本文關(guān)鍵詞： 自抗擾控制器 轉(zhuǎn)動慣量辨識 負(fù)載轉(zhuǎn)矩觀測 辨識補(bǔ)償 轉(zhuǎn)速估計(jì)　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：永磁同步電機(jī)具有高功率密度,體積小,散熱好等一系列優(yōu)點(diǎn)。目前,永磁同步電機(jī)已在高精度、高速伺服領(lǐng)域扮演重要的角色。永磁同步電機(jī)得到廣泛應(yīng)用的同時也對電機(jī)控制性能提出更高的要求,尤其對控制系統(tǒng)的抗擾性能提出更大的挑戰(zhàn)。為抑制負(fù)載變化給電機(jī)輸出轉(zhuǎn)速帶來的影響,本文對基于自抗擾控制技術(shù)的永磁同步電機(jī)控制策略進(jìn)行研究,采用了一種Gopinath觀測器補(bǔ)償自抗擾控制器的方案。對轉(zhuǎn)速環(huán)和電流環(huán)采用自抗擾控制器的永磁同步電機(jī)矢量控制系統(tǒng)進(jìn)行分析,電機(jī)控制問題中自抗擾控制技術(shù)相對于傳統(tǒng)PI控制具有更好的動態(tài)響應(yīng)性能和對內(nèi)外擾動抑制效果。本文對于自抗擾控制器參數(shù)整定問題:對線性結(jié)構(gòu)自抗擾控制器的控制參數(shù)整定,通過推導(dǎo)其系統(tǒng)閉環(huán)特征方程完成。對非線性結(jié)構(gòu)自抗擾控制器的控制參數(shù)整定,采用免疫粒子群算法完成。為減輕擴(kuò)張狀態(tài)觀測器負(fù)擔(dān),進(jìn)一步提高控制系統(tǒng)抗擾性能,本文對傳統(tǒng)自抗擾控制器進(jìn)行辨識補(bǔ)償,辨識補(bǔ)償?shù)奈锢砹堪ㄓ来磐诫姍C(jī)的轉(zhuǎn)動慣量和負(fù)載轉(zhuǎn)矩。轉(zhuǎn)動慣量辨識方面,分析對比了三種在線辨識方法:帶遺忘因子的最小二乘法、基于波波夫超穩(wěn)定性的模型參考自適應(yīng)、基于郎道離散迭代的模型參考自適應(yīng)算法。通過選擇合理的增益可以準(zhǔn)確地在線辨識轉(zhuǎn)動慣量。負(fù)載轉(zhuǎn)矩觀測方面,常用的在線觀測方法采用降階觀測器算法對狀態(tài)量進(jìn)行重構(gòu),本文根據(jù)所采用的方案特點(diǎn),采用Gopinath觀測器對負(fù)載轉(zhuǎn)矩進(jìn)行觀測,通過穩(wěn)定性分析選擇合理控制參數(shù)可以準(zhǔn)確對負(fù)載轉(zhuǎn)矩進(jìn)行觀測。對于不適合安裝轉(zhuǎn)速編碼器的特殊場合,本文采用模型參考自適應(yīng)算法實(shí)現(xiàn)基于自抗擾控制技術(shù)的永磁同步電機(jī)無速度傳感器控制,使用的無速度傳感器控制算法結(jié)構(gòu)簡單易實(shí)現(xiàn),系統(tǒng)性能可滿足基本要求。實(shí)驗(yàn)部分采用PESX-Ⅱ電力電子與電力傳動實(shí)驗(yàn)平臺,以TMS320F2812為控制核心。對采用PI控制、自抗擾控制、辨識補(bǔ)償自抗擾控制的永磁同步電機(jī)控制方案進(jìn)行驗(yàn)證,通過分析對比實(shí)驗(yàn)結(jié)果與仿真結(jié)果來驗(yàn)證采用辨識補(bǔ)償自抗擾控制方案的有效性和優(yōu)越性,方案可應(yīng)用于實(shí)際控制中。
[Abstract]:Permanent magnet synchronous motor (PMSM) has a series of advantages such as high power density, small size, good heat dissipation and so on. The field of high speed servo plays an important role. The permanent magnet synchronous motor (PMSM) has been widely used, and the control performance of PMSM is also required to be higher. In order to restrain the influence of load variation on motor output speed, the control strategy of PMSM based on ADRC is studied in this paper. A scheme of Gopinath observer compensation ADRC is adopted. The vector control system of permanent magnet synchronous motor (PMSM) with active disturbance rejection controller in speed loop and current loop is analyzed. The ADRC technique in motor control problem has better dynamic response performance compared with the traditional Pi control, and it can restrain the internal and external disturbance. In this paper, the parameter tuning problem of the ADRC controller and the linear structure ADRC control are discussed. The control parameters of the machine are set, By deducing the closed-loop characteristic equation of the system, the immune particle swarm optimization algorithm is used to adjust the control parameters of the nonlinear structure ADRC. In order to reduce the burden of the extended state observer, the disturbance rejection performance of the control system is further improved. In this paper, the traditional ADRC is identified and compensated. The physical parameters of identification compensation include moment of inertia of permanent magnet synchronous motor, load torque and moment of inertia identification. Three online identification methods are analyzed and compared: least square method with forgetting factor and model reference adaptation based on Popov hyperstability. A model reference adaptive algorithm based on Lang Dao discrete iteration is proposed. The moment of inertia can be accurately identified online by selecting a reasonable gain. The commonly used on-line observation method uses the reduced order observer algorithm to reconstruct the state quantity. According to the characteristic of the scheme, this paper uses the Gopinath observer to observe the load torque. The load torque can be accurately observed by selecting reasonable control parameters through stability analysis. In this paper, the speed sensorless control of PMSM based on ADRC is realized by using model reference adaptive algorithm. The speed sensorless control algorithm used in PMSM is simple and easy to realize. The performance of the system can meet the basic requirements. In the experiment part, PESX- 鈪，

本文編號：1505162