肌電信號的自適應動作識別。在此基礎上設計了模糊神經(jīng)網(wǎng)絡PID控制器用于實現(xiàn)假手的比例控制。針對假手需要抓取不同物體且抓取過程中物體的特性參數(shù)可能發(fā)生變化的問題,本文進行了基于抓取對象剛度模糊觀測的假手反演控制方法研究,采用李雅普諾夫(Lyapunov)理論設計反演控制器,實現(xiàn)自由空間內(nèi)的速度控制和約束空間內(nèi)的握力控制;為了均衡不同物體對控制器性能的影響,設計模糊觀測器實時估計被抓握物體的剛度,并將其用于調(diào)整反演控制器的參數(shù),實現(xiàn)了假手對不同物體的平穩(wěn)抓取控制。信息感知能力是智能假手的一個重要特征,目前的假手多處在無感知狀態(tài)或自感知狀態(tài),即假手沒有信息感知能力或感知到的信息只服務于假手控制器本身而假手的佩戴者卻無法感知到假手的狀態(tài)。針對假手對于佩戴者缺乏感知反饋的問題,文章進行了具有力觸覺感知反饋功能的假手控制方法研究。設計振動袖帶用于向佩戴者反饋假手的狀態(tài)信息,并基于振動觸覺再現(xiàn)技術(shù)設計了三種力觸覺信息的觸覺編碼,包括握力、手腕三維力、物體在假手上的滑動信息。
[Abstract]:The artificial hand is a typical human-computer interaction device, which plays an important role in the rehabilitation of the hand function of the amputee. The early artificial hand is used as a decorative purpose to decorate the missing limb of the person with disabilities in appearance. The modern intelligent artificial hand is mainly the electric artificial hand, it is the product of many subjects such as biomedicine, computer science, electronics, control science, robotics and so on, in which the artificial hand based on the myoelectric signal control is the most concerned. In this paper, under the support of the National Natural Science Foundation of China (NSFC) and the project of Jiangsu Province's Science and Technology Program, the author has made a study on the control of the mechanical prosthetic hand and the tactile sensation feedback of the bionic mechanical prosthetic hand from the viewpoint of improving the controllability and the perceptibility of the muscle electric artificial hand. In this paper, the present situation of the research on the home and abroad is analyzed from the aspects of the mechanism design, the control strategy, the information perception and so on. On the basis of this, the author puts forward the design index of the false hand for the actual demand, and designs the two-degree-of-freedom mechanical prosthetic hand including the hand-jaw opening-closing mechanism and the wrist-rotating mechanism and analyzes the designed prosthetic hand mechanism. Aiming at the demand of false hand information perception, a false hand finger integrated with the mechanism sensor is designed, and the designed finger can detect the holding force of the artificial hand and the contact position of the object to be grasped and the finger. The control system of the false hand is designed on the basis of the design of the artificial hand mechanism, including the hardware measurement and control circuit and the measurement and control software of the upper computer. In view of the recognition of the hand movement intention, on the basis of designing the surface myoelectric signal sensor, the electromyographic signal is collected and extracted, and the recognition and classification of the hand/ wrist 8 actions are carried out by using a support vector machine (SVM). In this paper, a generalized regression neural network (GRNN) is used to estimate the hand-holding force and the three-dimensional push-pull force of the hand. Due to the significant individual difference of the myoelectric signal, the control parameters of the artificial hand need to be adjusted according to the myoelectric signals of different wearers, and the wearer often needs to be trained for a long time to control the false hand movement. In order to solve this problem, this paper studies the motion recognition method of the decoupling self-adaptive learning of the myoelectric signal. firstly, a decoupling model of the two-channel muscle electric signal is established to eliminate the overlapping interference between the two-way signals, and the self-adaptive motion recognition of the myoelectric signal is realized. On this basis, the fuzzy neural network PID controller is designed to realize the proportional control of the false hand. In order to solve the problem that the artificial hand needs to grasp different objects and the characteristic parameters of the objects in the process can change, this paper studies the artificial hand inversion control method based on the object stiffness fuzzy observation, and adopts Lyapunov theory to design the inversion controller. in order to balance the influence of different objects on the performance of the controller, a fuzzy observer is designed to estimate the rigidity of the gripped object in real time and to adjust the parameters of the inversion controller, and the stable grabbing control of the artificial hand on different objects is realized. The information sensing capability is an important feature of the intelligent artificial hand, and the current false hand is in the non-sensing state or the self-sensing state, that is, the artificial hand has no information sensing capability or the perceived information is only serving the false hand controller, and the wearer of the artificial hand can not perceive the state of the false hand. In view of the problem of the false hand's lack of sense feedback to the wearer, the paper makes a study on the method of artificial hand control with the function of haptic perception feedback. The design vibration cuff is used to feedback the state information of the artificial hand to the wearer, and the haptic coding of three kinds of force tactile information is designed based on the vibration haptic reproduction technique, including the grip force, the three-dimensional force of the wrist, and the sliding information of the object on the artificial hand.
【學位授予單位】：東南大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP241
本文編號：2411298
[Abstract]:The artificial hand is a typical human-computer interaction device, which plays an important role in the rehabilitation of the hand function of the amputee. The early artificial hand is used as a decorative purpose to decorate the missing limb of the person with disabilities in appearance. The modern intelligent artificial hand is mainly the electric artificial hand, it is the product of many subjects such as biomedicine, computer science, electronics, control science, robotics and so on, in which the artificial hand based on the myoelectric signal control is the most concerned. In this paper, under the support of the National Natural Science Foundation of China (NSFC) and the project of Jiangsu Province's Science and Technology Program, the author has made a study on the control of the mechanical prosthetic hand and the tactile sensation feedback of the bionic mechanical prosthetic hand from the viewpoint of improving the controllability and the perceptibility of the muscle electric artificial hand. In this paper, the present situation of the research on the home and abroad is analyzed from the aspects of the mechanism design, the control strategy, the information perception and so on. On the basis of this, the author puts forward the design index of the false hand for the actual demand, and designs the two-degree-of-freedom mechanical prosthetic hand including the hand-jaw opening-closing mechanism and the wrist-rotating mechanism and analyzes the designed prosthetic hand mechanism. Aiming at the demand of false hand information perception, a false hand finger integrated with the mechanism sensor is designed, and the designed finger can detect the holding force of the artificial hand and the contact position of the object to be grasped and the finger. The control system of the false hand is designed on the basis of the design of the artificial hand mechanism, including the hardware measurement and control circuit and the measurement and control software of the upper computer. In view of the recognition of the hand movement intention, on the basis of designing the surface myoelectric signal sensor, the electromyographic signal is collected and extracted, and the recognition and classification of the hand/ wrist 8 actions are carried out by using a support vector machine (SVM). In this paper, a generalized regression neural network (GRNN) is used to estimate the hand-holding force and the three-dimensional push-pull force of the hand. Due to the significant individual difference of the myoelectric signal, the control parameters of the artificial hand need to be adjusted according to the myoelectric signals of different wearers, and the wearer often needs to be trained for a long time to control the false hand movement. In order to solve this problem, this paper studies the motion recognition method of the decoupling self-adaptive learning of the myoelectric signal. firstly, a decoupling model of the two-channel muscle electric signal is established to eliminate the overlapping interference between the two-way signals, and the self-adaptive motion recognition of the myoelectric signal is realized. On this basis, the fuzzy neural network PID controller is designed to realize the proportional control of the false hand. In order to solve the problem that the artificial hand needs to grasp different objects and the characteristic parameters of the objects in the process can change, this paper studies the artificial hand inversion control method based on the object stiffness fuzzy observation, and adopts Lyapunov theory to design the inversion controller. in order to balance the influence of different objects on the performance of the controller, a fuzzy observer is designed to estimate the rigidity of the gripped object in real time and to adjust the parameters of the inversion controller, and the stable grabbing control of the artificial hand on different objects is realized. The information sensing capability is an important feature of the intelligent artificial hand, and the current false hand is in the non-sensing state or the self-sensing state, that is, the artificial hand has no information sensing capability or the perceived information is only serving the false hand controller, and the wearer of the artificial hand can not perceive the state of the false hand. In view of the problem of the false hand's lack of sense feedback to the wearer, the paper makes a study on the method of artificial hand control with the function of haptic perception feedback. The design vibration cuff is used to feedback the state information of the artificial hand to the wearer, and the haptic coding of three kinds of force tactile information is designed based on the vibration haptic reproduction technique, including the grip force, the three-dimensional force of the wrist, and the sliding information of the object on the artificial hand.
【學位授予單位】：東南大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP241
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