Modelling on fuzzy control systems

A kind of modelling method for fuzzy control systems is first proposed here, which is called modelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method that is different from two well-known modelling methods, that is, the first modelling method, mechanism modelling method (MMM), and the second modelling method, system identification modelling method (SIMM). This method can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inference rules describing a practice system into a kind of nonlinear differential equation with variable coefficients, called HX equations, so that the mathematical model of the system can be obtained. This means that we solve the difficult problem of how to get a model represented as differential equations on a complicated or fuzzy control system.     

Adaptive fuzzy backstepping control for a class of MIMO switched nonlinear systems with unknown control directions

In this article, an adaptive fuzzy output tracking control approach is proposed for a class of multiple‐input and multiple‐output uncertain switched nonlinear systems with unknown control directions and under arbitrary switchings. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. A Nussbaum gain function is introduced into the control design and the unknown control direction problem is solved. Under the framework of the backstepping control design, fuzzy adaptive control and common Lyapunov function stability theory, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed‐loop system are bounded and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach. © 2015 Wiley Periodicals, Inc. Complexity 21: 155–166, 2016     

多重模糊蕴涵与生成模糊蕴涵的新方法

提出一种生成模糊蕴涵的新方法,即通过一个或多个选定的模糊蕴涵作多重迭代生成新的模糊蕴涵,称为多重模糊蕴涵.从选定的模糊蕴涵出发,根据不同的迭代次数,分别证明迭代结果仍为模糊蕴涵.进一步,分析当选定的模糊蕴涵具有某种性质时,所生成的多重模糊蕴涵是否仍保持这些性质.还对选定的模糊蕴涵分别为(S,N)-蕴涵和R-蕴涵这两种重要情形,进行更深入的分析.这些结果将为模糊蕴涵在控制及决策等领域的应用提供支持.     

Triple I method of approximate reasoning on Atanassov's intuitionistic fuzzy sets

Two basic inference models of fuzzy reasoning are fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT). The Triple I method is a very important method to solve the problems of FMP and FMT. The aim of this paper is to extend the Triple I method of approximate reasoning on Atanassov's intuitionistic fuzzy sets. In the paper, we first investigate the algebra operators' properties on the lattice structure of intuitionistic fuzzy information and provide the unified form of residual implications which indicates the relationship between intuitionistic fuzzy implications and fuzzy implications. Then we present the intuitionistic fuzzy reasoning version of the Triple I principles based on the models of intuitionistic fuzzy modus ponens (IFMP) and intuitionistic fuzzy modus tollens (IFMT) and give the Triple I method of intuitionistic fuzzy reasoning for residual implications. Moreover, we discuss the reductivity of the Triple I methods for IFMP and IFMT. Finally, we propose α-Triple I method of intuitionistic fuzzy reasoning.     

Theory of T-norms and fuzzy inference methods

In this paper, the theory of T-norm and T-conorm is reviewed and the T-norm, T-conorm and negation function are defined as a set of T-operators. Some typical T-operators and their mathematical properties are presented. Finally, the T-operators are extended to the conventional fuzzy reasoning methods which are based on the and operators. This extended fuzzy reasoning provides both a general and a flexible method for the design of fuzzy logic controllers and, more generally, for the modelling of any decision-making process.     

孵化控制系统的模糊免疫PID控制算法研究

家禽孵化是一个复杂的生物学过程,实现其自动控制水平有着重大意义.针对孵化系统是一个多变量、强耦合、大滞后的复杂动态系统,提出一种模糊免疫P ID控制方法,该方法根据模糊控制原理对P ID参数进行在线修改,利用生物免疫机理调整非线性函数,然后用免疫修正进一步调整P ID系统参数,使被控对象具有良好的性能,实现了家禽孵化设备中温度、湿度和含氧量的智能控制.系统投入运行表明,动态响应好,控制精度高,鲁棒性高,易于各种孵化的实现,从而提高了孵化率.     

车流高峰期交通信号的模糊控制及仿真

用较少的模糊规则控制车流高峰期的交通信号,建立仿真系统模拟单交叉口二相位交通流,利用遗传算法对模糊规则进行优化并找出绿灯时间模糊集隶属函数的最优边界.实验结果表明,优化后的模糊规则对高峰期的车流变化具有更强的适应性.     

Array approach to fuzzy logic

Promising results from applying an array-based approach to two-valued logic suggests its application to fuzzy logic. The idea is to limit the domain of truth-values to a discrete, finite domain, such that a logical relationship can be evaluated by an exhaustive test of all possible combinations of truth-values. The paper presents a study of the topic from an engineer's viewpoint. As an example 31 logical sentences valid in two-valued logic were tested in three-valued logic using the nested interactive array language, Nial. Out of these, 24 turned out to be valid in a three-valued extension based on the well-known S^* implication operator, also called “Gödel's implication operator”. Applications to automated approximate reasoning and fuzzy control are also illustrated.     

Trajectory stabilization of a model car via fuzzy control

This paper deals with trajectory stabilization of a computer simulated model car via fuzzy control. Stability conditions of fuzzy systems are given in accordance with the definition of stability in the sense of Lyapunov. First, we approximate a computer simulated model car, whose dynamics is nonlinear, by T-S (Takagi and Sugeno) fuzzy model. Fuzzy control rules, which guarantee stability of the control system under a condition, are derived from the approximated fuzzy model. The simulation results show that the fuzzy control rules effectively realize trajectory stabilization of the model car along a given reference trajectory from all initial positions under a condition and the dynamics of the approximated fuzzy model agrees well with that of the model car.     

Fuzzy relation equations theory as a basis of fuzzy modelling: An overview

We give a wide overview on the applications of fuzzy relation equations theory to decision-making processes, to the construction of preference relations and to Knowledge Engineering, mainly fuzzy control and fuzzy pattern recognition. General theoretical results and methodological aspects are pointed out.     

A general fuzzy control scheme for nonlinear processes with stability analysis

In this paper we develop a general fuzzy control scheme for nonlinear processes. Assuming little knowledge about the dynamics of the controlled process, the proposed scheme starts by probing the process at different points in its operating region to generate a fuzzy quantisation. A simple local controller is then designed at each fuzzy locality. A fuzzy inference mechanism then links up tje local controllers to form a global controller which can be further refined by the learning algorithm. By employing a newly developed structure-adaptive fuzzy modelling scheme, the appropriate fuzzy rule-base for the inference mechanism can be extracted stably and efficiently. The conditions for the stability of the global controller are rigourously established. Simulation results are presented to illustrate the effectiveness of the scheme.     

时变滞后系统的一种自校正混合模糊PID控制

普通模糊控制不能对时变滞后系统进行有效控制 ,甚至使系统失去稳定 .在 W.L.Bialkowski 1 983年提出的混合模糊 PID控制器的基础上 ,提出了一种自补偿混合模糊 PID控制器 ,并在此基础上提出了一种对积分系数 KI进行自校正的算法 .经 MATLAB仿真验证 ,该算法具有良好的控制品质 ,适应对象参数大范围变化的时滞系统 ,且易于工程实现 .     

Fuzzy measure of fuzzy events defined by fuzzy integrals

We define the concept of fuzzy measure of a fuzzy event by using a general form of fuzzy integral proposed by Murofushi, called fuzzy t-conorm integral, encompassing previous definitions. Zadeh defined the probability measure of a fuzzy event, and later the possibility measure of fuzzy event. Using a duality property of fuzzy t-conorm integral, we propose a general definition of fuzzy measure of fuzzy events, which is compatible with previous definitions of Zadeh, and possesses all properties of a fuzzy measure, in particular the duality property. Using our definition, we examine the case of decomposable measures and belief functions. A comparison with previous works is provided.     

Quantized control for polynomial fuzzy discrete‐time systems

This article investigates the control problem for polynomial fuzzy discrete‐time systems. Signal quantization is considered in this article. To deal with this issue, a logarithmic quantizer is adopted to quantize the control signal. First, a novel method is first proposed to model polynomial fuzzy discrete‐time systems and handle the quantized control problem of the systems. Second, based on Lyapunov‐stability theory, sufficient conditions are obtained in terms of sum of squares to guarantee the asymptotical stability of the systems and satisfy a performance. Finally, a simulation example is given to illustrate the effectiveness of the proposed results. © 2014 Wiley Periodicals, Inc. Complexity 21: 325–332, 2015     

Relaxed fuzzy observer‐based output feedback control synthesis of discrete‐time nonlinear control systems

This work develops the development of observer‐based output feedback control design of discrete‐time nonlinear systems in the form of Takagi–Sugeno fuzzy model. Lately, previous results have been improved in virtue of a two‐step method. From a technical point of view, it is not flawless and related problems have not been completely resolved. In this study, more advanced two‐steps approach is further developed while the relative sizes among different normalized fuzzy weighting functions are utilized by introducing some additional matrix variables. As a result of the above work, those main defects of the existing method can be redressed and a desired solution in aspect of not only reducing the conservatism but also alleviating the computation complexity is provided for some special cases. Moreover, the effectiveness of the proposed result is shown at length by means of an illustrative example. © 2016 Wiley Periodicals, Inc. Complexity 21: 593–601, 2016