發(fā)布時間：2018-01-04 17:41

  本文關(guān)鍵詞：基于自適應神經(jīng)網(wǎng)絡(luò)的復雜機械臂控制研究　出處：《湖南工業(yè)大學》2016年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 機械臂 柔性關(guān)節(jié) 神經(jīng)網(wǎng)絡(luò) 觀測器 反演控制 奇異攝動 DSP

【摘要】：隨著科學技術(shù)的迅猛發(fā)展,機器人技術(shù)也得到飛速地發(fā)展,并廣泛應用于各行各業(yè),如工業(yè)、航空航天、軍事、醫(yī)療等行業(yè)。而機械臂是通過模擬人的手臂的一種機械裝置,是機器人最主要的執(zhí)行機構(gòu)。機械臂系統(tǒng)本身就是一個非線性、強耦合、受干擾的復雜系統(tǒng)。而且,在實踐過程中,因工作情況非常復雜,很難建立精確的機械臂系統(tǒng)數(shù)學模型,比如負載的不確定,系統(tǒng)參數(shù)不確定,甚至完全無模型等情況。另外,從電機輸出軸到機械臂的執(zhí)行軸之間的傳動系統(tǒng)不可避免地產(chǎn)生了柔性,所以考慮關(guān)節(jié)柔性的機械臂控制也成為了當今研究的熱點和難點。除此之外,在工業(yè)應用中的機械臂出于節(jié)約成本或減少測量誤差的考慮,一些狀態(tài)變量是無法測量的,設(shè)計觀測器也成為了在控制器的設(shè)計當中一個重要部分。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)量不可測量的情況下研究出了一種基于一階濾波觀測器的滑模自適應控制器。最后,根據(jù)實際過程中的完全無模型的情況下,設(shè)計了一種BP神經(jīng)網(wǎng)絡(luò)的自適應觀測器,并且在此基礎(chǔ)上結(jié)合神經(jīng)網(wǎng)絡(luò)對非線性項的萬能逼近原理,采用反演控制的方式實現(xiàn)了機械臂的運動軌跡跟蹤。其次,考慮機械臂關(guān)節(jié)的柔性(特別是關(guān)節(jié)剛度較小的場合)提出了基于柔性補償(其本質(zhì)是增加二次濾波的帶寬)的奇異攝動控制方式的機械臂執(zhí)行端軌跡跟蹤控制器。針對完全無模型的情況下,提出了一種RBF神經(jīng)網(wǎng)絡(luò)的觀測器實現(xiàn)對不可測狀態(tài)向量的重構(gòu),并依此完成了PD控制器的設(shè)計。為了實現(xiàn)柔性機械臂的更進一步地智能控制,通過結(jié)合神經(jīng)網(wǎng)絡(luò)的較強自學習以及聯(lián)想能力和模糊系統(tǒng)的易于理解推理過程這兩者的優(yōu)點,提出一種模糊神經(jīng)網(wǎng)絡(luò)觀測器的狀態(tài)估計和未知非線性項的逼近,進而利用反演控制實現(xiàn)機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,通過搭建機械臂控制實驗平臺,實現(xiàn)DSP目標板與機械臂伺服系統(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到伺服系統(tǒng),進而傳送動力控制機械臂完成軌跡跟蹤的目的。同時,在不中斷DSP運行的情況下,采用Matlab下的模塊CCSLink實現(xiàn)DSP、CCS和matlab實時交互數(shù)據(jù)。最終,通過實驗結(jié)果更進一步驗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.
【學位授予單位】：湖南工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP241;TP183

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本文編號：1379459