發(fā)布時(shí)間：2018-06-21 10:38

  本文選題：永磁同步電機(jī) + 不確定性　； 參考：《沈陽(yáng)工業(yè)大學(xué)》2017年碩士論文

【摘要】：永磁同步電機(jī)(PMSM)以其體積小、結(jié)構(gòu)簡(jiǎn)單、轉(zhuǎn)動(dòng)慣量低、功率密度高等優(yōu)點(diǎn)在工業(yè)機(jī)器人、高精度數(shù)控機(jī)床等伺服控制領(lǐng)域獲得了廣泛的應(yīng)用。迭代學(xué)習(xí)控制(ILC)適用于執(zhí)行重復(fù)任務(wù)的伺服控制系統(tǒng),理論上可以達(dá)到完美的跟蹤效果。然而實(shí)際系統(tǒng)中存在的各類(lèi)擾動(dòng)、建模誤差、參數(shù)時(shí)變等不確定性會(huì)對(duì)ILC的收斂性和系統(tǒng)的跟蹤性產(chǎn)生不利影響。因此針對(duì)PMSM伺服系統(tǒng)在ILC過(guò)程中處理不確定性問(wèn)題方面的缺陷進(jìn)行研究,采用自適應(yīng)控制與ILC相結(jié)合的方法,即自適應(yīng)迭代學(xué)習(xí)控制(AILC),其兼具了ILC解決重復(fù)跟蹤問(wèn)題和自適應(yīng)控制解決系統(tǒng)不確定性問(wèn)題上的雙重優(yōu)勢(shì),提高了PMSM伺服系統(tǒng)的跟蹤精度,并加快了系統(tǒng)的收斂速度。首先,介紹了PMSM的結(jié)構(gòu)及分類(lèi),根據(jù)坐標(biāo)變換建立了PMSM在dq坐標(biāo)系下的數(shù)學(xué)模型。采用di=0控制建立了PMSM矢量控制系統(tǒng)并分析了摩擦轉(zhuǎn)矩、齒槽轉(zhuǎn)矩、模型誤差、參數(shù)時(shí)變等不確定因素對(duì)控制系統(tǒng)的影響,建立了系統(tǒng)的狀態(tài)方程。其次,針對(duì)PMSM伺服系統(tǒng)受模型不確定性影響導(dǎo)致的跟蹤精度下降,誤差發(fā)散等問(wèn)題,提出了一種參數(shù)自適應(yīng)迭代學(xué)習(xí)律。其主要是在PD反饋控制的基礎(chǔ)上增加自適應(yīng)迭代項(xiàng),通過(guò)沿迭代軸進(jìn)行參數(shù)學(xué)習(xí),辨識(shí)出控制律未知參數(shù)。然后提出一種改進(jìn)型自適應(yīng)迭代學(xué)習(xí)律,相當(dāng)于在前一種學(xué)習(xí)律的基礎(chǔ)上增加了對(duì)未知參數(shù)的時(shí)域估計(jì),充分利用了時(shí)域和迭代域的信息�；贚yapunov穩(wěn)定性理論,分析了兩種方案的收斂性。仿真結(jié)果表明,在存在模型不確定性的情況下,AILC比傳統(tǒng)型ILC收斂速度快,跟蹤精度高,可有效改善系統(tǒng)的性能。最后,針對(duì)PMSM伺服系統(tǒng)運(yùn)行過(guò)程中受動(dòng)態(tài)參數(shù)不確定性影響的問(wèn)題,提出一種L1自適應(yīng)反饋與ILC相結(jié)合的方法。其中L1自適應(yīng)控制器用來(lái)處理系統(tǒng)的動(dòng)態(tài)參數(shù)不確定性,并對(duì)時(shí)域上的外加擾動(dòng)進(jìn)行補(bǔ)償以便于學(xué)習(xí)控制器可以在標(biāo)稱(chēng)系統(tǒng)上設(shè)計(jì)。而學(xué)習(xí)控制器用來(lái)提高系統(tǒng)對(duì)周期性輸入的跟蹤能力,并對(duì)摩擦、齒槽轉(zhuǎn)矩等迭代域重復(fù)不確定性進(jìn)行補(bǔ)償。仿真結(jié)果表明,該方案對(duì)于減小系統(tǒng)動(dòng)態(tài)參數(shù)不確定性對(duì)系統(tǒng)的影響,提高系統(tǒng)的性能有良好的效果。
[Abstract]:PMSM (permanent Magnet synchronous Motor) has been widely used in the servo control fields such as industrial robot, high precision CNC machine tool and so on because of its advantages of small size, simple structure, low moment of inertia and high power density. Iterative learning control (ILC) is suitable for servo control systems that perform repetitive tasks and can achieve perfect tracking results in theory. However, uncertainties such as disturbances, modeling errors and time-varying parameters in the actual system will adversely affect the convergence of ILC and the tracking of the system. Therefore, aiming at the shortcomings of PMSM servo system in dealing with uncertain problems in ILC process, the method of combining adaptive control with ILC is adopted. That is adaptive iterative learning control (AILC), which has the dual advantages of ILC to solve the repeated tracking problem and adaptive control to the uncertainty problem of the system, improves the tracking accuracy of the PMSM servo system, and accelerates the convergence speed of the system. Firstly, the structure and classification of PMSM are introduced, and the mathematical model of PMSM in dq coordinate system is established according to coordinate transformation. The di=0 vector control system is established, and the influence of the uncertain factors such as friction torque, slot torque, model error and time-varying parameters on the control system is analyzed, and the state equation of the system is established. Secondly, a parameter adaptive iterative learning law is proposed for PMSM servo system, which is caused by the uncertainty of the model, such as the decrease of tracking accuracy and the divergence of error. On the basis of PD feedback control, the adaptive iteration term is added, and the unknown parameters of the control law are identified by learning the parameters along the iterative axis. Then an improved adaptive iterative learning law is proposed which is equivalent to adding the time domain estimation of unknown parameters on the basis of the former learning law and making full use of the information in the time domain and iteration domain. Based on Lyapunov stability theory, the convergence of two schemes is analyzed. The simulation results show that AILC has faster convergence speed and higher tracking accuracy than traditional ILC in the presence of model uncertainty and can effectively improve the performance of the system. Finally, a method of combining L1 adaptive feedback with ILC is proposed to solve the problem of dynamic parameter uncertainty in PMSM servo system. The L1 adaptive controller is used to deal with the dynamic parameter uncertainty of the system and to compensate the external disturbance in the time domain so that the learning controller can be designed on the nominal system. The learning controller is used to improve the tracking ability of the system to the periodic input, and to compensate for the repeated uncertainties in the iterative domain such as friction, slot torque and so on. The simulation results show that this scheme can reduce the influence of dynamic parameter uncertainty on the system and improve the performance of the system.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM341;TM921.541

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