本文選題：單神經(jīng)元PID + 反饋補(bǔ)償　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：欠驅(qū)動(dòng)系統(tǒng)指的是驅(qū)動(dòng)維數(shù)少于自由度維數(shù)的系統(tǒng),區(qū)別于二者相等的全驅(qū)動(dòng)系統(tǒng)。在控制理論中,二者均是控制系統(tǒng)的重要分類。欠驅(qū)動(dòng)系統(tǒng)具有許多諸如節(jié)省能源、節(jié)約成本和提高自由度等優(yōu)點(diǎn),同時(shí)有些特定的工作環(huán)境下工作或者有特定功能的機(jī)械裝置本身就需要使用欠驅(qū)動(dòng)結(jié)構(gòu),而且在某些全驅(qū)動(dòng)機(jī)構(gòu)發(fā)生故障的之后,全驅(qū)動(dòng)系統(tǒng)也可能變?yōu)榍夫?qū)動(dòng)系統(tǒng),所以欠驅(qū)動(dòng)系統(tǒng)的應(yīng)用越來(lái)越廣泛,研究成果也越來(lái)越多。但是由于其欠驅(qū)動(dòng)的特性,在控制中的難度相對(duì)較大,在控制過(guò)程中,既要讓全驅(qū)動(dòng)部分得到控制,又要讓欠驅(qū)動(dòng)部分能夠一直保持平衡,這為控制器的設(shè)計(jì)增加了難度。因而,在這方面的研究和關(guān)注一直很多。為了拓展欠驅(qū)動(dòng)系統(tǒng)的研究成果,針對(duì)欠驅(qū)動(dòng)系統(tǒng)開(kāi)發(fā)更多控制方法,本文對(duì)欠驅(qū)動(dòng)系統(tǒng)進(jìn)行了研究,主要研究了兩種控制方法:第一種方法是單神經(jīng)元PID反饋補(bǔ)償控制結(jié)構(gòu)。該方法屬于智能控制的范圍,選取了單神經(jīng)元PID作為主體控制器,加入了單神經(jīng)元PID辨識(shí)器,二者分工協(xié)作,由單神經(jīng)元PID辨識(shí)器根據(jù)實(shí)施情況進(jìn)行在線學(xué)習(xí),將每一個(gè)周期學(xué)習(xí)得到的參數(shù)傳遞給單神經(jīng)元PID控制器,單神經(jīng)元PID控制器采用該參數(shù)作為權(quán)值,運(yùn)用到內(nèi)部算法之中。由此,既保證了智能控制的優(yōu)點(diǎn),又可以避免離線學(xué)習(xí)。此外,在整個(gè)控制結(jié)構(gòu)之中,加入了傳統(tǒng)的PID控制結(jié)構(gòu)。PID控制器和單神經(jīng)元PID控制器處在并行的結(jié)構(gòu),在控制的初期,單神經(jīng)元PID結(jié)構(gòu)需要時(shí)間進(jìn)行學(xué)習(xí),而此時(shí)單神經(jīng)元的參數(shù)是不適用于系統(tǒng)的,此時(shí)主要以PID控制器為主要控制結(jié)構(gòu),有效地避免了人工神經(jīng)網(wǎng)絡(luò)控制前期的紊亂。同時(shí),在系統(tǒng)受到干擾的時(shí)候,該結(jié)構(gòu)也可以增加系統(tǒng)整體的抵抗性,增加魯棒性。并且使用MATLAB的Simulink模塊進(jìn)行仿真,使用該方法對(duì)一個(gè)欠驅(qū)動(dòng)系統(tǒng)進(jìn)行控制,和其他控制方法進(jìn)行仿真比較,對(duì)比各方法的結(jié)果,分析優(yōu)越性。第二種方法是直接參數(shù)反饋線性化方法。該方法運(yùn)用了在處理非線性系統(tǒng)時(shí)常用的反饋線性化方法,通過(guò)構(gòu)造控制器,將輸出信號(hào)通過(guò)一定改動(dòng)反饋回系統(tǒng)之中,將原系統(tǒng)中的非線性成分抵消掉,從而使得系統(tǒng)從非線性轉(zhuǎn)換為線性,從而降低控制難度,可以使用線性控制的相關(guān)理論進(jìn)行控制,從而控制欠驅(qū)動(dòng)系統(tǒng)。二階動(dòng)力學(xué)系統(tǒng)是常見(jiàn)的系統(tǒng)結(jié)構(gòu),時(shí)常應(yīng)用于各類機(jī)械系統(tǒng)之中。本方法針對(duì)一類二階非線性欠驅(qū)動(dòng)系統(tǒng)進(jìn)行研究,使用了直接參數(shù)化方法,使得得到的算法能夠直接針對(duì)原系統(tǒng)參數(shù)矩陣進(jìn)行操作,降低了系統(tǒng)維數(shù),減少了運(yùn)算量,增加了數(shù)值穩(wěn)定性。
[Abstract]:Underactuated system refers to a system with less drive dimension than a degree of freedom, which is different from a full drive system with equal drive dimensions. In control theory, both of them are important classification of control system. Underactuated systems have many advantages, such as saving energy, saving costs and increasing degrees of freedom, while some mechanical devices that work in a particular working environment or have a specific function need to use underactuated structures. After the failure of some full drive mechanism, the full drive system may become underactuated system, so the application of underactuated system is more and more extensive, and the research results are more and more. However, due to its underactuated characteristics, it is relatively difficult in the control process. In the control process, it is necessary to let the full drive part be controlled and the underactuated part to keep the balance all the time, which increases the difficulty of the controller design. As a result, there has been a lot of research and attention in this area. In order to extend the research results of underactuated system and to develop more control methods for underactuated system, two control methods are studied in this paper. The first method is single neuron PID feedback compensation control structure. This method belongs to the scope of intelligent control. The single neuron PID is selected as the main controller, and the single neuron PID identifier is added. The parameters of each cycle are transferred to the single neuron PID controller, and the single neuron PID controller uses this parameter as the weight value and applies it to the internal algorithm. This not only ensures the advantages of intelligent control, but also avoids off-line learning. In addition, in the whole control structure, the traditional PID control structure. Pid controller and single neuron PID controller are in parallel structure. In the early stage of the control, the single neuron PID structure needs time to learn. At this time, the parameters of single neuron are not suitable for the system. In this case, the PID controller is the main control structure, which effectively avoids the disturbance in the early stage of artificial neural network control. At the same time, when the system is disturbed, the structure can increase the overall resistance and robustness of the system. The Simulink module of MATLAB is used to simulate, and the method is used to control an underactuated system. Compared with other control methods, the results of each method are compared and the advantages are analyzed. The second method is direct parameter feedback linearization. In this method, the feedback linearization method is used in dealing with nonlinear systems. By constructing a controller, the output signal is fed back to the system by certain changes, and the nonlinear components in the original system are cancelled out. Thus, the system can be transformed from nonlinear to linear, thus reducing the difficulty of control. The theory of linear control can be used to control the underactuated system. Second-order dynamical system is a common system structure, which is often used in various mechanical systems. In this method, a class of second order nonlinear underactuated systems is studied, and a direct parameterization method is used, which enables the algorithm to operate directly on the original system parameter matrix, thus reducing the system dimension and the computational complexity. Numerical stability is increased.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

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