一类变论域自适应模糊控制器

目前常用的自适应模糊控制器存在控制精度不高和调整时间长等问题.变论域自适应模糊控制器能够显著提高控制精度并缩短其调整时间,但其控制效果对一组完备的初始模糊规则的依赖,限制了其应用的范围.针对上述问题,本文提出了一种推理规则前件论域可变,后件峰点自适应的自适应模糊控制器.Lyapnuov综合分析法证明新的控制器具有全局稳定性,并通过仿真实验表明,新控制器不仅能够提高控制精度,而且较为合理地回避了对初始模糊规则的依赖性,扩展了变论域模糊控制的适用范围.     

基于生物态势理论的Backstepping模糊控制

针对一类单输入单输出非线性系统,提出了一种基于生物态势理论的backstepping模糊自适应控制方法.设计中,将系统误差及其导数作为模糊规则的前件,将反应生物特性的生态位态势理论函数作为模糊规则的后件,设计了基于生物态势理论的Backstepping模糊控制器.该控制器将生物个体对外界扰动的适应对策引入设计中,使得控制器的自适应律具有生物自适应特性.并利用Lyapunov方法证明了闭环系统的稳定性.仿真结果进一步验证了方法的有效性.     

自组织结构的变论域模糊控制

针对变论域模糊控制,提出一种新的自组织结构的变论域模糊控制方法。自组织结构算法可以调整变论域模糊系统结构以及动态获得模糊规则,进一步减小变论域模糊控制项的稳态逼近误差。通过进一步理论分析可知,自组织结构算法仅仅保证了系统瞬时的切换是平稳的,但不能保证系统的闭环稳定性。给出了所提出控制方法的适用条件。通过与固定模糊系统结构的变论域模糊控制比较,仿真结果表明,所提出控制方法不仅使得系统的稳态跟踪误差更平稳,而且使得输入控制信号更加平滑。     

输入采用广义梯形隶属函数的最简模糊控制器结构分析

总结了应用最为广泛的三角形和梯形隶属函数的共同特点，明确定义了一种将以上两种隶属函数作为特例的广义梯形（GTS）隶属函数，推导了输入空间采用一致GTS（CGTS）模糊分划的两维最简模糊控制器的解析表达式。在此基础上，深入研究了CGTS模糊控制器的解析结构，证明了该模糊控制器等价于一种变结构的线性PI控制器与相应的定常控制偏置之和，并且在其输入论域上是单调递增、连续且有界的，同时揭示了此类控制器是一种更一般化的模糊控制器。     

一种新的无规则模糊逻辑系统构造及控制设计应用

模糊逻辑系统是一种具有输入输出功能的系统,本文构造的模糊逻辑系统的知识库并不储存If-then形式的模糊规则,而是储存关于输入和输出变量的模糊语言值,这些语言值被从属于这些语言值论域上的单位分解整合在一起,形成了若干关于输入输出变量的模糊蕴涵关系。当一组数据进入该模糊逻辑系统输入端时,它们首先被单点模糊化处理后激活这些模糊蕴涵关系,然后依据模糊推理机及合适的模糊算子得到模糊输出,最后经解模糊化得到最终输出结果。与通常的模糊逻辑系统相比较,这种模糊逻辑系统的主要优势是其输出不仅呈现为某些基函数的线性组合形式,而且组合系数的个数与输入变量的个数相同,因而这种模糊逻辑系统能够有助于解决模糊控制中的"维数灾难"问题。最后,针对一类非线性不确定系统,本文利用这种新的模糊逻辑系统设计了自适应跟踪控制器,并通过仿真算例验证了该方法的有效性。     

基于分层变论域模糊控制方法的倒车控制

为了解决一些模糊控制中的维数爆炸与精确控制问题,提出一种将分层控制思想和变论域控制思想结合起来的分层变论域模糊控制方法,并将这种模糊控制器应用于拖车倒车控制,其中下层变论域模糊控制器控制小车的方位角,上层变论域模糊控制器控制小车前轮转角.仿真实验结果与未采用分层方法的普通模糊控制器和变论域模糊控制器的控制结果作了比较,说明了新的控制方法计算量小,调整时间短且几乎无超调,具有较好的可行性与有效性.     

变论域模糊控制器及其在拖车倒车中的应用

建立变论域模糊控制器输出论城上的一种伸缩因子的微分方程模型,得到该伸缩因子的数学表达式.仿真实验表明在输出论城上使用这种新的伸缩因子构造的变论城模糊控制器可以快速准确地实现拖车倒车.在同样的条件下,与经过遗传算法参数优化的模糊控制器和普通模糊控制嚣的控制效果相比较,变论域模糊控制器控制得更加灵敏且几乎无超调.     

多输入多输出非线性最小相位系统的自适应模糊控制

针对多输入多输出非线性最小相位系统,把自适应模糊控制和自适应模糊辨识结合起来,提出了一种自适应模糊控制方案.设计辨识器用来辨识系统的未知部分;然后由跟踪误差和辨识误差给出了参数调节规律,两种误差同时调节参数改善了系统性能.模糊逻辑系统用来估计未知函数.控制方案保证了系统的稳定性,实现了有界跟踪.仿真结果表明了该方案的可行性.     

具有饱和输入和非严格反馈结构的切换非线性系统的自适应模糊控制

针对一类具有非光滑饱和输入和非严格反馈结构的任意切换规则下的非线性切换系统,给出了一种模糊减少计算量控制算法.通过构造公共Lyapunov函数,利用模糊系统对非严格反馈结构和未知不确定函数进行建模,采用辅助函数补偿饱和输入问题,基于逼近最优估计参数的界以减少在线自适应调节参数数目.该控制算法在任意切换规则下不仅可以调节输入饱和不确定性,还可以减少自适应调节参数.基于Lyapunov稳定性理论证明闭环系统的所有信号是有界的.数值仿真结果可以有效地验证控制器和自适应律设计算法的可行性.     

基于生物适应对策的间接自适应模糊控制

针对一类不确定非线性系统,提出了一种基于生物适应对策的间接自适应模糊控制方法.方法在控制器的设计中,将生态位态势理论函数作为模糊规则的后件构造模糊系统,给出了基于生物适应对策的自适应控制器.控制器的设计既体现了生物对环境的适应性,又体现了生物开发和利用环境的能力.通过实例仿真说明,控制器与常规控制器相比,具有更好的控制效果,而且具有良好的抗干扰性.     

Modelling class hierarchies in the fuzzy object-oriented data model

In this paper we describe a fuzzy logic based approach to modelling uncertainty in class hierarchies. It is shown that the traditional view of class hierarchies is subsumed in this model as a special case. The problem of multiple inheritance in class hierarchies is discussed and analyzed. The membership value derivations in the inheritance hierarchy reflects the degree of fuzziness existing in the data values and the semantics of the situation being modelled. Thus a more realistic modelling of the universe of discourse is possible through this approach. This model is compatible with existing object-oriented data models.     

Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems

In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme.     

考虑区间值的相对隶属函数与传统模糊分布函数的比较

通过分析比较,揭示了可变集理论中考虑区间值的相对隶属函数与传统的线性模糊分布函数之间的一些区别与联系,即传统的线性模糊分布函数是考虑区间值的相对隶属函数的一些特例,考虑区间值的相对隶属函数在描述模糊现象的模糊性时更具有普适性;同时指出了应用考虑区间值的相对隶属函数时应当注意的问题.     

Low-gain adaptive stabilization of infinite-dimensional second-order systems

In this paper, low-gain adaptive stabilization of infinite-dimensional undamped second-order systems is considered in the case where the input and output operators are collocated. The systems have an infinite number of poles and zeros on the imaginary axis. The adaptive stabilizer is constructed by a low-gain adaptive PI controller (proportional plus integral controller). An energy-like function and a multiplier function are introduced and low-gain adaptive stabilization of the linear second-order systems is analyzed.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.     

Observer-based robust adaptive variable universe fuzzy control for chaotic system

A novel observer-base output feedback variable universe adaptive fuzzy controller is investigated in this paper. The contraction and expansion factor of variable universe fuzzy controller is on-line tuned and the accuracy of the system is improved. With the state-observer, a novel type of adaptive output feedback control is realized. A supervisory controller is used to force the states to be within the constraint sets. In order to attenuate the effect of both external disturbance and variable parameters on the tracking error and guarantee the states to be within the constraint sets, a robust controller is appended to the variable universe fuzzy controller. Thus, the robustness of system is improved. By Lyapunov method, the observer-controller system is shown to be stable. The overall adaptive control algorithm can guarantee the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. In the paper, we apply the proposed control algorithms to control the Duffing chaotic system and Chua’s chaotic circuit. Simulation results confirm that the control algorithm is feasible for practical application.     

Stabilization of infinite‐dimensional second‐order system by adaptive PI‐controllers

In this paper adaptive stabilization of infinite‐dimensional undamped second‐order systems is considered in the case where the input and output operators are collocated. The systems have an infinite number of poles and zeros on the imaginary axis. The adaptive stabilizer is constructed by an adaptive PI‐controller (proportional plus integral controller). An energy‐like function and a multiplier function are introduced and adaptive stabilization of the linear second‐order systems is analyzed. Copyright © 2001 John Wiley & Sons, Ltd.     

Neural fuzzy point processes

Synaptic events in neural systems were described as generated by an apparatus @ possessing memory and encoding a fuzzy point process (the presynaptic discharge) into another N (the postsynaptic discharge). @ was considered to be a fuzzy automata, for which state membership is dependent on input membership and distribution as well as on a control exercised by other neural structures. In such a device, irregular input distributions favour a direct monotonic codification, whereas regular ones induce discontinuous and inverse relations between both fuzzy point processes. Both behaviors favour analogic and membership relations between the fuzzy input and output. However, there exist intermediate grades of irregularities which result in a context-free encoding, where similitude and equivalence relations predominate. The importance of such findings to neurophysiology is discussed.     

大窑湾入境集装箱量及疫情集装箱量的模糊预测研究

作为国际海运和物流业的主要装载工具集装箱是入境统计以及检验检疫的主要对象.按特定规律统计的集装箱量时间序列既有一定的统计规律,又有较大的随机性.采用模糊时间序列方法,通过构造论域、模糊集以及提出多重计算规则,对2005年6月至2012年10月大窑湾入境集装箱量、疫情集装箱量,以及疫情与入境集装箱量比值时间序列进行了模糊分析与预测.预测值与实际值的比较说明了算法的有效性.预测值与实际值的平均绝对误差、平均绝对百分误差从整体上揭示了时间序列自身的模糊性和模型的合理性.     

连续论域上Fuzzy集的信息熵

在文献[1]的基础上,把模糊熵从离散论域推广到连续论域,给出连续论域上Fuzzy集的偏熵、关联熵等概念。对其主要性质进行讨论,取得了一些令人满意的结果。拓宽信息熵的研究领域,对不确定复杂系统的信息处理、知识学习、归纳学习、模糊信息学的进一步研究打下一定基础。