压电陶瓷驱动器迟滞非线性的改善方法

压电陶瓷驱动器在微位移定位方面被广泛应用,但压电陶瓷驱动器的迟滞非线性严重影响其定位精度,因此,介绍了各种改善压电陶瓷驱动器迟滞非线性特性的方法,并提出了用3次样条插值法和最小二乘曲线拟合法来实现压电陶瓷驱动器的精确定位,改善压电陶瓷驱动器的迟滞非线性特性,实验表明,以上方法可使压电陶瓷驱动器的定位误差小于5％.     

压电陶瓷微位移器件迟滞模型的研究

在通过统计物理学角度分析压电陶瓷迟滞规律的基础上，结合数学建模方法，提出了一种简单实用的压电陶瓷迟滞数学模型。并设计了压电陶瓷实验控制系统，对迟滞数学模型进行了验证，实验结果表明，此模型可以有效减小压电陶瓷的迟滞非线性误差，提高压电陶瓷微位移的控制精度，本研究有助于实现基于压电陶瓷的高精度开环微位移控制。     

基于激光回馈干涉系统的PZT迟滞特性测量

压电陶瓷迟滞特性对精密控制系统的精度具有重要影响,目前PZT迟滞特性的测量方法精度较低或者测量系统复杂,难以实现高精度测量。总结分析了目前测量压电陶瓷特性的方法,并使用基于激光回馈干涉原理的微片激光回馈干涉测量系统,利用物体表面的散射光对压电陶瓷的迟滞特性进行测量和分析。该方法不需靶镜,对被测对象要求较低,测量便捷,测量精度达到纳米量级,对精密控制系统中压电陶瓷的校准测量具有重要意义。     

压电陶瓷致动器自适应逆控制方法的研究

压电陶瓷器件在精密定位和微位移控制中得到了广泛的应用，但是它也存在着迟滞，蠕变和位移非线性等不足，该文将自适应逆控制思想应用于对压电陶瓷致动器的控制，通过对其机电变换特性的分析，用自适应法建立压电陶瓷的迟滞蠕变模型和逆模型，并且在此基础上建立实验系统，对压电陶瓷致动器进行自适应逆控制法的研究，实验数据分析结果表明，该控制方法有良好的学习功能，系统的输出线性误差从28．1％减少到1．56％。     

用VB数据库实现压电陶瓷Preisach模型及定位控制

压电陶瓷器件在精密定位和微位移控制中得到了广泛的应用，但是它也存在着迟滞、蠕变和非线性等不足。preisach数学模型可用来描述压电陶瓷的迟滞特性，并根据电压变化的历史预测电压的对应位移量。根据压电陶瓷的特性及利用VB数据库技术实现preisach数学模型的改进形式，使压电陶瓷的位移控制精度得到提高和改进。     

基于电荷控制压电陶瓷驱动方法的研究进展

压电陶瓷驱动器在电场作用下将产生迟滞和蠕变。从而降低其定位精度。采用电荷控制可以减小位移迟滞和蠕变。该文从电流源电荷反馈和电压源电荷反馈两个角度，总结国内外各种电荷控制方案，进而提出应该优先选用电流源驱动压电陶瓷，并根据应用场合不同选择相应电荷控制方法的结论。     

基于深度神经网络(DNN)的压电陶瓷前馈补偿研究

针对压电陶瓷固有的迟滞非线性,设计了一种基于深度神经网络(DNN)的前馈补偿控制系统。该系统包含1个输入层、7个隐藏层和1个输出层。实验结果表明,开环情况下压电陶瓷的位移线性误差达8.91μm。施加神经网络前馈补偿后,压电陶瓷的最大位移误差降低到80 nm,稳态误差为±20 nm。进一步测试表明,在10～100 Hz输入频率下系统最大误差小于100 nm,均方根误差为0.01μm,验证了深度神经网络能够准确补偿压电陶瓷动态迟滞非线性,具有较好的频率泛化能力。     

压电扫描器系统中迟滞补偿器的设计

在自适应光学、空间光通信、激光加工等领域,激光扫描器是其中的一项关键器件.基于压电陶瓷叠堆的传统激光扫描器很难实现较好的线性扫描,为此设计了一种基于迟滞补偿器的压电扫描器.介绍了基于迟滞补偿器的压电扫描器系统组成,分析了压电扫描器中迟滞回线的修正原理及方法.利用经过迟滞补偿后的驱动信号驱动激光扫描器进行扫描,实验结果表...     

压电陶瓷驱动微进给刀架的迟滞建模

以对称式微位移缩小机构和柔性铰链相结合,压电陶瓷驱动的微进给刀架可实现精密加工,但刀架的迟滞特性影响其定位精度。该文根据非线性Preisach模型的理论知识及压电陶瓷驱动微进给刀架的电压位移特性,将模型进行修改后得到刀架迟滞特性的数学模型,并对数学模型式进行离散化处理。实验结果表明,改进后的迟滞模型形式简单,数据采集简便,模型描述精确,能较好地实现压电驱动微进给刀架的迟滞建模,提高了迟滞模型的实用性。为提高压电陶瓷驱动微进给刀架的定位精度,实现精密控制打下基础。     

压电陶瓷迟滞逆模型的前馈PID控制

为减小压带陶瓷迟滞特性对系统跟踪精度的影响,在Preisach模型的基础上对压电陶瓷迟滞性进行建模。借助Matlab软件对实验数据进行拟合和采用逆控制思想,在迟滞逆模型的基础上提出前馈PID(即比例、积分、微分)控制算法。实验结果表明,压电陶瓷最大迟滞性控制在2.2%内,输入、输出具有较好的线性关系,带前馈的PID控制具有良好的控制性能。     

基于单神经元PSD压电微驱动系统控制的研究

压电式微驱动器具有灵敏度高，响应快，易于控制，性能稳定等特点，由于压电陶瓷复杂的机理．它存在着迟滞、蠕变和非线性的压电误差，且受环境温度、外力振动等因素影响。在高精度定位时存在较大误差，为了消除压电陶瓷迟滞、蠕变、非线性及其他外部因素对定位精度的影响，采用了单神经元比例、求和、微分（PSD）功能的控制算法。实现微驱动定位工作台的实时控制。实验证明该控制算法对系统定位控制很有效。     

彩色多普勒超声对宫腔疾病的诊断

研究了微型三坐标测量仪中由压电陶瓷器件+柔性铰链构成的微动系统.根据该微动系统对定位精度的要求及微动系统具有迟滞、蠕变等强非线性的特点,该文提出一种神经网络自适应模糊推理比例微分积分(PID)位置控制系统,并进行了控制系统结构研究和实验分析.跟踪实验结果表明,智能PID控制能有效改善系统的动态和静态性能;定位精度实验表明两轴双向定位精度较传统PID控制有大的提高,达到了设计要求.     

Development of a novel dynamic friction model and precise tracking control using adaptive back-stepping sliding mode controller

The LuGre model which has been widely used to describe the friction phenomenon for mechanical systems consists of stiffness and viscous terms. The stiffness term of the LuGre model shows the friction torque to act linearly for the internal state of friction dynamics. Thus it cannot represent the hysteresis phenomenon of friction in the pre-sliding phase. Especially the hysteresis has the non-local memory characteristics. In this paper, the non-local memory hysteresis phenomenon is analyzed through experiments and the improved friction model using the Preisach model is proposed. In order to implement the Preisach model, the neural network algorithm is used to increase the efficiency of the Preisach algorithm. Based on the improved friction model, the adaptive back-stepping sliding mode controller (SMC) is designed to improve tracking performance in the sliding and pre-sliding phases. To evaluate the performance of the proposed friction control system, experiments are executed for a ball-screw servo system and the satisfactory results are shown.     

Adaptive displacement control with hysteresis modeling for piezoactuated positioning mechanism

An adaptive displacement control with hysteresis modeling for a piezoactuated positioning mechanism is proposed in this paper because the dynamic performance of piezosystems is often severely deteriorated due to the hysteresis effect of piezoelectric elements. First, a new mathematical model based on the differential equation of a motion system with a parameterized hysteretic friction function is proposed to represent the dynamics of motion of the piezopositioning mechanism. As a result, the mathematical model describes a motion system with hysteresis behavior due to the hysteretic friction. Then, by using the developed mathematical model, the adaptive displacement tracking control with the adaptation algorithms of the parameterized hysteretic function and of an uncertain parameter is proposed. By using the proposed control approach on the displacement control of the piezopositioning mechanism, the advantages of the asymptotical stability in displacement tracking, high-performance displacement response, and robustness to the variations of system parameters and disturbance load can be provided. Finally, experimental results are illustrated to validate the proposed control approach for practical applications.     

Real-time parallel adaptive neural network control for nonlinear servomechanisms—an approach using direct adaptive techniques

In this paper, a parallel adaptive neural network control system applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms is developed. The controller is based on the use of direct adaptive techniques and an approach of using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear controller. Properties of the proposed new controller are discussed in the paper and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and asymptotic tracking of the position reference signal is achieved. The effectiveness of the proposed adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load.     

一种递归模糊神经网络自适应控制方法

构造了一种递归模糊神经网络(RFNN),该RFNN利用递归神经网络实现模糊推理,并通过在网络的第一层添加了反馈连接,使网络具有了动态信息处理能力.基于所设计的RFNN,提出了一种自适应控制方案,在该控制方案中,采用了两个RFNN分别用于对被控对象进行辨识和控制.将所提出的自适应控制方案应用于交流伺服系统,并给出了仿真实验结果,验证了所提方法的有效性.     

压电式气体流量比例阀建模与控制技术研究

针对传统 Prandtl-Ishlinskii(PI)模型不能反映压电式气体比例阀迟滞非对称特性而导致其补偿控制精度难以提高的问题,提出了一种改进的 PI模型,通过添加3次多项式使其能拟合压电式气体流量比例控制阀的非对称迟滞曲线。利用改进的自适应粒子群遗传算法辨识所需的模型参数,模型相对误差为0．0073%,并将模型用于前馈补偿控制。实验结果表明,基于迟滞模型的前馈补偿控制可显著提高压电式气体比例阀输出流量控制的快速性,调节时间降低了60%。     

噪声有源控制的人工神经网络方法

讨论了有源噪声控制（ＡＮＣ）问题,提出一种基于人工神经网络的非线性噪声有源自适应控制方法,给出了一种基于误差梯度下降的学习算法,证明了闭环控制系统在Ｌｙａｐｕｎｏｖ意义下的稳定性。     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

A neural network control system with parallel adaptive enhancementsapplicable to nonlinear servomechanisms

In this paper, we present a technique for using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear control system. This proposed parallel adaptive neural network control system is applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. Properties of the controller are discussed, and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and the approximation error converges to zero asymptotically. In the paper, the effectiveness of the proposed parallel adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load