基于可控性柱面网壳结构主动控制作动器布置的优化研究

利用磁致伸缩材料的磁控特性制作的作动器可以对结构进行主动控制。首先分析了这种作动器的工作原理和设计方法,并通过实验对其进行了输出性能测试。接着在对作动器进行动力学建模的基础上,推导出整个柱面网壳结构的作动控制方程,同时基于作动效率,提出了不依赖于控制方法的位置优化准则,并且在综合考虑控制效果系数、硬件成本和系统复杂性等因素的基础上,初步确定了作动器的数量,然后采用遗传算法,对作动器的布置位置进行了优化。最后利用LQR主动控制算法,对一柱面网壳模型结构进行了主动控制分析。结果表明,通过优化布置的作动器能够有效地减小结构的动力反应,是一种较好的主动控制方法。此外,主动控制模拟结果也验证了应用遗传算法优化此类问题的优越性和可靠性。     

智能桁架结构最优振动控制与作动器优化配置

研究了智能桁架结构最优振动控制和作动器的优化配置问题。首先采用有限元方法,根据Hamilton原理推导了智能桁架结构的机电耦合动力学方程,根据线性二次型最优控制理论,推导了结构振动控制的数学模型,通过最小化性能泛函,求解黎卡提矩阵代数方程确定了最优控制输入。然后通过对最优控制性能指标函数的修正,得到了与初始状态无关的性能指标,以修正的性能指标为目标函数,应用模拟退火算法对作动器位置进行了优化配置。最后给出了空间智能桁架结构振动控制算例验证建模过程和算法。算例结果表明,通过最优振动控制可以使结构振动快速衰减,达到振动抑制的效果,而且通过模拟退火算法可以确定最佳的作动器布置方式。     

直升机结构响应主动控制作动器优化设计研究

采用子结构综合法建立考虑作动器动特性与其相互影响的直升机机身／多作动器耦合系统频域数学模型，研究了直升机结构响应主动控制中采用作动器位置序号进行编码的遗传算法对多作动器位置进行优选，以及与电磁式惯性型作动器参数优化的综合优化问题，进行了结构响应主动控制试验研究，验证了多作动器减振的有效性和所提出的作动器位置优选方法的正确性。     

振动控制中压电作动器非线性的补偿方法研究

在周期振动的自适应控制中,压电作动器的非线性特性会产生高次谐波,激发高阶模态振动。为抑制压电作动器的高次谐波激励,同时结合自适应振动控制的特点,提出一种新的作动器非线性补偿方法。该方法将作动器的非线性与结构的动态特性部分融合,用正交多项式从输入输出信号中拟合静态非线性及其逆变换,计算过程简单,数值稳定性高。在控制通道中,通过逆变换对控制信号进行预处理,使得补偿后的输入输出具有线性系统的特征,而输入输出之间的相位差完全由自适应算法进行补偿。实验结果表明,所给出的补偿方法能够抑制高次谐波,并改善了振动控制效果。     

柔性板压电作动器的优化位置与主动控制实验研究

对柔性悬臂板主动控制中作动器的优化位置进行研究,其中作动器采用压电形式,优化算法采用粒子群方法,指标函数采用基于能量的可控Gramian优化配置准则。仿真和实验结果显示,粒子群优化算法能够有效地对作动器的优化位置进行计算,尤其适用于多个作动器的位置优化问题,基于作动器最优位置的控制设计能够取得良好的控制效果。     

磁致伸缩作动器的设计与性能分析

在分析超磁致伸缩材料特性的基础上设计了一种微位移作动器．通过有限元分析，对作动器的结构进行优化，得到均匀的偏置磁场和激励磁场；设计了预压力加栽结构和强制冷却结构．对该作动器的动、静态性能进行了测试，结果表明：作动器基本上工作在线性区域内，其位移伸缩量大，低频动态性能较好，高频谐波分量影响较小，相位延迟较小，同时也证明了对作动器的磁场分析和结构优化是正确的．     

结构地震响应控制的优化方法

针对大跨空间结构地震响应主动控制,以超磁致伸缩材料为核心元件设计了一种可以应用于大跨空间结构振动主动控制中的超磁致伸缩作动器,制作出了作动器的原型并对其进行了输出性能测试。使用遗传算法对大跨空间结构主动控制作动器的布置位置进行了优化设计,最后进行了优化效果的数值模拟分析。以此验证了超磁致伸缩作动器具有良好的作动效应,利用遗传算法在大大提高结构主动控制优化设计效率时,可以保证实现对结构的整体优化以及作动器能高效、经济地实现对结构进行主动控制的目的。     

基于内点法和拉格朗日乘子法的混合试验冗余作动器控制方法

土木工程结构试验中,作动器使用数量多于所要控制的自由度数量时会产生冗余作动器控制问题。为此,提出基于内点法和拉格朗日乘子法的冗余作动器控制方法,给出作动器系统能力可行域的求解方法,该冗余作动器控制方法在控制方程中以三参量的形式考虑了单台作动器的出力范围。以MATLAB为平台计算了作动器能力可行域和冗余作动器出力值,结果表明,当试件位移命令在作动器能力可行域内,该方法可以实现良好的加载控制精度;当位移在作动器能力可行域外,该方法可以保证试验的安全性。基于Simulink搭建控制平台,分析冗余系统和非冗余系统的工作状态;最后以双层双向虚拟混合试验为例,验证了所提冗余控制方法的精度和可行性。     

新型高效电磁驱动反力作动器的设计及特性研究

设计了一种新型高效电磁驱动反力作动器，进行了作动器的磁场计算，利用Ansys软件对其弹簧刚度进行了分析。建立了作动器的力传递模型，通过Matlab软件对其力传递特性进行了仿真分析。以正弦信号输入对作动器的作动特性进行了试验，结果表明作动力大，作动器性能良好，可以很好适用于各种振动系统的主动振动控制。     

一种弯曲型压电堆作动器的设计及在振动控制中的应用

提出了一种弯曲型压电堆作动器并将其应用于悬臂梁主动振动控制中。新型作动器由一个压电堆和一个 型金属底座和一个预压螺钉组成。该作动器的通过底座对压电堆纵向变形的约束而使底座弯曲变形产生作动弯矩。推导了新型作动器的输出作动弯矩计算公式,并将其应用于悬臂梁振动控制中,采用热弹比拟方法结合状态空间理论建立了带新型压电作动器的悬臂梁振动控制系统状态空间方程。分别应用正位置反馈（Positive position feedback,PPF）和神经网络预测（Neural network predictive,NNP）控制方法设计了主动振动控制系统。闭环仿真结果表明,采用本文提出的新型压电堆作动器控制悬臂梁的一弯模态位移响应,应用PPF控制其幅值可降低57%,应用NNP控制其幅值可降低92%。     

HVAC系统的模糊预测函数控制器设计

针对暖通空调HVAC系统中由于存在高度非线性、时变特征以及扰动和不确定性等因素而难以控制的特点，提出基于Takagi-Sugeno(T-S)模糊模型的预测函数控制器设计方法。该方法通过最小二乘辨识算法建立系统的模糊T-S模型，然后基于模糊全局线性化预测模型，采用预测函数控制算法设计系统控制律。仿真实验结果表明该算法是一种跟踪性能好、鲁棒性强的有效控制方法。与常规的PID控制器相比，该方法具有超调量小、调整时间短等优良的动态性能。     

A polynomial chaos‐based approach to risk‐averse piezoelectric control of random vibrations of beams

This paper proposes a risk‐averse formulation for the problem of piezoelectric control of random vibrations of elastic structures. The proposed formulation, inspired by the notion of risk aversion in economy, is applied to the piezoelectric control of a Bernoulli‐Euler beam subjected to uncertainties in its input data. To address the high computational burden associated to the presence of random fields in the model and the discontinuities involved in the cost functional and its gradient, a combination of a nonintrusive anisotropic polynomial chaos approach for uncertainty propagation with a Monte Carlo sampling method is proposed. In the first part, the well‐posedness of the control problem is established by proving the existence of optimal controls. In the second part, an adaptive gradient‐based method is proposed for the numerical resolution of the problem. Several experiments illustrate the performance of the proposed approach and the significant differences that may occur between the classical deterministic formulation of the problem and its stochastic risk‐averse counterpart.     

LQG optimal controller synthesis with robust optimization in multivariable systems

Abstract

This paper presents an algorithm to synthesize a controller for treating the problem of robustness optimization in an LQG (linear‐quadratic‐Gaussian) control system. The controller not only maximizes the excess stability margin in perturbed system but also minimizes the cost functional J in LQG problems by specifying two frequency dependent weighting matrices Q(s) and R(s) in the cost functional.

Our approach is based on Wiener‐Hopf's technique (frequency domain approach), and two weighting matrices in cost functional are shaped by inverse LQG method. The feature of this paper is that the plant of the system has no stable, proper, square, and minimum phase constraints.     

Function-based design synthesis approach to design reuse

In this paper, a design reuse framework with a function-based design synthesis approach is proposed in the context of conceptual product development. Previous researches in design reuse have lacked a comprehensive functional base for knowledge representation and reasoning. The method presented in this paper uses a function-based product information model and a multiple objective optimization model to achieve design reuse. The information model is dependent on a functional core which is the key element vector. It is capable of modeling product information with sufficient abstraction, which in turn facilitates intelligent construction of product platforms. The multiple objective optimization method carries out automated design synthesis and evaluation subject to various design constraints. The approach has been applied in the design of the fan filter unit, a key clean room device. It has achieved intelligent design reuse in product conceptual design with significant rapidity and solution variety.     

EEG source localization using a sparsity prior based on Brodmann areas

Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.     

3D Printed Functional and Biological Materials on Moving Freeform Surfaces

Conventional 3D printing technologies typically rely on open‐loop, calibrate‐then‐print operation procedures. An alternative approach is adaptive 3D printing, which is a closed‐loop method that combines real‐time feedback control and direct ink writing of functional materials in order to fabricate devices on moving freeform surfaces. Here, it is demonstrated that the changes of states in the 3D printing workspace in terms of the geometries and motions of target surfaces can be perceived by an integrated robotic system aided by computer vision. A hybrid fabrication procedure combining 3D printing of electrical connects with automatic pick‐and‐placing of surface‐mounted electronic components yields functional electronic devices on a free‐moving human hand. Using this same approach, cell‐laden hydrogels are also printed on live mice, creating a model for future studies of wound‐healing diseases. This adaptive 3D printing method may lead to new forms of smart manufacturing technologies for directly printed wearable devices on the body and for advanced medical treatments.     

Isogeometric shape optimization for quasi‐static processes

A framework to solve shape optimization problems for quasi‐static processes is developed and implemented numerically within the context of isogeometric analysis (IGA). Recent contributions in shape optimization within IGA have been limited to static or steady‐state loading conditions. In the present contribution, the formulation of shape optimization is extended to include time‐dependent loads and responses. A general objective functional is used to accommodate both structural shape optimization and passive control for mechanical problems. An adjoint sensitivity analysis is performed at the continuous level and subsequently discretized within the context of IGA. The methodology and its numerical implementation are tested using benchmark static problems of optimal shapes of orifices in plates under remote bi‐axial tension and pure shear. Under quasi‐static loading conditions, the method is validated using a passive control approach with an a priori known solution. Several applications of time‐dependent mechanical problems are shown to illustrate the capabilities of this approach. In particular, a problem is considered where an external load is allowed to move along the surface of a structure. The shape of the structure is modified in order to control the time‐dependent displacement of the point where the load is applied according to a pre‐specified target. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamic component chemiluminescent sensor for assessing circulating polymorphonuclear leukocyte activity of peritoneal dialysis patients

Recurrent bacterial peritonitis is a major complication in peritoneal dialysis (PD) patients, which is associated with polymorphonuclear leukocyte (PMN) functional changes and can be assessed by a chemiluminescent (CL) reaction. We applied a new approach of a dynamic component chemiluminescence sensor for the assessment of functional states of PMNs in a luminol-amplified whole-blood system. This method is based on the evaluation of CL kinetic patterns of stimulated PMNs, while the parallel measurements of intracellular and extracellular production of reactive oxygen species (ROS) from the same sample can be conducted. Blood was drawn from diabetic and nondiabetic patients during follow-up, and during peritonitis. Healthy medical personnel served as the control group. Chemiluminescence curves were recorded and presented as a sum of three biological components. CL kinetic parameters were calculated, and functional states of PMNs were assessed. Data mining algorithms were used to build decision tree models that can distinguish between different clinical groups. The induced classification models were used afterward for differentiating and classifying new blind cases and demonstrated good correlation with medical diagnosis (84.6% predictive accuracy). In conclusion, this novel method shows a high predictive diagnostic value and may assist in detection of PD-associated clinical states.     

A Dirichlet–Neumann cost functional approach for the Bernoulli problem

The Bernoulli problem is rephrased into a shape optimization problem. In particular, the cost function, which turns out to be a constitutive law gap functional, is borrowed from inverse problem formulations. The shape derivative of the cost functional is explicitly determined. The gradient information is combined with the level set method in a steepest descent algorithm to solve the shape optimization problem. The efficiency of this approach is illustrated by numerical results for both interior and exterior Bernoulli problems.     

Reversible switching of microtubule motility using thermoresponsive polymer surfaces

We report a novel approach for the dynamic control of gliding microtubule motility by external stimuli. Our approach is based on the fabrication of a composite surface where functional kinesin motor-molecules are adsorbed onto a silicon substrate between surface-grafted polymer chains of thermoresponsive poly(N-isopropylacrylamide). By external temperature control between 27 and 35 degrees C, we demonstrate the reversible landing, gliding, and releasing of motor-driven microtubules in response to conformational changes of the polymer chains. Our method represents a versatile means to control the activity of biomolecular motors, and other surface-coupled enzyme systems, in bionanotechnological applications.