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Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

機(jī)譯：基于線性光學(xué)尺傳感器和霍爾傳感器的線性永磁同步電機(jī)的精密運(yùn)動(dòng)控制

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The linear optical-ruler sensor with 1 μm precision mounted in the linear permanent magnet synchronous machine (LPMSM) is used for measuring the mover position of LPMSM in order to enhance the precision of a measured mover position. Due to nonlinear friction and uncertainty effects, linear controllers are very hard to achieve good mover positioning of LPMSM. The proposed adaptive amended Elman neural network backstepping (AAENNB) control system is adopted for controlling the LPMSM drive system to bring about the mover positioning precision of LPMSM. Firstly, a backstepping scheme is posed for controlling the tracing motion of the LPMSM drive system. The proposed backstepping control system, which is applied in the mover position of the LPMSM drive system, possesses better dynamic control performance and robustness to uncertainties for the tracing trajectories. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) is posed to estimate the required lumped uncertainty. According to the Lyapunov stability theorem, on-line parameter training methodology of the amended Elman neural network (AENN) can be derived by use of adaptive law. The error estimated law is proposed to compensate for the observed error induced by the AENN with adaptive law. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the varied learning rate of the weights in the AENN. At last, these experimental results, which show better performance, are verified by the proposed control system.
機(jī)譯：安裝在線性永磁同步電機(jī)（LPMSM）中的精度為1μm的線性光學(xué)標(biāo)尺傳感器用于測(cè)量LPMSM的動(dòng)子位置，以提高被測(cè)動(dòng)子位置的精度。由于非線性摩擦和不確定性影響，線性控制器很難實(shí)現(xiàn)LPMSM的良好動(dòng)子定位。采用提出的自適應(yīng)修正Elman神經(jīng)網(wǎng)絡(luò)反推（AAENNB）控制系統(tǒng)來控制LPMSM驅(qū)動(dòng)系統(tǒng)，以實(shí)現(xiàn)LPMSM的動(dòng)子定位精度。首先，提出了一種用于控制LPMSM驅(qū)動(dòng)系統(tǒng)的跟蹤運(yùn)動(dòng)的后推方案。所提出的反推控制系統(tǒng)應(yīng)用于LPMSM驅(qū)動(dòng)系統(tǒng)的原動(dòng)機(jī)位置，具有更好的動(dòng)態(tài)控制性能和魯棒性，可跟蹤軌跡的不確定性。由于LPMSM具有非線性和時(shí)變動(dòng)態(tài)特性，因此提出了一種自適應(yīng)修正的Elman神經(jīng)網(wǎng)絡(luò)不確定性觀察器（AAENNUO）來估計(jì)所需的總不確定性。根據(jù)Lyapunov穩(wěn)定性定理，可以通過使用自適應(yīng)定律推導(dǎo)經(jīng)過修正的Elman神經(jīng)網(wǎng)絡(luò)（AENN）的在線參數(shù)訓(xùn)練方法。提出了誤差估計(jì)定律以利用自適應(yīng)定律補(bǔ)償AENN引起的觀測(cè)誤差。此外，為了幫助提高收斂性并獲得更好的學(xué)習(xí)性能，使用了改進(jìn)的粒子群優(yōu)化（PSO）算法來調(diào)整AENN中權(quán)重的變化學(xué)習(xí)率。最后，所提出的控制系統(tǒng)驗(yàn)證了這些具有較好性能的實(shí)驗(yàn)結(jié)果。

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