Development of Run-To-Run (R2R) controller for the multiple-input multiple-output (MIMO) system using fuzzy control theories

Run-to-Run (R2R) control has been extensively applied in semiconductor manufacturing. In particular, del Castillo, E. and Rajagopal, R., A multivariate double EWMA process adjustment scheme for drifting processes. IIE Trans., 2002, 34, 1055–1068, investigated double multivariate exponentially weighted moving average (MEWMA) controller for the multiple-input multiple-output (MIMO) system in an attempt to adjust and maintain the linear dynamic process outputs on target. Multivariate fuzzy control, inherently different from conventional MEWMA-based control, is another promising alternative that consists of fuzzy logic and set concept. Providing the fuzzy control can structure an appropriate membership function for the R2R MIMO system, thus it can be shown a practically useful control tool in comparison to MEWMA control. In this paper, fuzzy logic is utilized to design the multivariate fuzzy controller for the type of R2R applications based primarily on the min-max-gravity method advocated by Gupta, M.M., Kiszka, J.B. and Trojan, G.M., Multivariable structure of fuzzy control systems. IEEE Trans. Sys., Man Cybern., 1986, 16, 638–656. Under a variety of disturbance models, the proposed multivariate fuzzy controller can produce quite competitive control performance when compared to MEWMA control.     

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.     

基于小波变换的磁流变悬架模糊控制算法

运用小波变换的方法将汽车行驶时车身振动信号分解成若干频率成分,分解结果经模糊化处理后作为模糊控制器的输入,根据振动能量的频域分布和车辆悬架阻尼匹配原理制订模糊控制规则,实时调节磁流变阻尼器的阻尼力.实车道路试验表明,基于小波变换的模糊控制算法无论对车身振动还是车轴振动都取得了良好的抑制效果(共振峰值下降了5dB),并且比天棚阻尼控制更具优势.由于该算法只需要一个传感器测量车身加速度,不需要预测路面,是一种非常有应用前景的算法.     

Fuzzy controllers for nonlinear interconnected tmd systems with external force

Abstract

This paper investigates the effectiveness of a passive tuned mass damper (TMD) and active fuzzy controllers in reducing structural responses under external forces. In general, TMD is good for linear systems. We propose here a fuzzy controller to deal with nonlinear systems. For the fuzzy controller, a stability criterion in terms of Lyapunov's direct method is derived to guarantee the stability of interconnected TMD systems. Based on decentralized control and this criterion, a set of model‐based fuzzy controllers are then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear interconnected TMD system. Finally, an example is given to illustrate the concepts discussed throughout this paper.     

双层隔振系统量化因子模糊自调整主动控制技术研究

针对常规模糊控制器量化因子自调整困难、难以做到在线自适应的问题,提出一种应用于柴油机双层隔振系统主动控制的量化因子模糊自调整控制策略。它利用系统误差和“标准加速度”的信息作为输入,通过模糊推理实现模糊控制器输入输出量化因子的在线自调整。仿真和试验结果表明,此控制策略能明显地提高系统的隔振效果。     

A new method to deal with fuzzy classification problems by tuning membership functions for fuzzy classification systems

Abstract

This paper presents a new method to construct and tune membership functions and generate fuzzy classification rules from training instances for handling the Iris data classification problem. First, we find two attributes of the Iris data from the training instances that are suitable to serve as classification criteria. Then, we construct and tune the membership functions of these two attributes and generate fuzzy classification rules from the training instances. The proposed method generates the same number of fuzzy classification rules as the number of species of the training instances. It generates fewer fuzzy classification rules and can get a higher average classification accuracy rate than the existing methods.     

Knowledge verification for fuzzy expert systems

Abstract

The introduction and use of fuzzy logic has strengthened knowledge representation and reasoning capability in expert systems; nevertheless, it also increases the complexity and difficulty of knowledge verification, which is known to be an important issue for building reliable and high performance expert systems. In the past decade, knowledge verification problems, e.g., redundancy, conflict, circularity and incompleteness of knowledge, have been widely discussed from the viewpoint of using binary logic; nevertheless, the issue of verifying fuzzy knowledge is seldom discussed. In this paper, we attempt to detect potential structural errors among fuzzy rules by proposing a fuzzy verification algorithm. Moreover, a system for verifying fuzzy knowledge base has been developed based on the novel approach.     

Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.     

采用模糊控制的直流电机软启动及调速系统

介绍了以单片机为核心的直流电机软启动及调速系统,系统采用模糊控制策略,将转速偏差与偏差率模糊化并作为模糊控制器的输入量,根据所制定的模糊控制规则生成控制量。实验结果表明,该系统启动时间可以根据需要调整,具有良好的静态和动态性能。     

磁流变阻尼器的模糊逼近

由于磁流变液具有非线性特性，所以磁流变阻尼器的输入输出问具有很强的非线性关系。可准确描述其非线性特性的磁流变阻尼器正模型通常非常复杂，难以直接得到逆模型。考虑到某些模糊系统的万能逼近能力，本文提出用模糊系统来逼近磁流变阻尼器逆模型的新思路。根据自适应神经模糊推理系统原理，设计两个模糊系统分别逼近磁流变阻尼器的正模型和逆模型。研究结果表明：无论是正模型还是逆模型。对于训练数据，模糊系统均可以准确逼近，而对于检验数据也可比较准确逼近。正模型的逼近效果稍好，若要提高逆模型ANFIS的逼近精度．将以增加系统复杂性为代价。模糊逼近可以推广到其它的磁流变阻尼器模型中，特别是可对正模型未知的磁流变阻尼器进行建模与控制。     

基于模糊神经网络的公差-成本模型研究

研究了面向制造环境的公差-成本模型。对制造环境中有关的加工因素进行分类及模糊化处理,构造了加工因素的成本模糊影响系数,并以此系数和零件公差作为输入,建立了基于模糊神经网络的公差-成本模型。此模型在表征加工成本、公差关系上的精度较高,更适应面向制造公差设计的要求。     

Fuzzy covariance control for a class of continuous perturbed nonlinear stochastic systems

Abstract

This paper addresses the analysis and design of a fuzzy controller for a class of continuous perturbed nonlinear stochastic systems. The nonlinear systems are modeled by the Takagi‐Sugeno (T‐S) fuzzy models. The conventional LMI‐based fuzzy control method is inconvenient for directly assigning the common positive definite covariance matrix. Hence, this paper tries to develop a useful methodology to allow designers to assign a common positive definite covariance matrix for the closed‐loop system. Applying the theory of covariance control, a fuzzy controller is developed to achieve the stable conditions for the assigned common positive definite covariance matrix. Finally, a numerical example is given to demonstrate the usefulness of the proposed approach.     

Stable fuzzy control with adaptive rotor imbalance compensation for nonlinear magnetic bearing systems

Abstract

The six‐DOF nonlinear dynamics and rotor mass‐imbalance induced vibration of a high‐speed magnetic bearing are rather complex. In this paper, an analytical T‐S (Takagi‐Sugeno) fuzzy model with simplified linearly‐parameterized mass‐imbalance model for magnetic bearings is first constructed. Based on the T‐S fuzzy model, a stable fuzzy control including adaptive imbalance compensation is then proposed through robust performance analysis. Simulation validations show that the proposed control law can suppress the rotor imbalance‐induced vibration and has excellent capability for high‐speed tracking and levitation control.     

基于输出反馈的不确定非线性系统模糊鲁棒H∞控制

本文针对一类不确定非线性系统,研究了基于输出反馈的模糊鲁棒H∞控制问题。根据模糊T-S模型表征不确定非线性系统,基于状态观测器设计模糊控制器。由线性矩阵不等式和自适应律给出了模糊控制器存在的充分性条件。基于Lyapunov稳定性理论,提出的模糊控制方案在所有闭环信号最终一致有界意义下实现了期望的H∞性能。仿真结果表明了该方案的可行性。本文所提出的模糊跟踪控制器松弛了保守性,避免了匹配条件和上界。与已有的工作相比,本文约简了线性矩阵不等式的维数。     

半实物仿真的电加热炉系统辨识与模糊控制

以电加热炉为实验对象,建立基于MATLAB/Simulink的电加热炉炉温半实物仿真控制系统.通过系统辨识方法得到电加热炉的准确数学模型,使用模糊PD控制策略,先通过计算机仿真方式得到合适的控制器规则和参数,再将此控制器应用到对电加热炉的实际控制当中,对电加热炉的炉温进行控制,达到了较为理想的控制效果.实验表明,半实物仿真方法在系统建模和设计控制器、寻找合适控制器参数等方面具有方便、准确等优点,可以广泛应用到工业控制的压力、液位等其他方面的控制.     

A new query expansion method for document retrieval based on the inference of fuzzy rules

Abstract

Automatic query expansion based on user relevance feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and user relevance feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original user's query. Then, the system calculates the degree of similarity of each document with respect to the expanded user's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval.     

基于专家控制调整的机器人手臂系统模糊控制器设计

 对一种复杂的控制系统，简单的模糊控制器不能形成很好的控制效果，将专家知识应用到模糊控制器上，构成一种综合集成的智能专家模糊控制器，采用一种直接对隶属度函数参数进行矩阵式个体编码的遗传算法，对模糊控制器中隶属函数进行寻优，大大提高了系统的控制效果，同时由于专家知识的引入改善了系统对环境和机构参数变化的适应性。     

制冷系统FIRNN预测自适应模糊控制器的设计与仿真

从物理学角度将制冷控制系统看作一个弹性系统，借助有限脉冲响应神经网络（FIRNN）对被控对象的预测结果设计了直接针对系统响应曲线的能量函数和评价策略，得到一个自适应模糊控制器。将其应用于制冷系统的控制仿真后，控制性能得到显著提高。     

减摇鳍模糊免疫自适应PID控制器设计及仿真研究

针对船舶横摇的非线性模型，利用免疫反馈机理，设计了一种减摇鳍模糊免疫自适应PID控制器。控制器将模糊控制与PID控制相结合，采用Fuzzy推理，对非线性函数进行模糊逼近，用模糊免疫P调节器实时整定PID控制器的比例增益，采用常规模糊控制器在线调整免疫PID控制器的积分时间常数和微分时间常数。通过对船舶减摇鳍控制系统的仿真，可以看出采用模糊免疫自适应PID控制器其控制效果远优于常规PID控制器，使减摇鳍的减摇效果得到显著提高。     

Measuring the manufacturing process yield based on fuzzy data

The process yield is the most basic and common criterion used in the manufacturing industry as the basis for measuring process performance. In the conventional case, the underlying data for a manufacturing process are obtained from the output responses of continuous quantities that are always assumed to be real numbers. However, measurement of the output process occasionally appears to be imprecise in practical situations. Accordingly, the output responses should be assumed to be so-called fuzzy data. We propose a constructive methodology to obtain the fuzzy estimate of the yield index S _pk with the help of the extension principle of fuzzy sets theory. This study, based on an analytical approach, is an advancement over existing technology in the area of process capability analysis that is easy to implement in plant applications.