本文選題：下肢康復(fù)機(jī)器人 + 外骨骼��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：康復(fù)機(jī)器人是醫(yī)療機(jī)器人的重要分支,涉及康復(fù)醫(yī)學(xué)、機(jī)器人學(xué)及控制學(xué)等多個(gè)領(lǐng)域,致力于癱瘓患者的肢體康復(fù)。近年來(lái),隨著老齡化現(xiàn)象的日益嚴(yán)重,肢體癱瘓的患者也逐漸增多。本文針對(duì)下肢癱瘓的患者,設(shè)計(jì)了一款坐臥式下肢康復(fù)機(jī)器人,針對(duì)其控制策略進(jìn)行了研究,包括患者使用其進(jìn)行被動(dòng)運(yùn)動(dòng)和主動(dòng)運(yùn)動(dòng)時(shí)進(jìn)行的控制,并通過相應(yīng)的仿真和實(shí)驗(yàn)進(jìn)行驗(yàn)證。為了更好地控制機(jī)器人的運(yùn)動(dòng),需要詳細(xì)的了解本課題的實(shí)驗(yàn)平臺(tái)。本文研究的坐臥式下肢康復(fù)機(jī)器人具有3個(gè)自由度,可以滿足患者日常的步態(tài)訓(xùn)練。利用拉格朗日方程對(duì)機(jī)器人系統(tǒng)進(jìn)行動(dòng)力學(xué)分析,并推導(dǎo)出電動(dòng)缸的驅(qū)動(dòng)力與力矩的關(guān)系。通過動(dòng)力學(xué)方程得到關(guān)節(jié)驅(qū)動(dòng)力矩,然后利用ADAMS軟件建立康復(fù)機(jī)器人系統(tǒng)的動(dòng)力學(xué)模型,通過仿真實(shí)驗(yàn)得到驅(qū)動(dòng)力矩,兩者比較驗(yàn)證了動(dòng)力學(xué)方程和ADAMS模型的正確性。當(dāng)患肢完全癱瘓時(shí),需要完全由外力帶動(dòng)運(yùn)動(dòng),即被動(dòng)運(yùn)動(dòng)訓(xùn)練,而被動(dòng)運(yùn)動(dòng)控制可看作是軌跡跟蹤控制。本課題提出的被動(dòng)運(yùn)動(dòng)控制策略結(jié)合了三種控制方法:基于計(jì)算力矩法的PD反饋控制主要用于系統(tǒng)標(biāo)稱模型的控制,RBF神經(jīng)網(wǎng)絡(luò)用于逼近補(bǔ)償系統(tǒng)的模型的不確定項(xiàng),自適應(yīng)魯棒控制用于補(bǔ)償RBF神經(jīng)網(wǎng)絡(luò)的逼近誤差和外界干擾。本文運(yùn)用李雅普諾夫穩(wěn)定性理論驗(yàn)證了其穩(wěn)定性,并通過仿真實(shí)驗(yàn)驗(yàn)證算法的有效性。當(dāng)患肢逐漸康復(fù),有一定的運(yùn)動(dòng)能力時(shí)可以進(jìn)行主動(dòng)運(yùn)動(dòng)訓(xùn)練,本文通過基于位置的阻抗控制對(duì)患肢進(jìn)行期望力的跟蹤。本文首先對(duì)人機(jī)接觸阻抗進(jìn)行了研究,然后分析了阻抗參數(shù)對(duì)系統(tǒng)控制性能的影響,利用粒子群優(yōu)化算法優(yōu)化阻抗參數(shù),最后利用參考模型自適應(yīng)控制提高系統(tǒng)的魯棒性,并通過仿真實(shí)驗(yàn)驗(yàn)證了算法的有效性。完成對(duì)坐臥式下肢康復(fù)機(jī)器人實(shí)驗(yàn)系統(tǒng)的搭建,包括硬件系統(tǒng)和軟件系統(tǒng)。針對(duì)實(shí)驗(yàn)平臺(tái)的特點(diǎn),對(duì)電機(jī)和驅(qū)動(dòng)器進(jìn)行了相應(yīng)的設(shè)置,并對(duì)傳感器進(jìn)行了標(biāo)定,使其可以實(shí)時(shí)的檢測(cè)外界環(huán)境。為了保證系統(tǒng)的運(yùn)轉(zhuǎn)精度和安全性,對(duì)系統(tǒng)相應(yīng)的輸入和輸出進(jìn)行了詳細(xì)推導(dǎo),并設(shè)計(jì)了軟件限位。最后通過實(shí)驗(yàn)驗(yàn)證了本文提出的基于計(jì)算力矩法的神經(jīng)網(wǎng)絡(luò)魯棒控制和基于MRAC的自適應(yīng)阻抗控制的在提高控制精度方面的有效性。
[Abstract]:Rehabilitation robot is an important branch of medical robot. It is involved in rehabilitation medicine, robotics and control. In recent years, with the increasing aging phenomenon, the number of patients with limb paralysis is increasing. In this paper, a sitting and horizontal lower limb rehabilitation robot is designed for patients with lower extremity paralysis. The control strategy is studied, including the passive and active motion control. And through the corresponding simulation and experiment to verify. In order to better control the motion of the robot, we need to understand the experimental platform in detail. The robot has 3 degrees of freedom, which can meet the patients' daily gait training. The dynamic analysis of robot system is carried out by using Lagrange equation, and the relation between driving force and torque of electric cylinder is deduced. Then the dynamic model of rehabilitation robot system is established by using Adams software, and the driving torque is obtained by simulation experiment. The correctness of the dynamic equation and Adams model is verified by comparing the two equations. When the affected limb is completely paralyzed, it needs to be driven by external force, that is, passive motion training, and passive motion control can be regarded as trajectory tracking control. The passive motion control strategy proposed in this paper combines three control methods: PD feedback control based on calculating torque method is mainly used to control the nominal model of the system and RBF neural network is used to approximate the uncertainty of the model of compensation system. Adaptive robust control is used to compensate the approximation error and external disturbance of RBF neural network. In this paper, Lyapunov stability theory is used to verify its stability, and the effectiveness of the algorithm is verified by simulation experiments. When the affected limb recovers gradually and has certain movement ability, the active movement training can be carried out. In this paper, the expected force of the affected limb is tracked by the impedance control based on position. In this paper, the impact of impedance parameters on the control performance of the system is analyzed, and the particle swarm optimization algorithm is used to optimize the impedance parameters. Finally, the reference model adaptive control is used to improve the robustness of the system. The effectiveness of the algorithm is verified by simulation experiments. The experiment system of sitting and horizontal lower limb rehabilitation robot is built, including hardware system and software system. According to the characteristics of the experimental platform, the motor and driver are set up, and the sensor is calibrated so that it can detect the external environment in real time. In order to ensure the operation precision and security of the system, the corresponding input and output of the system are deduced in detail, and the software limit is designed. Finally, the effectiveness of the proposed neural network robust control based on the computational moment method and the adaptive impedance control based on MRAC in improving the control accuracy is verified by experiments.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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本文編號(hào)：2095814