基于新型切换模糊系统的控制方法研究

提出一类新型的切换模糊系统,即以聚类型模糊集合为基础的切换模糊系统.模糊模型的前件采用聚类型模糊集合和隶属度函数的形式.在研究中,首先讨论聚类型模糊集合的切换模糊系统形式和机理;然后,使用多Lyapunov函数给出离散切换模糊系统在满足最小驻留时间切换策略下的指数稳定性条件,并设计出切换律;最后,通过仿真实例验证所提出方法的有效性.     

前提不匹配的模糊时滞系统镇定条件的改进

本文研究了前提不匹配的Tagaki-Sugeno(T–S)模糊时滞系统的镇定问题.与一般的T–S模糊时滞系统相比,该系统中模糊模型与模糊控制器拥有不同的模糊规则数与不同的隶属度函数.基于Lyapunov稳定性理论,通过引进新型积分不等式,给出了包含隶属度函数信息的镇定条件.本文提出的新方法充分考虑了隶属度函数的信息,同时得到了Lyapunov函数导数的最小下界,因此新的镇定条件比以往结果具有更小的保守性.另一方面给出前提不匹配的控制器设计方法,由于模糊控制器的隶属度函数可以任意选取,因此提高了控制器设计的灵活性.最后仿真实例证明了本文方法的有效性及优越性.     

离散线性开关系统切换的模糊方法

在已知离散线性开关系统全局渐近稳定前提下，将线性开关系统切换问题转化为相应T－S模型模糊区域划分问题，通过在线实时调整T－S模型隶属函数参数来确定模糊区域，以二阶离散线性开关系统为例，给出了实现系统全局渐近稳定的模糊切换策略，该方法可扩展到高阶线性开关系统，计算机仿真证实该方法简捷，有效。     

一类离散的不确定切换模糊组合系统的分散H∞控制

针对一类离散的不确定切换模糊组合系统,利用平行分布补偿算法(PDC)给出分散切换模糊控制器的设计方法,利用多Lyapunov函数方法,给出使系统稳定且H∞控制问题可解的矩阵不等式条件,并给出分散切换律设计.仿真结果表明方法的有效性.     

基于模糊方法的线性切换系统的鲁棒稳定与镇定

目前的切换方法大多都是精确切换,但是实际系统由于干扰或执行延迟等原因,常常不能按照精确的切换条件进行切换,这直接威胁系统的稳定性,因此提出切换系统要进行模糊切换的,以切换系统在模糊切换下的稳定性条件为基础.研究模糊切换下的切换系统的鲁棒稳定、镇定问题,并提出系统鲁棒稳定、镇定的充分条件.大量的仿真结果证实,提出的使切换系统鲁棒稳定、镇定的模糊方法是切实有效的.     

一类离散切换模糊系统的稳定性

针对离散模糊系统,提出一类离散切换模糊系统的稳定性问题.使用切换技术及单Lyapunov函数、多Lyapunov函数方法构造出连续状态反馈控制器,使得相应的闭环系统渐近稳定,同时设计可以实现系统全局渐近稳定的切换律.模型中的每个切换系统的子系统是离散模糊系统,取常用的平行分布补偿PDC控制器,主要条件以凸组合和矩阵不等式的形式给出,具有较强的可解性.计算机仿真结果表明设计方法的可行性与有效性.     

一类不确定网络切换模糊系统的非脆弱控制

针对一类参数不确定的网络切换模糊系统,在控制器增益存在摄动以及状态不可测的情况下,研究系统的非脆弱控制问题.采用平均驻留时间法、Lyapunov函数法以及线性矩阵不等式技术等,给出网络切换模糊系统指数稳定的平均驻留时间条件以及系统切换律设计,并给出基于观测器的反馈控制器设计方法和使系统指数稳定的矩阵不等式条件,并将此条...     

一类不确定切换模糊时滞系统的鲁棒控制

针对利用模糊T-S模型建模的切换模糊系统,考虑当系统同时存在不确定和时滞的情况下,研究系统的状态反馈控制问题.利用切换技术和多Lyapunov函数方法,给出状态反馈控制器存在的充分条件,相应结果以矩阵不等式形式给出,并给出切换律设计.利用平行分布补偿算法(PDC),给出切换模糊状态反馈控制器设计,使得闭环系统在所设计的控制器和切换律下,对所有允许的不确定具有鲁棒性.仿真结果表明方法的有效性.     

基于T-S模糊模型的多时滞非线性网络切换控制系统非脆弱H∞控制

针对一类控制器增益存在摄动的不确定非线性网络切换系统,在系统同时存在随机时变时滞和数据包丢失的情况下,研究系统的非脆弱H_∞控制问题.首先,利用T-S模型,将非线性网络切换系统建模为网络切换模糊系统;其次,将数据包丢失作为时滞处理,并采用Bernoulli分布的随机序列描述该时滞;再次,采用平均驻留时间的方法(ADT)设计系统的切换律及非脆弱状态反馈控制器,并给出网络切换模糊时滞系统指数稳定的平均驻留时间条件;最后,结合李雅普诺夫(LKF)方法给出系统均方指数稳定且满足H_∞性能指标的充分条件.仿真结果验证了所提出设计方法的有效性.     

一种神经模糊系统模型

本文提出一种神经模糊系统模型,其中模糊规则前件用π隶属函数(形状类似于三角形隶属函数,但具有平滑性)表达,给出了类似于BP的参数学习算法.对于平滑函数近似问题的仿真结果表明,与模糊规则前件使用三角形隶属函数的神经模糊系统模型相比,本文提出的模型具有学习过程更加稳定平滑和逼近误差小的优点.对这两种模型性能上的差异做了定性解释.     

不确定非线性网络化系统的鲁棒H_∞控制

用T-S(Takagi-Sugeno)模糊方法研究了带有参数不确定的非线性网络化系统的鲁棒控制.首先,考虑到诱导时延和数据丢包等网络因素的影响,基于事件驱动的保持器的更新序列建立闭环反馈系统的采样模型,并将其转化为状态中附加两个时滞变量的连续T-S模糊系统.然后,利用时滞系统方法,分析不确定闭环模糊系统的鲁棒H∞性能,并设计相应的鲁棒H∞模糊控制器.最后,仿真例子表明了方法的有效性.     

A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.     

非线性离散时间系统的自适应模糊补偿控制

针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性.     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.     

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF-THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.     

Enhancement of robust performance for fuzzy parametric uncertain time‐delay systems using differential evolution algorithm

This paper proposes a systematic methodology for the enhancement of robust stability and performance of a fuzzy parametric uncertain time‐delay system. A fuzzy parametric uncertain time‐delay system is an example for a linear time‐invariant uncertain time‐delay system with fuzzy coefficients. By using the nearest approximation, these fuzzy coefficients are approximated into crisp sets called intervals to get an interval system. The proposed approach develops the necessary and sufficient stability conditions of interval polynomials for determining the robust stability. Then, by using these developed stability conditions, a set of inequalities in terms of controller parameters are obtained from the closed‐loop characteristic polynomial of fuzzy parametric uncertain time‐delay system. Finally, these inequalities are solved to obtain robust controller with the help of a differential evolution algorithm for an unstable fuzzy parametric uncertain time‐delay system. Consequently, a lead‐lag compensator is constructed based on the frequency domain approach to improve the performance of the fuzzy parametric uncertain time‐delay system. The proposed method has the advantage of less computational complexity and easy to implement on a digital computer. The viability of the proposed methodology is illustrated through a numerical example for its successful implementation. The efficacy of the proposed methodology is also evaluated against the available approach in the literature and the simulation results are successfully implemented for robust stability and performance of fuzzy parametric uncertain time‐delay systems.     

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.     

一般齐次T-S模糊系统的逼近性能

为剖析一般齐次T-S模糊系统的逼近性能,通过广泛总结常用模糊集的特点,明确定义了一种具有普遍意义的输入空间的一般模糊划分(GFP).基于输入采用GFP的一般齐次T-S模糊系统的解析结构,证明了该类一般齐次T-S模糊系统能够以任意精度逼近任意非线性函数,并得到了一个其作为通用逼近器的充分条件.作为GFP的一种退化,进一步研究了输入采用线性模糊划分(LFP)的一般齐次T-S模糊系统的一阶逼近性能.仿真实例验证了所得理论结果的有效性,并考察了充分条件的保守性.这为基于齐次T S模糊模型的复杂系统建模与控制提供了理论指导.     

Symbolical Reasoning about Numerical Data: A Hybrid Approach

By combining methods from artificial intelligence and signal analysis, we have developed a hybrid system for medical diagnosis. The core of the system is a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, automatically from given examples and indirectly formulated by the physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. A complex signal transformation preprocesses the digital data a priori to the symbolic representation. Results demonstrate the high accuracy of the system in the field of diagnosing electroencephalograms where it outperforms the visual diagnosis by a human expert for some phenomena.     

CLUSTERED BASED TAKAGI‐SUGENO NEURO‐FUZZY MODELING OF A MULTIVARIABLE NONLINEAR DYNAMIC SYSTEM

This research frame work investigates the application of a clustered based Neuro‐fuzzy system to nonlinear dynamic system modeling from a set of input‐output training patterns. It is concentrated on the modeling via Takagi‐Sugeno (T‐S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Hence, such created initial memberships are then employed to construct suitable T‐S sub‐models. Furthermore, the T‐S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). Compared to other well‐known approximation techniques such as artificial neural networks, fuzzy systems provide a more transparent representation of the system under study, which is mainly due to the possible linguistic interpretation in the form of rules. Such intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of fuzzy if‐then rules. The developed T‐S Fuzzy modeling system has been then applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Validation results have resulted in a very close antenna sub‐models of the original nonlinear antenna system. The suggested technique is very useful for development transparent linear control systems even for highly nonlinear dynamic systems.