Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator

Because the control performance of a piezoactuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics of the piezo-actuator is completed by integrating the parameterized hysteresis model into a mechanical motion dynamics. Based on this developed dynamics, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty E is difficult to obtain in practical application. Therefore, a RFNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty E on line. And, some experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust to the variations of system parameters and external load.     

气动肌肉的最小二乘支持向量机迟滞模型

针对传统迟滞模型存在的待辨识参数多、参数辨识过程复杂和辨识精度低等问题,采用最小二乘支持向量机对气动肌肉的位移/气压迟滞开展建模研究。通过非线性映射将原始数据空间映射到高维空间,将原系统的非线性问题变成高维空间中的线性问题,借助于最小二乘法求解该线性方程组,从而提高其求解速度及收敛精度。在气动肌肉迟滞特性实验的基础上,采用所建数学模型,与经典的PI模型进行对比。结果表明,采用最小二乘支持向量机建立的数学模型具有更高的建模精度,均方差和平均误差相比PI模型分别减小了99.21%和99.1%,该方法可为后续气动肌肉的迟滞补偿控制提供有效的手段。     

钢丝绳减振器准静态非对称迟滞模型参数识别

由于钢丝间存在着干摩擦作用,钢丝绳减振器具有明显的非线性迟滞阻尼。现有文献中钢丝绳减振器迟滞模型的参数识别大都是基于初始零变形情况下给出的。实际上,在隔振系统中减振器是在承载作用下,此时已有一定的初始变形。钢丝绳减振器具有强非线性,相对于不同初始变形的迟滞曲线中的参数是不同的。为此,基于钢丝绳减振器额定荷载下的实验迟滞曲线,考虑高阶刚度和可能出现的多种阻尼成分,通过参数识别给出钢丝绳减振器额定荷载下的准静态非对称迟滞模型。结果表明：理论回线与试验回线吻合,该模型能很好地反映钢丝绳减振器的准静态非线性刚度和阻尼特性。     

应用自适应模糊神经网络进行摩擦补偿的研究

在分析了摩探性及其对伺服系统低速特性的影响的基础上,提出了一种基于自适应模糊神经网络的摩探辨识和补偿方法,该方法既可实现摩探力矩的在线辨识和补偿,又能保证系统的稳定性。仿真结果表明,该方法是有效的,而且便于工程应用。     

Parameters Identification of Interface Friction Model for Ceramic Matrix Composites Based on Stress-Strain Response

An approach to identify parameters of interface friction model for Ceramic Matrix composites based on stress-strain response was developed. The stress distribution of fibers in the interface slip region and intact region of the damaged composite was determined by adopting the interface friction model. The relation between maximum strain, secant moduli of hysteresis loop and interface shear stress, interface de-bonding stress was established respectively with the method of symbolic-graphic combination. By comparing the experimental strain, secant moduli of hysteresis loop with computation values, the interface shear stress and interface de-bonding stress corresponding to first cycle were identified. Substituting the identification of parameters into interface friction model, the stress-strain curves were predicted and the predicted results fit experiments well. Besides, the influence of number of data points on identifying the value of interface parameters was discussed. And the approach was compared with the method based on the area of hysteresis loop.     

基于匹配追随算法的摩擦力自适应辨识与补偿研究

提出一种基于匹配追随算法的摩擦力自适应辨识与补偿新方法,该方法利用正交多项式实现对系统中以摩擦力为主的非线性动态充分逼近与补偿,不要求任何摩擦力模型,相对于基于线性化模型的自适应补偿算法,该算法具有更高的优越性,基函数集的选取对该算法精度及计算量有重要影响,该算法给出了一种基于残差分析法的基函数集优选方法,最后,通过仿实验验证了该算法的有效性。     

压电宏纤维致动器的双极性非对称迟滞建模及补偿控制

提出了一种双极性非对称改进PI(Bipolar Asymmetric Improved PI,BAIPI)模型描述压电宏纤维(Marco Fi-ber Composite,MFC)的迟滞特性,BAIPI模型利用经典Prandtl-Ishlinskii(PI)迟滞模型Play算子加权叠加描述MFC的对称迟滞特性,然后叠加...     

The elastostatic approach to compression of a cracked medium with dry friction

Summary In the frame of a linear elastic material compression of a cracked plane is considered. Friction during a mutual sliding of the crack surfaces can be responsible for some non-linear effects, in particular, for hysteresis. A rigorous solution to the problem of non-axisymmetric compression of a space weakened by a circular crack with dry friction is given. This solution is obtained in displacements and the field of displacements is represented in elementary functions.     

Efficient implementation of vector Preisach-type models using orthogonally coupled hysteresis operators

Vector hysteresis models are regarded as helpful tools that can be utilized in the simulation of multidimensional field-media interactions. Recently, substantial efforts have been focused on the refinement of vector Preisach-type models of hysteresis. The purpose of this paper is to present a computationally efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally inter-related elementary operators. Such a model is implemented via a linear neural network (LNN) fed from the outputs of discrete Hopfield neural network (DHNN) blocks having step activation functions. With this DHNN-LNN configuration, it is possible to carry out the identification process using well-established widely available algorithms. Details of the model, its identification, and experimental testing are presented.     

Integrated sample processing system involving on-column protein adsorption, sample washing, and enzyme digestion for protein identification by LC-ESI MS/MS

An automated system has been developed for protein identification using mass spectrometry that incorporates sample cleanup, preconcentration, and protein digestion in a single stage. The procedure involves the adsorption of a protein or a protein mixture from solution onto a hydrophobic medium that is contained within a microcolumn. The protein is digested while still bound to the hydrophobic support. The peptides are then eluted from surface digestion to an electrospray ionization mass spectrometer for detection and sequencing. The entire system is fully automated wherein the mass spectrometer is collecting data continuously. We demonstrate that this system is capable of identifying standard protein samples at concentrations down to 100 nM. Further development of this technique may offer a potential solution for proteomics applications that require unattended operation, such as on-line monitoring and identification of microorganisms on the basis of the detection of their protein biomarkers.     

Evaluation and Compensation of Quantization Effect on Servo Performance for Hard Disk Drives

We investigated the quantization effect on the servo performance of a 1.8-in hard disk drive by changing the bits of the proposed quantizer model. We measured and analyzed the frequency response of the quantizer with different bits. The corresponding error rejection functions show that the poor rejection ability of the servo loop to low-frequency disturbances is caused by the quantizer in addition to the actuator pivot friction behavior. We propose a simple and low-cost scaling scheme to compensate the effect of the quantizer. With the compensation, the effect on the rejection ability is mostly due to the friction. Therefore, the effects from quantization and friction on the error rejection function can be differentiated, and the quantization and friction induced problems can be dealt with separately in the servo loop. In addition, through the proposed quantization model and measurement methodology, suitable bit resolution for the quantizer with and without the compensation can be identified.     

Magnetic hysteresis modeling via feed-forward neural networks

A general neural approach to magnetic hysteresis modeling is proposed. The general memory storage properties of systems with rate independent hysteresis are outlined. Thus, it is shown how it is possible to build a neural hysteresis model based on feed-forward neural networks (NN's) which fulfills these properties. The identification of the model consists in training the NN's by usual training algorithms such as backpropagation. Finally, the proposed neural model has been tested by comparing its predictions with experimental data     

压电陶瓷片迟滞补偿建模及悬臂梁主动控制研究

为增强压电悬臂梁振动控制效果,提出一种基于最小二乘法的逆迟滞补偿控制算法。在不同电压下对压电陶瓷片位移进行实测,应用最小二乘法对其迟滞环进行多项式拟合建模,并利用压电片逆迟滞补偿模型对控制电压进行补偿。通过悬臂梁振动主动控制试验系统研究PID控制器在有、无逆迟滞补偿时的控制效果。结果表明:经过PID逆迟滞补偿后的主动控制效果比传统PID提高10.083%。因此,该逆迟滞补偿方法能够有效增强压电陶瓷片的主动控制效果,对于压电悬臂梁振动主动控制具有重要参考价值。     

一种压阻式压力传感器全温区温度补偿方法

针对压阻式压力传感器因热零点漂移、热灵敏度改变以及热迟滞效应引起的误差,提出一种压阻式压力传感器全温区温度补偿方法.该方法是在温升和温降全温区样本采集的数据基础上,采用最小二乘法曲面拟合原理对压阻式压力传感器进行数字补偿.通过对传感器进行实验标定和误差分析,并与常用的单一温升样本采集并进行数字补偿的方法进行对比,结果表...     

氮化铬反应磁控溅射的状态空间模型预测控制

为了制备出具有优良摩擦性能的氮化铬薄膜,采用了状态空间模型预测控制对氮气流量进行连续稳定控制。本文运用了靶电压控制法对氮气流量进行快速反馈控制,通过反应磁控溅射实验收集氮气流量与靶电压的迟滞效应数据,使用系统辨识法得到被控对象的数学模型,利用状态空间模型预测算法进行控制,并与PID控制对比研究。仿真结果表明状态空间模型预测控制的调节时间比PID控制快16 s,阶跃扰动比PID控制小七倍,模型预测控制具有更好的控制效果。在实验中应用状态空间模型预测控制氮气流量,制备的氮化铬薄膜进行摩擦系数的测试为0.7,实验达到预期效果。     

基于颤振效应的气动比例阀摩擦力补偿研究

针对气动比例阀的摩擦力是造成其死区的主要因素,分析了比例阀摩擦力的产生原因.根据振动对摩擦力的影响,对气动比例阀叠加高频低幅颤振信号进行摩擦力补偿的机理进行了研究,并基于软件VC 和LabVIEW开发出摩擦力颤振补偿器.实验结果表明,通过在控制信号上叠加高频低幅的颤振信号,使阀芯始终处于微小振动状态,可以有效克服比例阀摩擦力,消除爬行现象,缩短滞后时间.该补偿方法具有较高的工程应用价值.     

Drop detachment and motion on fuel cell electrode materials

Liquid water is pushed through flow channels of fuel cells, where one surface is a porous carbon electrode made up of carbon fibers. Water drops grow on the fibrous carbon surface in the gas flow channel. The drops adhere to the superficial fiber surfaces but exhibit little penetration into the voids between the fibers. The fibrous surfaces are hydrophobic, but there is a substantial threshold force necessary to initiate water drop motion. Once the water drops begin to move, however, the adhesive force decreases and drops move with minimal friction, similar to motion on superhydrophobic materials. We report here studies of water wetting and water drop motion on typical porous carbon materials (carbon paper and carbon cloth) employed in fuel cells. The static coefficient of friction on these textured surfaces is comparable to that for smooth Teflon. But the dynamic coefficient of friction is several orders of magnitude smaller on the textured surfaces than on smooth Teflon. Carbon cloth displays a much smaller static contact angle hysteresis than carbon paper due to its two-scale roughness. The dynamic contact angle hysteresis for carbon paper is greatly reduced compared to the static contact angle hysteresis. Enhanced dynamic hydrophobicity is suggested to result from the extent to which a dynamic contact line can track topological heterogeneities of the liquid/solid interface.     

Hysteresis and Compensation Behaviors of Mixed Spin-1 and Spin-2 Hexagonal Ising Nanowire System

By utilizing the framework of the effective field theory with correlation, hysteresis and compensation behaviors of mixed spin-1 and spin-2 hexagonal Ising nanowire system is investigated in detail. The effects of Hamiltonian parameters on hysteresis behaviors are discussed, in detail. The compensation behavior of the system is also studied and according to values of Hamiltonian parameters, the Q-, S-, R-, and N-type compensation behaviors is obtained in the system. Finally, the obtaining results are compared with some experimental and theoretical results and found in a qualitatively good agreement.     

Key Technique of Austenitic Stainless Steel on-line Solution Treatment

Generally the methods of solution treatment of austenitic stainless steel are bifurcated on-line solution and off-line solution . For a founded enterprise, it is necessary to find out how to get across alterations and search a measure of on -line solution disposal with less investment and higher efficiency. By studying and analysingin laboratory, several key points and the methods settle them are presented, which offers a new route to realize austenitic stainless steel on-line solution. By reducing the cost greatly, it makes the enterprise larger benefits.     

Using neural networks in the identification of Preisach-typehysteresis models

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results