【摘要】：微納米執(zhí)行器是微納米定位技術的核心元件,尋找性能優(yōu)良的執(zhí)行器材料對提高微納米定位精度具有十分重要的意義。壓電陶瓷是一種能使機械能量和電能量之間相互轉換的智能執(zhí)行器材料,它具有價格低、質(zhì)量輕、頻率響應快、位移精度高、驅(qū)動力大等優(yōu)越點,近些年成為了一個研究熱點,并廣泛應用于航天航空、機器人、微控制工程等領域。然而壓電陶瓷材料存在遲滯現(xiàn)象,該特性導致壓電陶瓷執(zhí)行器響應速度變慢、可控性變差,嚴重阻礙壓電陶瓷執(zhí)行器的應用與發(fā)展。因此,如何消除遲滯現(xiàn)象成為實現(xiàn)壓電陶瓷執(zhí)行器精密位移控制必須要攻克的難題。消除遲滯非線性常用的思路可歸納為建模和控制兩種,針對壓電陶瓷執(zhí)行器中率相關遲滯非線性的現(xiàn)象,本文首先對其建立率相關遲滯非線性模型,由于傳統(tǒng)KP模型屬于率不相關的遲滯非線性模型,本文因此在經(jīng)典KP模型的基礎上加以改進,構造出Hammerstein模型,該模型可以描述壓電陶瓷執(zhí)行器中頻率相關遲滯非線性(率相關遲滯非線性)。此模型由經(jīng)典KP模型與傳遞函數(shù)串級組成,KP模型用來表征遲滯非線性,傳遞函數(shù)部分用來描述動態(tài)率相關性特性。其次,本文分步對其進行參數(shù)辨識,并采用和聲搜索優(yōu)化算法和蝙蝠和聲混合優(yōu)化算法對KP模型的密度函數(shù)進行辨識,選取誤差較小的一種辨識算法。最后,為了減小甚至消除率相關遲滯非線性對位移控制精度的影響,本文提出了三種控制方案:第一種控制方案是遲滯逆模型補償?shù)那梆伩刂品桨?實驗表明,本文所提出的前饋控制器有效地減少了率相關遲滯非線性對位移控制精度的不利影響。開環(huán)控制雖然提高了跟蹤精度,但是系統(tǒng)的抗干擾能力較弱,于是本文提出第二種控制方案,采用BP算法整定PID參數(shù)與前饋相結合的復合控制方案,進一步提高了控制精度,而且增強了系統(tǒng)的抗干擾能力。由于復合控制仍然是基于模型的控制方法,那么建模不確定性依然會削弱控制效果。第三種方案是一種無需逆模型的控制方案,主要思想是基于神經(jīng)網(wǎng)絡逼近非線性函數(shù)的超強能力,用兩個神經(jīng)網(wǎng)絡分別逼近控制系統(tǒng)函數(shù)關系中的兩個未知量,以達成系統(tǒng)輸出無差跟蹤系統(tǒng)輸入的理想目的。實驗數(shù)據(jù)表明,同復合控制相比,神經(jīng)網(wǎng)絡控制的最大誤差和均方根誤差均有減小。實驗結果證明了神經(jīng)網(wǎng)逼近非線性函數(shù)控制系統(tǒng)的跟蹤精度優(yōu)于壓電陶瓷執(zhí)行器復合控制系統(tǒng)的精度。
[Abstract]:Micro / nano actuators are the core components of micro / nano positioning technology. It is very important to find good actuator materials to improve the precision of micro / nano positioning. Piezoelectric ceramic is a kind of intelligent actuator material which can convert mechanical and electrical energy. It has many advantages such as low price, light mass, fast frequency response, high displacement precision, high driving force and so on. In recent years, piezoelectric ceramics has become a research hotspot. And widely used in aerospace, robotics, micro-control engineering and other fields. However, the hysteresis phenomenon of piezoelectric ceramic materials leads to the slow response speed and poor controllability of piezoelectric actuators, which seriously hinders the application and development of piezoelectric actuators. Therefore, how to eliminate hysteresis becomes a difficult problem to realize the precision displacement control of piezoelectric actuator. The common ideas for eliminating hysteresis nonlinearity can be summarized as modeling and control. Aiming at the phenomenon of rate-dependent hysteresis nonlinearity in piezoelectric ceramic actuators, a rate-dependent hysteresis nonlinear model is established in this paper. Because the traditional KP model belongs to the rate independent hysteretic nonlinear model, this paper improves on the classical KP model and constructs the Hammerstein model. The model can be used to describe the frequency dependent hysteresis nonlinearity in piezoelectric actuator. The model is composed of the classical KP model and the transfer function cascade to characterize the hysteresis nonlinearity, and the transfer function part is used to describe the dynamic rate correlation. Secondly, the parameter identification of KP model is carried out step by step, and the density function of KP model is identified by harmony search optimization algorithm and bat harmony sound hybrid optimization algorithm, and a less error identification algorithm is selected. Finally, in order to reduce or even eliminate the influence of rate-dependent hysteresis nonlinearity on displacement control accuracy, three control schemes are proposed in this paper: the first control scheme is feedforward control scheme with hysteresis inverse model compensation. The feedforward controller presented in this paper can effectively reduce the adverse effect of the rate dependent hysteresis nonlinearity on the displacement control accuracy. Although the open-loop control improves the tracking accuracy, the anti-jamming ability of the system is weak, so the second control scheme is proposed in this paper. The BP algorithm is used to adjust the PID parameters and the feedforward compound control scheme, which further improves the control accuracy. Moreover, it enhances the anti-interference ability of the system. Because compound control is still a model-based control method, modeling uncertainty will still weaken the control effect. The third scheme is a control scheme without inverse model. The main idea is to use two neural networks to approximate the two unknown variables in the function relationship of the control system, based on the super ability of the neural network to approximate the nonlinear function. In order to achieve the system output differential tracking system input ideal purpose. The experimental data show that the maximum error and root mean square error of neural network control are smaller than that of compound control. The experimental results show that the tracking accuracy of the neural network approximating nonlinear function control system is better than that of the piezoelectric ceramic actuator composite control system.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN384;TP273

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