【摘要】：外骨骼機(jī)器人是一種可穿戴式機(jī)器人,其在行軍作戰(zhàn)、醫(yī)療康復(fù)和民用助力等方面具有極廣泛的應(yīng)用前景。本文就外骨骼機(jī)器人的傳感系統(tǒng)和控制算法,從串聯(lián)彈性驅(qū)動(dòng)、機(jī)器人動(dòng)力學(xué)模型、靈敏度放大控制器的設(shè)計(jì)和在線強(qiáng)化學(xué)習(xí)參數(shù)優(yōu)化等角度對(duì)其展開(kāi)研究。為增強(qiáng)穿戴者在佩戴機(jī)器人行走過(guò)程中的舒適性,提高機(jī)器人的仿生特性,本文在傳統(tǒng)外骨骼關(guān)節(jié)基礎(chǔ)上進(jìn)行改進(jìn),在關(guān)節(jié)驅(qū)動(dòng)電機(jī)和負(fù)載之間串聯(lián)了彈性元件,用以減緩運(yùn)動(dòng)過(guò)程中的沖擊作用并存儲(chǔ)運(yùn)動(dòng)能量。先對(duì)彈性關(guān)節(jié)進(jìn)行了數(shù)學(xué)建模,通過(guò)MATLAB仿真分析其動(dòng)態(tài)跟隨特性以確定合適本系統(tǒng)的彈性體剛度。為最大程度上簡(jiǎn)化機(jī)器人傳感系統(tǒng),本文采用靈敏度放大控制方法,此控制方法不需要任何檢測(cè)人機(jī)交互力的傳感器,但其對(duì)機(jī)器人動(dòng)力學(xué)方程及動(dòng)力學(xué)參數(shù)的準(zhǔn)確性提出較高要求。本文使用拉格朗日方程推導(dǎo)機(jī)器人動(dòng)力學(xué)方程,為最大程度上保證模型準(zhǔn)確性,關(guān)節(jié)摩擦力矩和電機(jī)轉(zhuǎn)子慣量等因素被統(tǒng)一考慮在模型當(dāng)中,桿件質(zhì)量和質(zhì)心位置均采用實(shí)驗(yàn)的方法進(jìn)行辨識(shí)。所設(shè)計(jì)的控制器采用通過(guò)實(shí)驗(yàn)辨識(shí)出來(lái)的機(jī)器人動(dòng)力學(xué)參數(shù),在定系數(shù)靈敏度放大控制實(shí)驗(yàn)成功之后,為進(jìn)一步優(yōu)化機(jī)器人隨動(dòng)效果,本文設(shè)計(jì)了強(qiáng)化學(xué)習(xí)在線優(yōu)化靈敏度系數(shù)的算法。即下層仍采用靈敏度放大控制,上層采用DMP軌跡規(guī)劃結(jié)合強(qiáng)化學(xué)習(xí)的在線參數(shù)優(yōu)化算法對(duì)靈敏度系數(shù)進(jìn)行在線優(yōu)化。使用DMP算法對(duì)人體運(yùn)動(dòng)步態(tài)進(jìn)行學(xué)習(xí)并給出預(yù)測(cè)軌跡,其與實(shí)際軌跡的偏差作為Q學(xué)習(xí)算法的實(shí)時(shí)獎(jiǎng)勵(lì)。通過(guò)MATLAB仿真驗(yàn)證了控制算法的穩(wěn)定性。最后搭建外骨骼機(jī)器人實(shí)驗(yàn)平臺(tái),在外骨骼機(jī)器人實(shí)驗(yàn)系統(tǒng)上對(duì)所設(shè)計(jì)的算法進(jìn)行驗(yàn)證,證實(shí)了動(dòng)力學(xué)參數(shù)辨識(shí)的準(zhǔn)確性和在線優(yōu)化靈敏度系數(shù)算法的有效性。
[Abstract]:Exoskeleton robot is a wearable robot, which has a wide application prospect in marching, medical rehabilitation and civil assistance. In this paper, the sensing system and control algorithm of exoskeleton robot are studied from the point of view of series elastic drive, dynamic model of robot, design of sensitivity amplification controller and optimization of on-line reinforcement learning parameters. In order to enhance the comfortableness of the wearer in the walking process of the wearing robot and to improve the bionic characteristics of the robot, this paper improves on the traditional exoskeleton joint and makes a series of elastic elements between the joint driving motor and the load. Used to slow down the impact of motion and store motion energy. Firstly, the elastic joint is modeled by mathematical method, and its dynamic following characteristic is analyzed by MATLAB simulation to determine the stiffness of the elastic body suitable for the system. In order to simplify the robot sensor system to the greatest extent, the sensitivity amplification control method is adopted in this paper. This control method does not need any sensors to detect the human-computer interaction force. However, the accuracy of the dynamic equation and dynamic parameters of the robot is very high. In this paper, the dynamic equations of the robot are derived by using Lagrange equation. In order to ensure the accuracy of the model to the greatest extent, the joint friction moment and the moment of inertia of the motor rotor are considered in the model. The mass and centroid position of the member are identified by the experimental method. The designed controller adopts the robot dynamics parameters identified by experiments. After the experiment of constant coefficient sensitivity amplification control is successful, in order to further optimize the robot follow-up effect, In this paper, an algorithm for on-line optimization of sensitivity coefficient by reinforcement learning is designed. In other words, the lower layer still adopts sensitivity amplification control, and the upper layer optimizes the sensitivity coefficient by DMP trajectory planning and reinforcement learning online parameter optimization algorithm. The DMP algorithm is used to study the human moving gait and the prediction trajectory is given. The deviation from the actual track is the real time reward of Q learning algorithm. The stability of the control algorithm is verified by MATLAB simulation. Finally, the experimental platform of exoskeleton robot is built, and the designed algorithm is verified on the exoskeleton robot experimental system, which verifies the accuracy of dynamic parameter identification and the effectiveness of on-line optimization sensitivity coefficient algorithm.
【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(lèi)號(hào)】：TP242

【相似文獻(xiàn)】

相關(guān)博士學(xué)位論文 前4條

1 張超;下肢助力外骨骼機(jī)器人研究[D];哈爾濱工業(yè)大學(xué);2016年

2 陳春杰;基于柔性傳動(dòng)的助力全身外骨骼機(jī)器人系統(tǒng)研究[D];中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院深圳先進(jìn)技術(shù)研究院);2017年

3 龍億;下肢外骨骼人體運(yùn)動(dòng)預(yù)測(cè)與人機(jī)協(xié)調(diào)控制技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2017年

4 陳珊;行走下肢液壓增力外骨骼自適應(yīng)魯棒力控制研究[D];浙江大學(xué);2017年

相關(guān)碩士學(xué)位論文 前7條

1 叢林;下肢外骨骼機(jī)器人系統(tǒng)參數(shù)辨識(shí)和控制方法研究[D];哈爾濱工業(yè)大學(xué);2017年

2 唐逵;基于有限元方法的下肢外骨骼結(jié)構(gòu)優(yōu)化設(shè)計(jì)[D];電子科技大學(xué);2017年

3 馬舜;主被動(dòng)結(jié)合式下肢助力外骨骼機(jī)器人研制[D];哈爾濱工業(yè)大學(xué);2017年

4 楊陽(yáng);外骨骼電機(jī)伺服驅(qū)動(dòng)系統(tǒng)設(shè)計(jì)[D];電子科技大學(xué);2017年

5 盛文濤;基于人機(jī)耦合系統(tǒng)特性分析的外骨骼單腿參數(shù)辨識(shí)方法[D];哈爾濱工業(yè)大學(xué);2017年

6 牟洋;拇指功能康復(fù)的外骨骼機(jī)器人研究[D];哈爾濱工業(yè)大學(xué);2017年

7 郭寧;上肢神經(jīng)康復(fù)外骨骼機(jī)器人控制策略研究[D];哈爾濱工業(yè)大學(xué);2017年

，

本文編號(hào)：2419376