CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor程龙
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword压电驱动纳米定位平台 压电陶瓷执行器 粘滞-滑动执行器 迟滞非线性 智能预测控制
Abstract       压电驱动纳米定位平台具有定位精度高、响应速度快、驱动能力强等诸多优点,广泛应用于高精度定位、精密微操作等领域。平台的核心器件压电陶瓷执行器基于逆压电效应进行工作,其定位精度与硬件平台的性能息息相关。同时,压电陶瓷执行器存在迟滞、蠕变、振动等复杂的非线性特性,严重影响执行器的定位精度。针对以上问题,本文对压电驱动纳米定位平台的系统设计和压电陶瓷执行器的智能预测控制方法进行研究。在此基础上,进一步对压电陶瓷执行器的机械结构进行扩展,得到粘滞-滑动执行器,并研究其整体建模方法与自适应模糊模型预测控制方法,实现长行程、高精度的定位控制。本文的主要内容和创新之处如下:
       压电驱动纳米定位平台的系统设计: 考虑到原有的商业纳米定位平台(P- 753.1 CD 压电陶瓷执行器和E-665.CR 伺服控制器,PI 公司) 存在工作频率较低、 功率较小等不足,本文设计了一款高频响、大功率的压电驱动纳米定位平台。该平 台由电源模块、控制模块(包括微控制器、数模转换器和模数转换器)、驱动模块、 压电陶瓷执行器和测量装置等部分组成。电源模块为整个系统提供稳定的供电,控 制模块负责通信和控制,微控制器通过数模转换器输出控制电压,该电压经驱动模 块放大后对压电陶瓷执行器进行激励,压电陶瓷执行器受到电压激励后会产生相 应的位移,测量装置采集该位移信号并由模数转换器传递给微控制器,从而构成闭 环控制系统。实验结果表明,所设计的压电驱动纳米定位平台的工作频率和功率是 原有的商业纳米定位平台的三倍多,并且线性度高、稳定性好,可实现压电陶瓷执行器的高频、高精度定位控制。
       基于迟滞逆补偿和模型预测控制的复合控制方法: 为克服压电陶瓷执行器 的迟滞非线性,本文提出一种基于迟滞逆补偿和模型预测控制的复合控制方法。该 控制方法采用前馈-反馈控制方案,主要由前馈迟滞补偿器和基于神经网络的模型 反馈预测控制器构成。首先,利用Duhem 逆模型构造前馈控制器,以补偿压电陶 瓷执行器的迟滞非线性。然后使用神经网络描述压电陶瓷执行器的动态特性,并 对其进行瞬时线性化以方便模型预测控制器的设计。基于该瞬时线性化的神经网 络模型,进行模型预测控制器的设计,该控制器具有解析形式的控制率。最后进行 实验验证,包括多种形式参考轨迹的跟踪实验、考虑外部负载和物理约束的实验 等,同时与文献中已有的控制方法进行比较。实验结果表明,所提出的基于迟滞逆 补偿和模型预测控制的复合控制方法可实现低于10 纳米的定位精度,优于文献中的大多数控制方法。
       粘滞-滑动精密定位平台的建模与控制: 针对压电陶瓷执行器行程较短的不 足,本文进一步对压电陶瓷执行器的机械结构进行扩展,得到粘滞-滑动执行器。粘 滞-滑动执行器由压电陶瓷执行器和末端器构成,基于特殊的粘滞-滑动机理可实现 理论上无限的运动行程,但是对其进行建模和控制更具挑战。首先,采用模糊模型逼近粘滞-滑动执行器的输入输出关系,该模型将粘滞-滑动执行器视为一个整体, 通过多个“IF-THEN 规则”描述整体模型。此外,为了提升模型的精度,引入了 自适应方法实时调整模型的参数。基于上述自适应模糊模型,本文提出一种自适应 模糊模型预测控制器,可实现对粘滞-滑动执行器的长行程、高精度定位控制。实 验结果显示,自适应模糊模型的误差小于40 纳米,自适应模糊模型预测控制器跟踪10Hz 正弦参考信号的最大误差小于20 纳米,说明了所提出的建模与控制方法的有效性。
Other Abstract       Piezo-actuated nanopositioning stages are widely used in many practical applications like high-precision positioning, micromanipulation and so on because of the high precision, rapid response and large mechanical force. As the pivotal components of piezo-actuated nanopositioning stages, piezoelectric actuators (PEAs) can achieve the nanoscale positioning based on the inverse piezoelectric effect, and the positioning accuracy is closely related to the performance of the hardware platform. In addition, the existence of hysteresis, creep and vibration makes it difficult to realize the precise control of PEAs. To deal with these nonlinearities, the design of a piezo-actuated nanopositioning stage and intelligent predictive control of PEAs are studied in this thesis. In order to achieve the precise long-distance movement, the PEAs are further developed to implement the piezoelectric stick-slip devices (PASSDs), and the overall modeling methods and the adaptive fuzzy model predictive control of PASSDs are proposed. The main contents and innovations of this thesis are summarized as follows:
       Design of a piezo-actuated nanopositioning stage: The existing commercial nanopositioning stage (P753.1CD, PI Corporation) suffers from the disadvantages of low power and low working frequency, therefore this thesis designs a highpower, high-working-frequency piezo-actuated nanopositioning stage. This nanopositioning stage consists of a power supply module, a control module (including a microcontroller, a digital-to-analog converter and an analog-to-digital converter), a drive module, a PEA and a measuring device. The power supply module provides a stable power supply for the entire system. The control module is responsible for the communication and control. The microcontroller outputs a control voltage through a digital-to-analog converter. After amplified by the drive module, this voltage is used to drive the PEA. Then, the PEA generates a corresponding displacement, and the measurement device collects the displacement signal and transmits it to the microcontroller through an analog-to-digital converter, thus forming a closed-loop control system. Experimental results show that the power and working frequency of the self-designed piezo-actuated nanopositioning stage are improved about two times, compared to the existing commercial nanopositioning stage. In addition, the piezo-actuated nanopositioning stage is of the high linearity and good stability, which can help achieving the high-frequency high-precision positioning control of PEAs.
       A composite control approach with hysteresis compensation and model predictive control: In order to overcome the hysteresis nonlinearity of PEAs, this thesis proposes a composite control method based on hysteresis compensation and model predictive control. This control method belongs to a feedforwardfeedback control scheme, which is mainly composed of a feedforward hysteresis compensator and a neural network model based feedback predictive controller. First, the feedforward controller is constructed by the inverse Duhem hysteresis model to compensate the hysteresis nonlinearity of PEAs. Then a neural network is adopted to describe the dynamic characteristics of PEAs, and it’s instantaneously linearized to facilitate the design of predictive controller. Based on this instantaneously linearized neural network model, a model predictive controller is designed, which has analytical control laws. Finally, experimental verifications are carried out, including various trajectory tracking experiments, experiments considering external loads and physical constraints, and comparison with existing control approaches in the literature. Experimental results show that the proposed composite control method can achieve a positioning accuracy of less than 10 nanometers, which is superior to most control approaches in the literature.
       Modeling and control of piezoelectric stick-slip devices: Considering that the movement range of PEAs is relatively limited, this thesis further develops a PASSD. The PASSD consists of a PEA and an end-effector, which can achieve a theoretically infinite movement range based on the stick-slip mechanism. However, modeling and control of PASSDs are challenging. First, a T-S fuzzy model is adopted to describe the input-output relationship of PASSDs, which treats the PASSD as a whole, and several local linear models are combined to obtain the overall model of PASSDs. In addition, in order to improve the model accuracy, an adaptive method is introduced to adjust the model parameters in real time. Based on this adaptive fuzzy model, an adaptive fuzzy predictive controller is proposed, which can achieve a long-distance precise control of PASSDs. The experimental results show that the error of the adaptive fuzzy model is less than 40 nanometers, and the maximum tracking error of the adaptive fuzzy predictive controller is less than 20 nanometers when tracking the 10Hz sinusoidal reference, which demonstrates the effectiveness of the proposed modeling and control methods.
Document Type学位论文
Recommended Citation
GB/T 7714
王昂. 压电驱动纳米定位平台的设计与智能预测控制方法研究[D]. 北京. 中国科学院研究生院,2018.
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