Modeling and Hierarchical Neuro-Fuzzy Control for Flexure-Based Micropositioning Systems

The micropositioning system using flexural bearing (e.g., for wafer steppers and coarse-fine positioning systems) is a system of infinite degrees of freedom. It is difficult to design a controller for the partial differential equation of the system directly. In this paper, a closed-form dynamics model is first developed using the assumed modes method and the least squares method. Then, a hierarchical neuro-fuzzy controller using backpropagation (BP) training algorithm is proposed for the precision control and active damping of the micropositioning system. Simulation results show that the suggested strategy can actively suppress the flexible vibration and have high positioning performance.     

结合模糊控制的时序数据预测循环神经网络

时序数据处理任务中,循环神经网络模型以及相关衍生模型有较好的性能,如长短期记忆模型(LSTM),门限循环单元(GRU)等.模型的记忆层能够保存每个时间步的信息,但是无法高效处理某些领域的时序数据中的非等时间间隔和不规律的数据波动,如金融数据.本文提出了一种基于模糊控制的新型门限循环单元(GRU-Fuzzy)来解决这些问...     

基于分层模糊控制的弹性倒立摆系统

随着技术水平的提高,对许多机械系统的高速化和轻量化的要求也变得越来越苛刻,这就需要以减小其重量作为代价,而重量的减少将引起其弹性刚度的减弱,导致系统振动的加剧。为了实现对具有弹性的不稳定系统的控制,该文重点对弹性倒立摆的稳定性控制进行了研究,利用分层模糊控制实现了弹性倒立摆在可移动小车上的动态平衡。通过仿真和具体实验的结果证明,该系统具有良好的鲁棒稳定性。     

混凝土强度模糊神经网络检测系统

为了提高检测精度,建立了模糊神经网络来综合评定结构的混凝土强度,充分利用了钻芯法和回弹法这两种常用混凝土测强方法的特点以及模糊神经网络的自学习、泛化和模糊逻辑推理功能.根据回弹值与钻芯值之间趋于幂函数关系的专家经验,将回弹值和钻芯值分别取常用对数作为模型的输入和输出,以提高建模精度.同时,模型参数采用一种混合学习算法确定,可以提高学习速度.实验结果表明,模型预测结果的平均相对误差为10.316%,相对标准差为12.895%,满足工程实际要求.该方法可以有效地映射出钻芯、回弹数据间复杂的非线性关系,为混凝土强度检测评定提供了一种有效的途径.     

A Neuro-Fuzzy Approach for Compensating Color Backlight Images

This paper presents a neuro-fuzzy approach for compensating exposure in the case of backlighting, regardless of the position of objects. To achieve the compensation effect, the fuzzy C-means algorithm is first used to extract features from a backlight image. Then these extracted features are presented to a trained artificial immune system based neuro-fuzzy system (AISNFS) to estimate the amount of compensation. Finally, the estimated amount of compensation incorporated with a compensation equation is used to enhance the intensity component of the backlight image to produce a compensated image. Several backlight images were used to test the performance of the algorithm.     

Boost变换器的T-S模糊建模与控制

针对Boost变换器的非线性特性,考虑变换器参数不同情况对系统模型的影响,分别建立了参数确定和参数不确定条件下Boost变换器的等价T-S模糊模型。基于建立的等价T-S模糊模型,利用Lyapunov函数方法和线性矩阵不等式方法,给出了Boost变换器并行分配补偿模糊控制器的参数化设计方法。仿真结果表明,所建立的Boost变换器T-S模糊模型是可靠的,所设计的模糊控制器与模糊PI相比具有较强的鲁棒性和抗扰性。     

基于TS模糊区域模型的航天器姿态控制

由于燃料消耗和柔性部件展开等原因,挠性航天器的姿态动力学方程存在着参数的不确定性,因此,研究参数不确定TS模糊系统的鲁棒控制器设计方法,并将其应用到平面机动挠性航天器的姿态机动控制问题.将参数不确定TS模糊系统描述为TS模糊区域模型,根据PDC原理设计了模糊区域控制器,并用分段Lyapunov法证明了模糊控制系统的全局...     

自适应神经模糊控制器及其在炉温控制系统中的应用

将自适应模糊控制技术与神经网络技术相结合,提出了一种自适应神经模糊控制器的实现方法,并用一种改进的快速ＢＰ算法来训练网络。该方法和算法用于炉温控制系统,获得了满意的控制效果,验证了方法和算法的有效性。     

Stable Adaptive Fuzzy Control with TSK Fuzzy Friction Estimation for Linear Drive Systems

This paper considers the control of a linear drive system with friction and disturbance compensation. A stable adaptive controller integrated with fuzzy model-based friction estimation and switching-based disturbance compensation is proposed via Lyapunov stability theory. A TSK fuzzy model with local linear friction models is suggested for real-time estimation of its consequent local parameters. The parameters update law is derived based on linear parameterization. In order to compensate for the effects resulting from estimation error and disturbance, a robust switching law is incorporated in the overall stable adaptive control system. Extensive computer simulation results show that the proposed stable adaptive fuzzy control system has very good performances, and is potential for precision positioning and trajectory tracking control of linear drive systems.     

制糖过程中递归模糊神经网络软测量技术

糖厂澄清工段过程包含复杂的物理和化学反应,具有非线性、大滞后和不确定性的特点,难以建立精确的机理模型.常规神经网络建模是静态映射,实际应用中,权值的调节不能充分利用工业生产现场的动态数据信息,效果不理想.为此,提出了含有递归环节的T-S模糊神经网络(TSRFNN)结构,采用混沌BP学习算法引入非线性自反馈项获得复杂系统的动力学特征,通过与常规T-S模糊神经网络(TSFNN)在糖厂澄清工段过程的建模与仿真试验中进行比较,结果表明,在处理这类时变复杂系统建模方面TSRFNN表现出更加优越的性能,获得了非线性系统的全局最优模型.     

新型电梯群控系统的建模和控制策略研究

针对电梯群控系统所要解决的是一个复杂的、具有非线性、不确定性的多目标随机系统的决策问题引入了细胞自动机理论建立电梯群控制系统的模型,模糊控制算法实现群控策略。该群控系统目前已经在实际运行,测试结果证明其运行可靠,性能稳定,有很好的实用性和推广价值。     

自适应翼肋的建模与分布式协调控制

针对分布式驱动的自适应翼肋进行建模与分布式协调控制研究。基于分析力学的方法建立了自适应翼肋的动力学模型。以这个非线性关联动力学模型为基础,采用Takagi—Sugeno（T—S）模糊逼近理论,建立了自适应翼肋的仿射型T—S模糊关联模型。对仿射型T—S模糊关联模型的物理耦合项进行变换,将系统模型写成空间关联系统的形式,以解耦控制器设计条件。基于并行分配补偿理论,针对系统模型具有耦合项和非零常数项的特点,设计了满足鲁棒性能指标的包含耦合项和偏置项的分布式协调控制器。控制器设计条件具有线性矩阵不等式的形式,并且只包含单个驱动单元的参数,计算量较小。仿真结果表明所设计的自适应翼肋分布式协调控制器,能够在外界扰动作用下使翼肋的形状收敛到期望翼型;翼肋在变形过程中能保持光滑连续的外形。     

Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control

The paper presents a general methodology of adaptive control based on fuzzy model to deal with unknown plants. The problem of parameter estimation is solved using a direct approach, i.e. the controller parameters are adapted without explicitly estimating plant parameters. Thus, very simple adaptive and control laws are obtained using Lyapunov stability criterion. The generality of the approach is substantiated by Stone-Weierstrass theorem, which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control, this implies the adaptive law with fuzzified adaptive control parameters. The proposed control algorithm may be viewed as an extension of classical adaptive control for linear plants, but compared to the latter it provides higher adaptation ability and consequently better performance if the plant is nonlinear. The global stability of the control system is assured and the tracking error converges to the residual set that depends on fuzzification properties. The main advantage of the approach is simplicity that suits control engineers since wide range of industrial processes can be controlled by the proposed method. In the paper, the control of heat exchanger is performed.     

PID与模糊控制算法的比较及其改进

分析比较了PID控制和模糊控制算法，针对一般PID控制容易产生超调，模糊控制的稳态精度不高的缺陷，提出了相应的改进算法－复合PID算法和带动态补偿的模糊控制算法，并通过水温控制仿真实验，比较了这几种控制算法的控制效果，实验结果证明，这两种改进算法不仅系统超调小，而且具有较高的稳态控制精度，可广泛应用于精确控制领域。     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

混沌范德玻—杜芬系统的T—S模糊控制

针对混沌ADVP（范德玻—杜芬）系统,进行了T-S模糊建模和模糊控制器设计,实现了系统的稳定。在用T-S模糊模型精确重构系统结构的基础上,利用反馈同步思想和极点配置方法,基于并行分布补偿（PDC）技术,进行了控制器设计。整个设计过程只需在模糊模型基础上作极点配置,简化了计算,得到了简单且易实现的控制器。仿真表明,受控系统能够快速达到收敛,验证了方法的有效性。     

单连杆柔性臂负载自适应模糊滑模控制

针对单连杆柔性臂,提出了负载自适应模糊滑模控制与最优控制相结合的混合控制方法。首先,采用奇异摄动将系统分为慢变和快变两个子系统。然后,对慢变子系统采用负载自适应模糊滑模控制,快变子系统采用最优控制。最后,仿真结果表明,该方法不仅能实现柔性臂轨迹的快速、准确跟踪,有效地抑制弹性振动,并且对负载的变化具有强的鲁棒性。     

并联Boost变换器T-S建模与控制

在并联Boost变换器建模过程中,含有状态量的输入矩阵使得模型具有非线性特点,本文针对并联Boost变换器的这一特点,建立Boost变换器等价T-S模型,并根据建立的模型,利用Lyapunov函数方法和线性不等式方法,采用并行分配补偿(PDC)策略设计非线性T-S模糊控制器。仿真结果表明:本文所建立的并联Boost变换器的模型是准确的,设计的T-S模糊控制器是可靠的,系统工作稳定并且具有较强的抗扰动性能。     

Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators

In this paper, a robust stable fuzzy control design based on feedback linearization is presented. Takagi–Sugeno fuzzy model is used as representing the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. For this structured uncertainty, the closed system can be analyzed by applying the perturbation system stability analysis to the fuzzy feedback linearization systems and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. Based on the developed analysis method, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition.     

Fuzzy Modeling and Control for Conical Magnetic Bearings Using Linear Matrix Inequality

A general nonlinear model with six degree-of-freedom rotor dynamics and electromagnetic force equations for conical magnetic bearings is developed. For simplicity, a T–S (Takagi–Sugeno) fuzzy model for the nonlinear magnetic bearings assumed no rotor eccentricity is first derived, and a fuzzy control design based on the T–S fuzzy model is then proposed for the high speed and high accuracy control of the complex magnetic bearing systems. The suggested fuzzy control design approach for nonlinear magnetic bearings can be cast into a linear matrix inequality (LMI) problem via robust performance analysis, and the LMI problem can be solved efficiently using the convex optimization techniques. Computer simulations are presented for illustrating the performance of the control strategy considering simultaneous rotor rotation tracking and gap deviations regulation.