本文關(guān)鍵詞： 機(jī)器人控制 永磁同步電機(jī) 模糊神經(jīng)網(wǎng)絡(luò)控制 滑模控制 位置控制與軌跡規(guī)劃　出處：《青島大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：機(jī)器人是一種由各種電機(jī)驅(qū)動(dòng)的裝置,驅(qū)動(dòng)電機(jī)的工作性能影響著機(jī)器人的工作性能。然而,傳統(tǒng)研究中很多機(jī)器人控制方法的研究只對(duì)機(jī)器人動(dòng)力學(xué)進(jìn)行研究,沒有考慮電機(jī)的因素。本文將關(guān)節(jié)機(jī)器人模型的控制方法研究和驅(qū)動(dòng)電機(jī)運(yùn)動(dòng)控制的研究結(jié)合。永磁同步電機(jī)因結(jié)構(gòu)簡單、運(yùn)行可靠度高和維護(hù)方便等諸多優(yōu)點(diǎn),在本文機(jī)器人關(guān)節(jié)伺服系統(tǒng)中得到應(yīng)用。機(jī)器人具有較強(qiáng)的非線性和不確定性,給機(jī)器人的控制研究帶來很多困難。為了解決這個(gè)問題,本文以二自由度關(guān)節(jié)機(jī)器人為研究對(duì)象,將永磁同步電機(jī)作為機(jī)器人關(guān)節(jié)的驅(qū)動(dòng)電機(jī)。本文研究了一種PD加重力補(bǔ)償?shù)目刂品椒?該方法在PD控制器的基礎(chǔ)上引進(jìn)重力補(bǔ)償項(xiàng),能夠加快系統(tǒng)的響應(yīng)速度,減少達(dá)到穩(wěn)定狀態(tài)的時(shí)間。電機(jī)控制器中選用滑�？刂品椒�,這種方法對(duì)電機(jī)具有良好的控制效果。仿真結(jié)果表明,基于PD重力補(bǔ)償?shù)臋C(jī)器人控制系統(tǒng)具有良好的響應(yīng)特性和跟蹤能力。針對(duì)PD加重力補(bǔ)償控制中運(yùn)動(dòng)響應(yīng)和跟蹤能力的一些不足,提高機(jī)器人的控制效果,研究了一種基于模糊神經(jīng)網(wǎng)絡(luò)的機(jī)器人控制方法。模糊神經(jīng)網(wǎng)絡(luò)是一種兼具模糊控制和神經(jīng)網(wǎng)絡(luò)控制各自優(yōu)點(diǎn)的控制方法。由于其良好的控制效果和學(xué)習(xí)能力,可替代PD加重力補(bǔ)償控制,能夠解決機(jī)器人控制中的非線性和不確定的問題。在使用模糊神經(jīng)網(wǎng)絡(luò)控制時(shí),先對(duì)機(jī)器人動(dòng)力學(xué)進(jìn)行分離,將其中的不確定項(xiàng)分離,然后利用模糊神經(jīng)網(wǎng)絡(luò)的方法進(jìn)行控制。同時(shí)為了減小滑�？刂圃斐傻亩秳�(dòng),對(duì)上一部分提出的滑�？刂品椒ㄟM(jìn)行了一些改進(jìn)。仿真結(jié)果表明,對(duì)比PD加重力補(bǔ)償控制,系統(tǒng)的跟蹤和響應(yīng)能力得到顯著提高。機(jī)器人在運(yùn)動(dòng)時(shí)容易受到外界的各種干擾,一個(gè)具有良好性能機(jī)器人伺服系統(tǒng)不僅要求系統(tǒng)能夠準(zhǔn)確迅速的跟蹤輸入值,而且要求系統(tǒng)能有較好的抗擾動(dòng)能力。為了解決這個(gè)問題,本文研究了帶有負(fù)載轉(zhuǎn)矩觀測器的機(jī)器人關(guān)節(jié)控制方法。一般情況下可以將外部擾動(dòng)視為控制系統(tǒng)負(fù)載轉(zhuǎn)矩的變化。所以將在模糊神經(jīng)網(wǎng)絡(luò)控制器的基礎(chǔ)上增加負(fù)載轉(zhuǎn)矩觀測,提高系統(tǒng)的抗干擾能力。仿真結(jié)果表明,該控制方法在受到轉(zhuǎn)矩?cái)_動(dòng)時(shí),受到的影響較小,具有良好的抗干擾能力。綜上,為了解決機(jī)器人的非線性和不確定問題,提高系統(tǒng)的快速響應(yīng)跟蹤能力和抗干擾能力,本文研究了幾種不同的控制器。通過對(duì)不同控制器的控制效果進(jìn)行對(duì)比分析,對(duì)系統(tǒng)進(jìn)行相應(yīng)的改進(jìn)。本文最后研究的帶有負(fù)載轉(zhuǎn)矩觀測器的基于模糊神經(jīng)網(wǎng)絡(luò)和滑�？刂频臋C(jī)器人伺服控制系統(tǒng),不僅具有良好的快速響應(yīng)和跟蹤能力,而且具有良好的抗干擾能力達(dá)到了設(shè)計(jì)要求。
[Abstract]:Robot is a kind of device driven by a variety of motors. The working performance of the driving motor affects the performance of the robot. However, many traditional research methods of robot control only study the dynamics of the robot. This paper combines the control method of joint robot model with the motion control of driving motor. The permanent magnet synchronous motor has many advantages, such as simple structure, high operation reliability and convenient maintenance, etc. The robot has strong nonlinearity and uncertainty, which brings a lot of difficulties to the research of robot control. In order to solve this problem, In this paper, the permanent magnet synchronous motor (PMSM) is used as the driving motor of the robot joints, and a control method of PD weighting force compensation is studied, which introduces the gravity compensation term based on the PD controller. It can speed up the response speed of the system and reduce the time to reach the stable state. The sliding mode control method is used in the motor controller, which has a good control effect on the motor. The simulation results show that, The robot control system based on PD gravity compensation has good response characteristics and tracking ability. In this paper, a robot control method based on fuzzy neural network is studied. Fuzzy neural network is a control method with the advantages of both fuzzy control and neural network control. Instead of PD weighting force compensation control, it can solve the nonlinear and uncertain problems in robot control. In the use of fuzzy neural network control, the dynamics of robot is separated first, and the uncertainty is separated. Then the fuzzy neural network is used to control it. In order to reduce the jitter caused by sliding mode control, some improvements are made to the sliding mode control method proposed in the previous part. The simulation results show that the PD weighting compensation control is compared with PD weighting force compensation control. The tracking and response ability of the system has been greatly improved. The robot is vulnerable to various kinds of interference when it moves. A robot servo system with good performance not only requires the system to track the input value accurately and rapidly. In order to solve this problem, the system is required to have better anti-disturbance ability. In this paper, a robot joint control method with a load torque observer is studied. In general, the external disturbance can be regarded as the change of the load torque of the control system. Therefore, the load torque observation will be added to the fuzzy neural network controller. The simulation results show that the control method is less affected by torque disturbance and has good anti-jamming ability. In order to solve the nonlinear and uncertain problems of the robot, the simulation results show that the proposed control method has good anti-interference ability. In order to improve the ability of fast response tracking and anti-jamming, several kinds of controllers are studied in this paper, and the control effects of different controllers are compared and analyzed. Finally, the robot servo control system with load torque observer based on fuzzy neural network and sliding mode control is studied. And has good anti-interference ability to meet the design requirements.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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