输出约束的有限时间自适应区间二型模糊输出反馈PMSM伺服控制

针对永磁同步电机驱动的伺服系统在不确定性摩擦和未知负载的影响下难以达到高精度的控制效果,提出一种基于区间二型模糊系统的带有输出约束的有限时间自适应输出反馈控制方案.首先,构建一个基于非线性扰动观测器的区间二型模糊状态观测器,分别完成对于未知扰动和速度的估计,区间二型模糊系统完成对于非线性摩擦的逼近;然后,在此基础上,结合滤波误差补偿机制和有限时间技术,引入障碍Lyapunov函数和反步控制技术设计输出约束的自适应区间二型模糊输出反馈控制器;最后,根据Lyapunov稳定性理论提出严格的稳定性分析,保证闭环系统的所有信号均是有限时间内有界的,并通过数值仿真和实验验证了所提出方法的有效性.     

基于数据挖掘与系统理论建立摩擦模糊模型与控制补偿

建立机械摩擦力模型及其相应的控制补偿策略一直是人们所关注的问题. 由于摩擦力所固有的非线性及不确定特征, 用传统的数学建模与控制补偿方法难以达到满意的系统性能要求. 本文采用模糊建模技术逼近摩擦动力系统并将辨识结果用在前馈补偿控制器设计中. 模糊建模过程由以下3个部分组成: 首先采用数据挖掘技术辨识出模糊系统的模糊规则库, 然后利用该规则库建立模糊系统的静态模型, 最后以李雅普诺夫稳定性理论为基础进一步辨识出模糊系统的动态模型. 在控制器设计方面, 采用了自适应模糊系统前馈补偿的比例微分(Proportional-derivative, PD)算法. 运用李雅普诺夫稳定性分析证明了闭环系统跟踪误差的有界性. 数值仿真结果表明了该方法的有效性和实用性.     

线控转向系统的自适应高阶滑模控制

车辆线控转向(steer-by-wire,SbW)系统存在摩擦力矩及回正力矩等不确定动态特性,难以实现精确建模与有效控制.为此,提出一种基于自适应模糊逻辑系统的自适应高阶滑模(adaptive higher-order sliding mode,AHOSM)方法,实现SbW系统的有效控制.首先,通过自适应模糊逻辑系统逼...     

区间二型模糊集和模糊系统: 综述与展望

一型模糊集可以建模单个用户的语义概念中的不确定性, 即个体内不确定性. 一型模糊系统在控制和机器学习中得到了大量成功应用. 区间二型模糊集能同时建模个体内不确定性和个体间不确定性, 因而在很多应用中显示了比一型模糊系统更好的性能, 是近年来的研究热点. 本文首先介绍了区间二型模糊集的重要概念和理论研究进展, 总结了其在决策和机器学习中的成功应用, 然后介绍了区间二型模糊系统的基本操作和理论研究进展, 并回顾了其在控制和机器学习中的典型应用. 最后, 对区间二型模糊集和模糊系统未来的研究方向进行了展望.     

考虑LuGre 摩擦的伺服系统自适应模糊控制

针对摩擦非线性的存在会使伺服系统控制精度难以提高的问题,建立了考虑动态LuGre摩擦的伺服系统数学模型,在系统参数和负载转矩未知的情况下设计了自适应模糊控制器,用自适应模糊逻辑系统在线逼近包含LuGre摩擦在内的非线性环节,从而实现了伺服系统高精度的位置跟踪。利用Lyapunov函数证明了闭环系统的稳定性。仿真结果表明,该控制器能有效地补偿摩擦非线性的影响,并对负载转矩变化具有较强的鲁棒性。     

基于扰动观测器的水面无人船自适应模糊控制器设计

为了使水面无人船(USV)获得更好的跟踪性能,本文设计了基于扰动观测器和命令滤波器的自适应模糊控制器.对于该系统存在建模不确定性和外部环境的扰动,采用模糊逻辑系统(FLS)和一个新的扰动观测器对其进行逼近和补偿.在扰动观测器和控制器中加入了一个新的自适应参数,用来改善控制精度.基于此,本文设计了命令滤波反步控制方法,可以保证系统在所有状态下都是有界的,且跟踪误差在有限时间内小于规定的精度.仿真结果显示该方法有效,且可以满足给定的控制精度.     

具有H∞跟踪特性的机器人自适应模糊控制

为使机器人系统在有外界扰动的情况下具有良好的抗干扰能力,加快输出跟踪误差的收敛速度并提高其精度,提出了一种稳定的模糊自适应控制策略.该控制算法基于模糊逻辑系统,将模糊自适应控制器的设计与H∞控制相结合,在鲁棒补偿的基础上引入模糊补偿;并基于Lhapunov方法,给出了学习自适应律,及H∞跟踪特性的证明.对二自由度机器人的仿真结果表明,该算法使系统在跟踪误差的收敛速度和精度上都有了很大的改善,具有良好的鲁棒性和抗干扰能力.     

一类复杂控制系统分散自适应鲁棒控制器设计

基于模糊逻辑系统具有充分利用语言信息和逼近连续函数性质的思想,分析研究了一类非线性不确定复杂系统的自适应控制问题.利用系统的数学模型和模糊逻辑系统对不确定性的输出信息,设计出了复杂系统的分散自适应鲁棒控制器和模糊逻辑系统参数估计的自适应律,在较弱的假设条件下,证明了这种控制器使被控系统的状态及参数估计误差一致终极有界.仿真实例表明,所提出的方法是有效的.     

PEMFC空气供给系统的二型自适应模糊建模与过氧比控制

质子交换膜燃料电池（Proton exchange membrane fuel cell,PEMFC）空气供给系统存在外部扰动和参数不确定等动态特性,难以实现精准建模和控制.本文结合精确线性化和二型模糊逻辑系统,提出一种自适应控制器实现PEMFC空气供给系统的建模与过氧比控制.该控制器不需要PEMFC空气供给系统模型结构和参数完全已知的条件,而是通过二型模糊逻辑系统在线逼近PEMFC空气供给系统中的未建模动态并从Lyapunov函数中导出自适应参数,从而保证系统收敛性与稳定性.通过稳定性分析证明了该控制器作用下系统跟踪误差的有界性,仿真实验进一步验证了该控制器的有效性与实用性.     

基于模糊观测器T-S模型和自适应模糊逻辑系统的一类非线性系统的H∞控制

针对一类非线性系统，把模糊T-S模型和自适应模糊逻辑系统两类模糊逻辑方式结合起来，提出了一种基于观测器的控制方案．首先，应用模糊T-S模型对非线性系统建模，设计观测器来观测系统状态；由线性矩阵不等式得到模糊模型的控制律．其次，应用自适应模糊逻辑系统作为补偿器来补偿建模误差．证明了闭环系统满足期望的性能．仿真结果表明了该方案的可行性．     

区间Type-2 T-S间接自适应模糊控制

本文在Type-1 T-S间接自适应模糊控制器的基础上,利用Type-2模糊系统理论,提出了区间Type-2 T-S间接自适应模糊控制器的设计方法.由于该系统的规则前件是区间Type-2模糊集合,后件为精确数,使构造的控制方法既具备Type-2模糊集处理诸多不确定性的特点,能够减少由于规则不确定对系统的影响,同时又具有T-S模糊模型后件为各输入变量的线性组合的特点,可以提高系统的建模精度,减少系统的规则数等优点.本文利用Lyapunov合成方法,研究了在所有变量一致有界的意义下,闭环系统的全局稳定性,分析了区间Type-2 T-S间接自适应模糊控制系统的收敛性,并给出了系统参数的自适应律.通过倒立摆跟踪模型进行仿真,验证其有效性和优越性.     

基于模糊补偿的机械手鲁棒自适应模糊控制研究

多关节机械手系统中普遍存在摩擦特性、随机干扰及负载变化等非线性因素的影响。针对传统的PID控制和模糊控制很难对该类系统实现快速高精度的跟踪控制等问题,本文在模糊信息已知并且所有状态变量均可测得的情况下,设计了一种基于模糊补偿的鲁棒自适应模糊控制律。同时,为了减少模糊逼近的计算量,提高运算效率,采用了对不同的扰动补偿项加以区分、分别逼近的方法。仿真实验结果表明,这种改进的带模糊补偿的鲁棒自适应模糊控制可以很好地抑制摩擦、扰动及负载变化等非线性因素的影响。     

Interval type-2 fuzzy logic based antenatal care system using phonocardiography

The assessment of fetal wellbeing depends heavily on variations in fetal heart rate (FHR) patterns. The variations in FHR patterns are very complex in nature thus its reliable interpretation is very difficult and often leads to erroneous diagnosis. We propose a new method for evaluation of fetal health status based on interval type-2 fuzzy logic through fetal phonocardiography (fPCG). Type-2 fuzzy logic is a powerful tool in handling uncertainties due to extraneous variations in FHR patterns through its increased fuzziness of relations. Four FHR parameters are extracted from each fPCG signal for diagnostic decision making. The membership functions of these four inputs and one output are chosen as a range of values so as to represent the level of uncertainty. The fuzzy rules are constructed based on standard clinical guidelines on FHR parameters. Experimental clinical tests have shown very good performance of the developed system in comparison with the FHR trace simultaneously recorded through standard fetal monitor. Statistical evaluation of the developed system shows 92% accuracy. With the proposed method we hope that, long-term and continuous antenatal care will become easy, cost effective, reliable and efficient.     

Optimization of interval type-2 fuzzy systems for image edge detection

This paper presents the optimization of a fuzzy edge detector based on the traditional Sobel technique combined with interval type-2 fuzzy logic. The goal of using interval type-2 fuzzy logic in edge detection methods is to provide them with the ability to handle uncertainty in processing real world images. However, the optimal design of fuzzy systems is a difficult task and for this reason the use of meta-heuristic optimization techniques is also considered in this paper. For the optimization of the fuzzy inference systems, the Cuckoo Search (CS) and Genetic Algorithms (GAs) are applied. Simulation results show that using an optimal interval type-2 fuzzy system in conjunction with the Sobel technique provides a powerful edge detection method that outperforms its type-1 counterparts and the pure original Sobel technique.     

Parallel Interval Type-2 Subsethood Neural Fuzzy Inference System

Neuro-fuzzy models are being increasingly employed in the domains like weather forecasting, stock market prediction, computational finance, control, planning, physics, economics and management, to name a few. These models enable one to predict system behavior in a more human-like manner than their crisp counterparts. In the present work, an interval type-2 neuro-fuzzy evolutionary subsethood based model has been proposed for its use in finding solutions to some well-known problems reported in the literature such as regression analysis, data mining and research problems relevant to expert and intelligent systems. A novel subsethood based interval type-2 fuzzy inference system, named as Interval Type-2 Subsethood Neural Fuzzy Inference System (IT2SuNFIS) is proposed in the present work. Mathematical modeling and empirical studies clearly bring out the efficacy of this model in a wide variety of practical problems such as Truck backer-upper control, Mackey–Glass time-series prediction, Narazaki–Ralescu and bell function approximation. The simulation results demonstrate intelligent decision making capability of the proposed system based on the available data. The major contribution of this work lies in identifying subsethood as an efficient measure for finding correlation in interval type-2 fuzzy sets and applying this concept to a wide variety of problems pertaining to expert and intelligent systems. Subsethood between two type-2 fuzzy sets is different from the commonly used sup-star methods. In the proposed model, this measure assists in providing better contrast between dissimilar objects. This method, coupled with the uncertainty handling capacity of type-2 fuzzy logic system, results in better trainability and improved performance of the system. The integration of subsethood with type-2 fuzzy logic system is a novel idea with several advantages, which is reported for the first time in this paper.     

基于半监督模糊C均值算法的遥感影像分类

遥感影像数据因其固有的不确定性与复杂性,导致传统的无监督分类算法难以对其准确建模。基于模糊集理论的模式识别方法可以有效地表达数据的模糊性,其中二型模糊集能更好地刻画类间多重不确定性,而半监督法可以利用少量先验知识来解决算法对数据的泛化性问题,因此提出一种基于半监督的自适应区间二型模糊C均值遥感影像分类方法（SS-AIT2FCM）。首先,结合半监督和进化论思想,提出一种新的模糊权重指数选取方法,以提升自适应区间二型模糊C均值聚类算法的鲁棒性与泛化性,使算法更适用于光谱混叠严重、覆盖面积大、地物丰富的遥感数据分类;然后,通过对少量标记样本的软约束监督,对区间二型模糊算法迭代过程进行优化指导,来挖掘数据的最优表达。实验选用了北京颐和园区域的SPOT5多光谱遥感影像数据和广东横琴岛区域的Landsat TM多光谱遥感影像数据,对现有流行的模糊分类算法和SS-AIT2FCM的分类结果进行了比较。结果表明,SS-AIT2FCM获得了更高的分类精度与更清晰的类别边界,且有较好数据泛化能力。     

Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory

Real applications based on type-2 (T2) fuzzy sets are rare. The main reason is that the T2 fuzzy set theory requires massive computation and complex determination of secondary membership function. Thus most real-world applications are based on one simplified method, i.e. interval type-2 (IT2) fuzzy sets in which the secondary membership function is defined as interval sets. Consequently all computations in three-dimensional space are degenerated into calculations in two-dimensional plane, computing complexity is reduced greatly. However, ability on modeling information uncertainty is also reduced. In this paper, a novel methodology based on T2 fuzzy sets is proposed i.e. T2SDSA-FNN (Type-2 Self-Developing and Self-Adaptive Fuzzy Neural Networks). Our novelty is that (1) proposed system is based on T2 fuzzy sets, not IT2 ones; (2) it tackles one difficult problem in T2 fuzzy logic systems (FLS), i.e. massive computing time of inference so as not to be applicable to solve real world problem; and (3) membership grades on third dimensional space can be automatically determined from mining input data. The proposed method is validated in a real data set collected from Macao electric utility. Simulation and test results reveal that it has superior accuracy performance on electric forecasting problem than other techniques shown in existing literatures.