Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.     

The adaptive control of nonlinear systems using the Sugeno-type offuzzy logic

An adaptive fuzzy controller is synthesized from a collection of fuzzy IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms in the fuzzy IF-THEN rules are changed according to some adaptive laws for the purpose of controlling a plant to track a reference trajectory. In the paper, a direct adaptive fuzzy control design method is developed for the general higher order nonlinear continuous systems. We use the Sugeno-type of the fuzzy logic system to approximate the controller. It is proved that the closed-loop system using this adaptive fuzzy controller is globally stable in the sense that all signals involved are bounded. Finally, we apply the method of direct adaptive fuzzy controllers to control an unstable system     

Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems

A fuzzy logic controller equipped with a training algorithm is developed such that the H^∞ tracking performance should be satisfied for a model-free nonlinear multiple-input multiple-output (MIMO) system, with external disturbances. Due to universal approximation theorem, fuzzy control provides nonlinear controller, i.e., fuzzy logic controllers, to perform the unknown nonlinear control actions and the tracking error, because of the matching error and external disturbance is attenuated to arbitrary desired level by using H^∞ tracking design technique. In this paper, a new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances. Therefore, linguistic fuzzy control rules can be directly incorporated into the controller and combine the H^∞ attenuation technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbance, data uncertainties, very well, but the adaptive type-1 fuzzy controller must spend more control effort in order to deal with noisy training data. Furthermore, the adaptive interval type-2 fuzzy controller can perform successful control and guarantee the global stability of the resulting closed-loop system and the tracking performance can be achieved.     

Design of a New Fuzzy Suction Controller Using Fuzzy Modeling for Nonlinear Boundary Layer

There are two types of fuzzy modeling: 1) imitating an expert experiment or fulfilling an engineering knowledge, and 2) modeling a complex or unknown system. In this paper, based on the first type of fuzzy modeling, a new fuzzy suction controller (NFSC) is proposed using its linguistic rules to design nonlinear boundary layer. Two kinds of nonlinear boundary layers are discussed. The first kind is designed by three rules derived according to a new interpretation of the switching conditions for a suction controller such that the new controller reduces chattering and spends less energy than a suction controller does. A design procedure summarizes the NFSC design. The second kind of nonlinear boundary layer is the linguistic rules designed to have sliding sectors to control a mobile robot for trajectory tracking. The discussion emphasizes the advantage of nonlinear boundary layers, compared with traditional suction controllers usually using linear boundary. In addition, the proposed NFSC provides a flexible way to adjust the controller functions using linguistic rules based on the first type of fuzzy modeling     

Single-chip fuzzy logic controller design and an application on a permanent magnet dc motor

This paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dc converters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibility of a high-performance non-linear fuzzy logic controller which can be implemented by using a general purpose microcontroller without modified fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.     

Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

Linguistic rules in natural language are useful and consistent with human way of thinking. They are very important in multi-criteria decision making due to their interpretability. In this paper, our discussions concentrate on extracting linguistic rules from data sets. In the end, we firstly analyze how to extract complex linguistic data summaries based on fuzzy logic. Then, we formalize linguistic rules based on complex linguistic data summaries, in which, the degree of confidence of linguistic rules from a data set can be explained by linguistic quantifiers and its linguistic truth from the fuzzy logical point of view. In order to obtain a linguistic rule with a higher degree of linguistic truth, a genetic algorithm is used to optimize the number and parameters of membership functions of linguistic values. Computational results show that the proposed method is an alternative method for extracting linguistic rules with linguistic truth from data sets.     

多变量模糊系统控制设计及其在并行混合电动汽车中的应用

利用矩阵半张量积方法研究了多变量模糊系统模糊逻辑控制器的设计,并得到了若干新的结果.首先给出了模糊规则新的表示形式,基于该表示形式,构造了模糊逻辑控制器的结构矩阵,将复杂的模糊推理转变成了简单的代数等式.然后当模糊控制规则不完全时,建立了最小入度控制算法;当模糊控制规则不一致时,给出了相应的处理方法.最后将得到的结果应用到并行混合电动汽车(PHEV)能量管理和控制策略的模糊控制器设计.     

三维模糊控制器的结构研究

基于Zadeh的模糊逻辑推理和语言控制策略,进行了三维模糊控制器的结构研 究.证明了具有线性控制规则的三维模糊控制器可等同于一个全局多层次线性关系式和一 个局部非线性PID型控制器,由此剖析了模糊控制器的推理机制和其非线性本质.     

Adaptive fuzzy PI controller with shifted control singletons

It is well known the fact that the design of a fuzzy control system is based on the human expert experience and control engineer knowledge regarding the controlled plant behavior. As a direct consequence, a fuzzy control system can be considered as belonging to the class of intelligent expert systems. The tuning procedure of a fuzzy controller represents a quite difficult and meticulous task, being based on prior data regarding good knowledge of the controlled plant. The complexity of the tuning procedure increases with the number of the fuzzy linguistic variables and, consequently, of the fuzzy inference rules and thus, the tuning process becomes more difficult. The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables. The novelty consists in extending the classic structure of the fuzzy inference core with an intelligent module, which tunes one of the control singletons, providing a significant simplification of the design and implementation procedure. The proposed strategy implements a logical, not physical, supplementation of the linguistic terms associated to the controller output. Therefore, a fuzzy rules set with a reduced number of linguistic terms is used to implement the expert control system. This logical supplementation is based on an intelligent algorithm which performs a shifting of only one of the control singletons (the singleton associated to the SMALL_ linguistic variable), its value becoming variable, a fact that allows an accurate control and a better performance for the expert control system. The logic of this intelligent algorithm is to initially provide a high controller output, followed by a slowdown of the control signal near to the operating set point. The main advantage of the proposed expert control strategy is its simplicity: a reduced number of linguistic terms, combined with an intelligent tuning of a single parameter, can provide results as accurate as other more complex available solutions involving tuning of several parameters (well described by the technical literature). Also, a simplification of the preliminary off-line tuning procedure is performed by using a reduced set of fuzzy rules. The generality of the proposed expert control strategy allows its use for any other controlled process.     

A systematic method for design of multivariable fuzzy logic controlsystems

This paper proposes a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In this work, the fuzzy rule base is generated by a rule-generated function, which is based on the negative gradient of a system performance index; the membership functions of isosceles triangle of input/output variables are fixed in the same cardinality and only the input/output scaling factors are generated from a genetic algorithm based on a fitness function. As a result, the searching space of parameters is narrowed down to a small space, the multivariable fuzzy logic controller can quickly constructed, and the fuzzy rules and the scaling factors can easily be determined. The performance of the proposed method is examined by computer simulations on a Puma 560 system and a two-inverted pendulum system     

Extracting fuzzy control rules from experimental human operatordata

This paper proposes an approach where the interpretation of manual control strategies is carried out by modeling the human operator as a fuzzy logic controller. The linguistic rules thus obtained can provide a better insight into the operator's actions, allowing mistakes to be more easily pinpointed and corrected. Instead of extracting the control rules directly from raw experimental data, an intermediary ARMA model for the operator is employed to improve the data consistency. For illustration, this method is applied to the problem of supervising an apprentice operator, with basis on rules extracted from the actions of an experienced manual operator.     

发电机组汽门系统的多模型自学习控制

针对发电机组的非线性、大范围运行等实际问题，研究了用于汽门系统的多模型自学习控制（MMSC），首先根据各种工况下的样本数据归纳出模糊控制规则；然后由模糊聚类算法将多种工况约简为典型工况，得到相应的子模型模糊控制器（FLC）．以子模型FLC输出的加权集成作为MMSC的控制输出，而加权系数取决干子模型匹配度．在子模型FLC学习优化中，由支持向量机离线逼近模糊规则曲面，再由梯度下降算法在线自学习．仿真实验验证了所设计控制器的优良性能．     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

多输入–多输出非线性系统的特征模型及在挠性卫星姿态控制中的应用

研究多输入–多输出（MIMO）高阶非仿射非线性系统的特征建模问题.首先证明了MIMO高阶非仿射非线性系统的特征模型可用二阶时变差分方程组描述,并给出了特征模型的建模误差.然后设计了基于特征模型的自适应模糊广义预测控制器,利用Lyapunov方法分析了闭环系统的稳定性.由于控制结构中使用了分层模糊逻辑系统,从而极大减少了模糊规则和可调参数的个数,提高了控制的实时性.通过对挠性卫星姿态控制的仿真研究验证了所给控制方案的有效性,可实现高精度的姿态控制,且该方法具有较强的鲁棒性.     

Vague controller: A generalization of fuzzy logic controller

In this research, a vague controller (VC) is synthesized by using the notion of vague sets, which are a generalization of fuzzy sets and characterized by a truth-membership function and a falsity-membership function. The vague sets follow the basic set operations and logic operations defined for fuzzy sets, and are superior to fuzzy sets in that they could deal with the uncertainty encountered in real-world applications in a more natural way. Depending on the vague sets, the VC is developed as a generalization of fuzzy logic controller (FLC). The design procedures of the VC, which allow an arbitrary number of input variables, and each variable could have a distinct number of linguistic values, are outlined in this paper. In order to compensate the effort in constructing two series of membership functions for vague sets and to ease the difficulties in designing VCs, a new means of designating membership functions for VCs is also presented in this article. This method constructs a set of membership functions systematically by using only two parameters: number of linguistic values of a linguistic variable and shrinking factor. The membership functions generated by this method, shrinking-span membership functions (SSMFs), have different spans over the universe of discourse and, therefore, are more rational and more practical from the human expert's point of view.     

Fuzzy logic based attitude control of the spacecraft X-38 along a nominal re-entry trajectory

Fuzzy logic control techniques are investigated for applications in the intelligent re-entry flight control of the ESA–NASA crew return vehicle. Three PD-Mamdani fuzzy controllers are constructed to control the inner-loop attitude dynamics, simulated by a fully nonlinear 3 degree-of-freedom simulator of the CRV. Each controller uses an angle tracking error and its derivative to calculate a commanded control surface deflection of the simulator. The input-domains are partitioned with 5 membership functions, resulting in 25 fuzzy rules for each rule-base. The output-domains are partitioned with 9 membership functions. The Mamdani controllers use a standard max–min inference process and a fast center of area method to calculate the crisp control signals. Simulation results show the ability to track a reference trajectory with acceptable performance, though the real strength of a nonlinear fuzzy logic controller is yet to be proven with more demanding benchmark trajectories.     

Nonlinear fuzzy controller

We consider a fuzzy controller with two inputs, triangular fuzzy numbers, Zadeh logic to evaluate linear control rules, and a center of gravity defuzzifier. We derive a closed form expression for the defuzzified output and show it is a nonlinear controller. We then analyze the nonlinearities of the fuzzy controller with respect to the PI controller.     

高速公路非线性反馈模糊逻辑匝道控制器

入口匝道控制是高速公路交通控制和智能运输系统的重要组成部分,但现有的入口匝道控制效果尚不理想.为此,本文提出一种非线性反馈方法用模糊逻辑进行入口匝道控制.建立了高速公路交通流动态模型,在此基础上,结合模糊逻辑理论设计了非线性反馈匝道控制器,根据密度误差和误差变化用模糊控制决定匝道调节率,模糊变量选用三角形隶属度函数,并制定了包含56条模糊规则的规则库,最后用MATLAB软件进行系统仿真.结果表明该控制器具有优越的动态和稳态性能,它能使高速公路主线交通流密度保持为设定的期望密度,该方法用在高速公路入口匝道控制中效果良好.     

A simplified version of mamdani's fuzzy controller: the natural logic controller

This paper proposes the natural logic controller (NLC) that it comes through a very important simplification of the Mamdani's fuzzy controller (MFC) allowing easy-design for single-input-single-output (SISO) regulation problems. Usually, fuzzy controllers are built with two classical signals of process: The error and its rate of change. They use a moderate number of fuzzy subsets and fuzzy rules. The main features of the NLC approach are that use the minimal fuzzy partition (only two fuzzy subsets per variable) and it use the minimal fuzzy rule base (only two rules). The nonlinear resulting fuzzy controller is the simplest one with an analytically well-defined, input-output mapping and accepting a linear approximation at origin. It allows easy extension to more than two signals of process. Some properties of nonlinear mapping of NLC are analyzed and some results are also presents on testing stability when NLC is used on a linear process. A special attention is addressed to the two inputs NLC case, where stability can be tested using the circle criterion. Finally, two application examples are discussed in details.