本文關(guān)鍵詞：基于自適應(yīng)神經(jīng)網(wǎng)絡(luò)的復(fù)雜機(jī)械臂控制研究　出處：《湖南工業(yè)大學(xué)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 機(jī)械臂 柔性關(guān)節(jié) 神經(jīng)網(wǎng)絡(luò) 觀測(cè)器 反演控制 奇異攝動(dòng) DSP

【摘要】：隨著科學(xué)技術(shù)的迅猛發(fā)展,機(jī)器人技術(shù)也得到飛速地發(fā)展,并廣泛應(yīng)用于各行各業(yè),如工業(yè)、航空航天、軍事、醫(yī)療等行業(yè)。而機(jī)械臂是通過(guò)模擬人的手臂的一種機(jī)械裝置,是機(jī)器人最主要的執(zhí)行機(jī)構(gòu)。機(jī)械臂系統(tǒng)本身就是一個(gè)非線性、強(qiáng)耦合、受干擾的復(fù)雜系統(tǒng)。而且,在實(shí)踐過(guò)程中,因工作情況非常復(fù)雜,很難建立精確的機(jī)械臂系統(tǒng)數(shù)學(xué)模型,比如負(fù)載的不確定,系統(tǒng)參數(shù)不確定,甚至完全無(wú)模型等情況。另外,從電機(jī)輸出軸到機(jī)械臂的執(zhí)行軸之間的傳動(dòng)系統(tǒng)不可避免地產(chǎn)生了柔性,所以考慮關(guān)節(jié)柔性的機(jī)械臂控制也成為了當(dāng)今研究的熱點(diǎn)和難點(diǎn)。除此之外,在工業(yè)應(yīng)用中的機(jī)械臂出于節(jié)約成本或減少測(cè)量誤差的考慮,一些狀態(tài)變量是無(wú)法測(cè)量的,設(shè)計(jì)觀測(cè)器也成為了在控制器的設(shè)計(jì)當(dāng)中一個(gè)重要部分。本論文的研究首先考慮系統(tǒng)參數(shù)不確定和部分狀態(tài)量不可測(cè)量的情況下研究出了一種基于一階濾波觀測(cè)器的滑模自適應(yīng)控制器。最后,根據(jù)實(shí)際過(guò)程中的完全無(wú)模型的情況下,設(shè)計(jì)了一種BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)觀測(cè)器,并且在此基礎(chǔ)上結(jié)合神經(jīng)網(wǎng)絡(luò)對(duì)非線性項(xiàng)的萬(wàn)能逼近原理,采用反演控制的方式實(shí)現(xiàn)了機(jī)械臂的運(yùn)動(dòng)軌跡跟蹤。其次,考慮機(jī)械臂關(guān)節(jié)的柔性(特別是關(guān)節(jié)剛度較小的場(chǎng)合)提出了基于柔性補(bǔ)償(其本質(zhì)是增加二次濾波的帶寬)的奇異攝動(dòng)控制方式的機(jī)械臂執(zhí)行端軌跡跟蹤控制器。針對(duì)完全無(wú)模型的情況下,提出了一種RBF神經(jīng)網(wǎng)絡(luò)的觀測(cè)器實(shí)現(xiàn)對(duì)不可測(cè)狀態(tài)向量的重構(gòu),并依此完成了PD控制器的設(shè)計(jì)。為了實(shí)現(xiàn)柔性機(jī)械臂的更進(jìn)一步地智能控制,通過(guò)結(jié)合神經(jīng)網(wǎng)絡(luò)的較強(qiáng)自學(xué)習(xí)以及聯(lián)想能力和模糊系統(tǒng)的易于理解推理過(guò)程這兩者的優(yōu)點(diǎn),提出一種模糊神經(jīng)網(wǎng)絡(luò)觀測(cè)器的狀態(tài)估計(jì)和未知非線性項(xiàng)的逼近,進(jìn)而利用反演控制實(shí)現(xiàn)機(jī)械臂的軌跡跟蹤控制,仿真結(jié)果也證明了該控制器的合理性與可行性。最后,通過(guò)搭建機(jī)械臂控制實(shí)驗(yàn)平臺(tái),實(shí)現(xiàn)DSP目標(biāo)板與機(jī)械臂伺服系統(tǒng)的數(shù)據(jù)通信,并最終在DSP中執(zhí)行控制算法并傳輸控制信號(hào)到伺服系統(tǒng),進(jìn)而傳送動(dòng)力控制機(jī)械臂完成軌跡跟蹤的目的。同時(shí),在不中斷DSP運(yùn)行的情況下,采用Matlab下的模塊CCSLink實(shí)現(xiàn)DSP、CCS和matlab實(shí)時(shí)交互數(shù)據(jù)。最終,通過(guò)實(shí)驗(yàn)結(jié)果更進(jìn)一步驗(yàn)證了控制策略的可行性與有效性。
[Abstract]:With the rapid development of science and technology, robot technology has also been rapidly developed, and widely used in various industries, such as industry, aerospace, military. The mechanical arm is a kind of mechanical device which simulates the human arm and is the most important actuator of the robot. The robot arm system itself is a nonlinear and strong coupling. In practice, it is very difficult to establish accurate mathematical model of manipulator system, such as uncertain load and uncertain system parameters. In addition, the transmission system from the motor output shaft to the actuator shaft of the manipulator inevitably produces flexibility. Therefore, the control of manipulator with flexible joints has become a hot and difficult point. In addition, in industrial applications, the robot arm is considered to save cost or reduce the measurement error. Some state variables are unmeasurable. The design of observer has become an important part of controller design. In this paper, first of all, considering the uncertainty of system parameters and the unmeasurable state of part of the system, a new method based on first-order filter observation is proposed. The sliding mode adaptive controller. Finally. In this paper, an adaptive observer of BP neural network is designed, and the universal approximation principle of neural network to nonlinear term is combined. The motion trajectory tracking of the manipulator is realized by inverse control. Secondly. Considering the flexibility of the manipulator joints (especially when the stiffness of the joints is small), a new method based on flexibility compensation is proposed (the essence of which is to increase the bandwidth of the secondary filtering). A singularly perturbed control method for the manipulator actuator trajectory tracking controller. For the case of no model. An observer based on RBF neural network is proposed to reconstruct the unmeasurable state vector, and the PD controller is designed in order to realize further intelligent control of the flexible manipulator. By combining the strong self-learning of neural networks and the advantages of associative ability and easy to understand the reasoning process of fuzzy systems, a fuzzy neural network observer state estimation and approximation of unknown nonlinear terms are proposed. Then the trajectory tracking control of the manipulator is realized by inverse control. The simulation results also prove the rationality and feasibility of the controller. Finally, the manipulator control experimental platform is built. The data communication between the DSP target board and the manipulator servo system is realized. Finally, the control algorithm is implemented in DSP and the control signal is transmitted to the servo system. At the same time, under the condition of not interrupting the DSP running, the module CCSLink under Matlab is used to realize the DSP. CCS and matlab interact with each other in real time. Finally, the feasibility and effectiveness of the control strategy are further verified by the experimental results.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP241;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李光;符浩;;無(wú)模型機(jī)械臂BP神經(jīng)網(wǎng)絡(luò)狀態(tài)觀測(cè)及反演跟蹤控制[J];中國(guó)機(jī)械工程;2016年07期

2 李亞中;杜榮華;舒剛?cè)A;;基于改進(jìn)最小二乘法的永磁無(wú)刷電機(jī)參數(shù)辨識(shí)[J];公路與汽運(yùn);2015年03期

3 梁捷;陳力;;柔性關(guān)節(jié)-柔性臂空間機(jī)器人的神經(jīng)網(wǎng)絡(luò)自適應(yīng)反演控制及雙重柔性振動(dòng)抑制[J];計(jì)算力學(xué)學(xué)報(bào);2014年04期

4 楊劍鋒;張翠;張峰;;機(jī)械臂軌跡跟蹤控制——基于EC-RBF神經(jīng)網(wǎng)絡(luò)的機(jī)械臂模型參考自適應(yīng)控制[J];計(jì)算機(jī)工程與應(yīng)用;2015年09期

5 賈鶴鳴;宋文龍;郭少彬;楊立新;;基于神經(jīng)網(wǎng)絡(luò)的機(jī)械臂自適應(yīng)輸出反饋控制設(shè)計(jì)[J];應(yīng)用科學(xué)學(xué)報(bào);2013年04期

6 吳玉香;張景;王聰;;機(jī)械臂的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制與學(xué)習(xí)[J];機(jī)械工程學(xué)報(bào);2013年15期

7 孫敬構(gòu);史士財(cái);王學(xué)飛;王達(dá);劉宏;;大型空間機(jī)械臂柔性關(guān)節(jié)的微分幾何算法控制器設(shè)計(jì)[J];哈爾濱工程大學(xué)學(xué)報(bào);2012年11期

8 張洪東;李宏;鄭勇;;基于遞推最小二乘法的永磁同步電動(dòng)機(jī)參數(shù)辨識(shí)[J];微特電機(jī);2011年11期

9 徐志超;周玉國(guó);于鳳滿;;基于改進(jìn)神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)觀測(cè)器設(shè)計(jì)[J];微型機(jī)與應(yīng)用;2011年08期

10 黨進(jìn);倪風(fēng)雷;劉業(yè)超;劉宏;;基于新型補(bǔ)償控制策略的柔性關(guān)節(jié)控制器設(shè)計(jì)[J];機(jī)器人;2011年02期

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