本文選題：機(jī)械臂系統(tǒng) + 運(yùn)動(dòng)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(guò)程中,存在著大量高強(qiáng)度的重復(fù)性工作,這類工作適合使用機(jī)械臂系統(tǒng)代替人類完成。在汽車、電子等行業(yè)中,機(jī)械臂已經(jīng)被大量投入使用,相關(guān)的機(jī)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(jī)械臂系統(tǒng)大多使用國(guó)外知名廠商生產(chǎn)的精密機(jī)械臂,系統(tǒng)的購(gòu)買和維護(hù)成本很高,我國(guó)中小型企業(yè)難以負(fù)擔(dān)設(shè)備引入的高額成本。因此,選取合適的低成本機(jī)械臂系統(tǒng)、提出適合于實(shí)際工況的機(jī)械臂控制方案,對(duì)于提升中小型輕、手工業(yè)企業(yè)的自動(dòng)化程度有很大的實(shí)際意義。本文選取一類配備運(yùn)動(dòng)學(xué)控制器的機(jī)械臂系統(tǒng)作為研究對(duì)象,設(shè)計(jì)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(wèn)題,為這一類機(jī)械臂系統(tǒng)的控制器選取問(wèn)題提供了一套比較完整的解決方案。本文的主要研究?jī)?nèi)容如下:(1)針對(duì)機(jī)械臂對(duì)象模型未知的情況,選取了 一種無(wú)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(wèn)題,因此選取迭代學(xué)習(xí)控制作為機(jī)械臂系統(tǒng)的基本控制方案;之后,本文對(duì)比了閉環(huán)迭代學(xué)習(xí)算法與開(kāi)環(huán)算法的特點(diǎn),選取了對(duì)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(jiǎn)單、魯棒性強(qiáng)的特點(diǎn)以及迭代學(xué)習(xí)過(guò)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(jī)械臂系統(tǒng)的控制算法。仿真實(shí)驗(yàn)證明了該算法對(duì)于解決機(jī)械臂軌跡跟蹤問(wèn)題的有效性。(2)針對(duì)機(jī)械臂對(duì)象參考模型已知的情況,提出了一種基于固定運(yùn)動(dòng)學(xué)模型的迭代學(xué)習(xí)控制算法。通過(guò)使用機(jī)械臂對(duì)象的參考模型作為先驗(yàn)知識(shí),利用運(yùn)動(dòng)學(xué)逆解得到的參考關(guān)節(jié)角指導(dǎo)迭代學(xué)習(xí)過(guò)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(guò)使用基于固定運(yùn)動(dòng)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(duì)機(jī)械臂參考模型與實(shí)際對(duì)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(duì)機(jī)械臂運(yùn)動(dòng)學(xué)模型參數(shù)進(jìn)行在線估計(jì),每次迭代后更新對(duì)象的參考模型用于下一步迭代學(xué)習(xí)控制。仿真實(shí)驗(yàn)結(jié)果表明,通過(guò)使用基于自適應(yīng)模型的算法,機(jī)械臂系統(tǒng)在對(duì)象參考模型失配較為嚴(yán)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(jī)械臂硬件平臺(tái),并基于該平臺(tái)對(duì)上文中的無(wú)模型與基于固定模型的迭代學(xué)習(xí)控制算法進(jìn)行了初步實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果顯示,由于觀測(cè)誤差等因素的存在,系統(tǒng)輸出存在一定波動(dòng);但是隨著迭代的進(jìn)行,機(jī)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(duì)上述實(shí)驗(yàn)結(jié)果進(jìn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP241

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