本文選題：機械臂系統(tǒng) + 運動學(xué)控制器��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：在輕工業(yè)、手工業(yè)等行業(yè)的生產(chǎn)過程中,存在著大量高強度的重復(fù)性工作,這類工作適合使用機械臂系統(tǒng)代替人類完成。在汽車、電子等行業(yè)中,機械臂已經(jīng)被大量投入使用,相關(guān)的機械臂系統(tǒng)控制技術(shù)也已比較成熟;但是,現(xiàn)有的成熟機械臂系統(tǒng)大多使用國外知名廠商生產(chǎn)的精密機械臂,系統(tǒng)的購買和維護成本很高,我國中小型企業(yè)難以負擔設(shè)備引入的高額成本。因此,選取合適的低成本機械臂系統(tǒng)、提出適合于實際工況的機械臂控制方案,對于提升中小型輕、手工業(yè)企業(yè)的自動化程度有很大的實際意義。本文選取一類配備運動學(xué)控制器的機械臂系統(tǒng)作為研究對象,設(shè)計了三種不同形式的迭代學(xué)習(xí)控制器用以解決不同情況下的控制問題,為這一類機械臂系統(tǒng)的控制器選取問題提供了一套比較完整的解決方案。本文的主要研究內(nèi)容如下:(1)針對機械臂對象模型未知的情況,選取了 一種無模型的PD型閉環(huán)迭代學(xué)習(xí)控制算法。首先,由于迭代學(xué)習(xí)算法本身非常適合解決非線性系統(tǒng)軌跡跟蹤問題,因此選取迭代學(xué)習(xí)控制作為機械臂系統(tǒng)的基本控制方案;之后,本文對比了閉環(huán)迭代學(xué)習(xí)算法與開環(huán)算法的特點,選取了對于環(huán)境干擾抑制效果較好的閉環(huán)形式迭代學(xué)習(xí)算法;最后,考慮到PID形式的控制器結(jié)構(gòu)簡單、魯棒性強的特點以及迭代學(xué)習(xí)過程本身在迭代軸上的積分效應(yīng),選取了PD型閉環(huán)迭代學(xué)習(xí)律作為機械臂系統(tǒng)的控制算法。仿真實驗證明了該算法對于解決機械臂軌跡跟蹤問題的有效性。(2)針對機械臂對象參考模型已知的情況,提出了一種基于固定運動學(xué)模型的迭代學(xué)習(xí)控制算法。通過使用機械臂對象的參考模型作為先驗知識,利用運動學(xué)逆解得到的參考關(guān)節(jié)角指導(dǎo)迭代學(xué)習(xí)過程,該算法能夠有效加快系統(tǒng)跟蹤誤差的收斂速度。仿真結(jié)果顯示,通過使用基于固定運動學(xué)模型的控制算法,系統(tǒng)誤差收斂速度明顯提升。(3)針對機械臂參考模型與實際對象偏差較大的情況,提出了一種基于自適應(yīng)模型的迭代學(xué)習(xí)控制算法。該算法使用卡爾曼濾波方法對機械臂運動學(xué)模型參數(shù)進行在線估計,每次迭代后更新對象的參考模型用于下一步迭代學(xué)習(xí)控制。仿真實驗結(jié)果表明,通過使用基于自適應(yīng)模型的算法,機械臂系統(tǒng)在對象參考模型失配較為嚴重的情況下依然能夠順利完成軌跡跟蹤的任務(wù)。(4)搭建了機械臂硬件平臺,并基于該平臺對上文中的無模型與基于固定模型的迭代學(xué)習(xí)控制算法進行了初步實驗。實驗結(jié)果顯示,由于觀測誤差等因素的存在,系統(tǒng)輸出存在一定波動;但是隨著迭代的進行,機械臂末端執(zhí)行器的輸出軌跡與期望軌跡之間的誤差能夠逐漸減小。本文對上述實驗結(jié)果進行了誤差分析。
[Abstract]:In the production process of light industry, handicraft industry and other industries, there is a large number of high intensity repetitive work, this kind of work is suitable for the use of robotic arm system instead of human completion. In automobile, electronics and other industries, the mechanical arm has been widely used, and the related control technology of the manipulator system has been relatively mature. However, most of the existing mature manipulator systems use the precision mechanical arm produced by well-known foreign manufacturers. The cost of purchasing and maintaining the system is very high, and it is difficult for the small and medium enterprises in our country to afford the high cost of the equipment. Therefore, it is of great practical significance to select the appropriate low cost manipulator system and put forward the control scheme of the manipulator which is suitable for the actual working conditions, which is of great practical significance for the promotion of small and medium-sized enterprises and the automation degree of the handicraft enterprises. In this paper, a class of manipulator with kinematics controller is selected as the research object, and three kinds of iterative learning controllers are designed to solve the control problems under different conditions. It provides a complete solution for the controller selection of this kind of manipulator system. The main contents of this paper are as follows: (1) A model-free PD type closed-loop iterative learning control algorithm is selected for the unknown manipulator model. Firstly, the iterative learning algorithm is very suitable to solve the trajectory tracking problem of nonlinear systems, so iterative learning control is chosen as the basic control scheme of the manipulator system. In this paper, the characteristics of closed-loop iterative learning algorithm and open-loop algorithm are compared, and a closed-loop iterative learning algorithm is selected, which is effective in suppressing environmental interference. Finally, considering the simple structure of pid controller, Because of the strong robustness and the integral effect of iterative learning process itself on the iterative axis, the PD type closed-loop iterative learning law is selected as the control algorithm for the manipulator system. The simulation results show that the algorithm is effective in solving the trajectory tracking problem of manipulator. (2) an iterative learning control algorithm based on fixed kinematics model is proposed to solve the problem of manipulator reference model. By using the reference model of the manipulator as a priori knowledge and using the reference joint angle obtained from the inverse kinematics solution to guide the iterative learning process, the algorithm can effectively accelerate the convergence rate of the tracking error of the system. The simulation results show that the convergence speed of the system error is improved obviously by using the control algorithm based on the fixed kinematics model. (3) the deviation between the reference model of the manipulator and the actual object is large. An iterative learning control algorithm based on adaptive model is proposed. In this algorithm, the parameters of kinematics model of manipulator are estimated online by Kalman filter, and the reference model of the object is updated after each iteration for the next iterative learning control. The simulation results show that the manipulator system can successfully complete the trajectory tracking task even if the object reference model mismatch is serious by using the adaptive model-based algorithm. (4) the hardware platform of the manipulator is built. On the basis of this platform, the iterative learning control algorithms without model and fixed model are tested. The experimental results show that the system output fluctuates due to observation errors, but with the iteration, the error between the output trajectory of the manipulator end actuator and the desired trajectory can be gradually reduced. In this paper, the error analysis of the above experimental results is carried out.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP241

【參考文獻】

相關(guān)期刊論文 前10條

1 陶洪峰;董曉齊;楊慧中;;參考軌跡更新的點到點迭代學(xué)習(xí)控制算法優(yōu)化及應(yīng)用[J];控制理論與應(yīng)用;2016年09期

2 孟明輝;周傳德;陳禮彬;馮淼;苗純正;;工業(yè)機器人的研發(fā)及應(yīng)用綜述[J];上海交通大學(xué)學(xué)報;2016年S1期

3 田國會;袁麗;李國棟;李巖;;結(jié)合迭代學(xué)習(xí)控制的視覺伺服物品抓取方法[J];華中科技大學(xué)學(xué)報(自然科學(xué)版);2015年S1期

4 白國振;荊鵬翔;;基于改進粒子群算法的并聯(lián)機械手運動學(xué)參數(shù)辨識[J];信息與控制;2015年05期

5 王琨;駱敏舟;曹毅;李可;張秋菊;;基于遺傳算法的串聯(lián)機械臂運動學(xué)參數(shù)標定[J];系統(tǒng)科學(xué)與數(shù)學(xué);2015年01期

6 翟敬梅;董鵬飛;張鐵;;基于視覺引導(dǎo)的工業(yè)機器人定位抓取系統(tǒng)設(shè)計[J];機械設(shè)計與研究;2014年05期

7 祁若龍;周維佳;王鐵軍;;一種基于遺傳算法的空間機械臂避障軌跡規(guī)劃方法[J];機器人;2014年03期

8 譚民;王碩;;機器人技術(shù)研究進展[J];自動化學(xué)報;2013年07期

9 尼加提·努爾買買提;楊慧中;陶洪峰;;卡爾曼濾波器在軟測量建模數(shù)據(jù)處理中的應(yīng)用[J];化工自動化及儀表;2013年05期

10 劉長良;賈萬根;;一般非線性離散系統(tǒng)P-D型迭代學(xué)習(xí)收斂性研究[J];電力科學(xué)與工程;2012年06期

，

本文編號：2061424