一类复杂非线性系统的模糊控制

针对一类复杂非线性系统,把模糊T-S模型和自适应模糊逻辑系统结合起来,提出了一种跟踪控制方案.首先,应用模糊T-S模型对非线性系统建模,设计观测器用来观测系统状态:其次,应用基于权值、中心和宽度3个参数可调节的自适应时延模糊逻辑系统补偿器来消除建模误差和小确定性.文中证明了闭环系统满足期望的跟踪性能.示例仿真结果表明了该方案的有效性.     

Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities

This paper presents an indirect approach to interval type-2 fuzzy logic system modeling to forecaste the level of air pollutants. The type-2 fuzzy logic system permits us to model the uncertainties among rules and the parameters related to data analysis. In this paper, we propose an indirect method to create an interval type-2 fuzzy logic system from a historical data, where Footprint of Uncertainties of fuzzy sets are extracted by implementation of an interval type-2 FCM algorithm and based on an upper and lower value for the level of fuzziness m in FCM. Finally, the proposed model is applied for prediction of carbon monoxide concentration in Tehran air pollution. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to type-1 fuzzy logic systems in terms of two performance indices.     

一类多变量非线性动态系统的鲁棒自适应模糊控制

对一类非线性多变量未知动态系统,提出了一种自适应模糊控制策略.策略中采用 IF-THEN推理规则来构造模糊逻辑系统,实现对系统中未知函数的估计,在建模误差为零的 条件下设计状态反馈控制器及参数的自适应律.分析了当存在建模误差时,闭环系统的稳定 性和鲁棒性.     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.     

Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.     

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．     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

Chattering-free fuzzy variable structure control for multivariable nonlinear systems

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) with guaranteed stability for multivariable systems is presented. It is aimed at obtaining an improved performance of nonlinear multivariable systems. The main contribution of this work is firstly developing a generic matrix formulation of the FLC-VSC algorithm for nonlinear multivariable systems, with a special attention to non-zero final state. Secondly, ensuring the global stability of the controlled system. The multivariable nonlinear system is represented by T-S fuzzy model. The identification of the T-S model parameters has been improved using the well known weighting parameters approach to optimize local and global approximation and modeling capability of T-S fuzzy model. The main problem encountered is that T-S identification method cannot be applied when the membership functions (MFs) are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. In order to overcome the chattering problem a switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules together with the state variables. A two-link robot system and a mixing thermal system are chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of proposed FLC-VSC method.     

Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms

We describe in this paper a comparative study between fuzzy inference systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms with the goal of having optimized versions of both types of fuzzy systems. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy systems of integration. The comparative study of the type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.     

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.     

Granular type-2 membership functions: A new approach to formation of footprint of uncertainty in type-2 fuzzy sets

In this study, a new approach for the formation of type-2 membership functions is introduced. The footprint of uncertainty is formed by using rectangular type-2 fuzzy granules and the resulting membership function is named as granular type-2 membership function. This new approach provides more degrees of freedom and design flexibility in type-2 fuzzy logic systems. Uncertainties on the grades of membership functions can be represented independently for any region in the universe of discourse and free of any functional form. So, the designer could produce nonlinear, discontinuous or hybrid membership functions in granular formation and therefore could model any desired discontinuity and nonlinearity. The effectiveness of the proposed granular type-2 membership functions is firstly demonstrated by simulations done on noise corrupted Mackey–Glass time series prediction. Secondly, flexible design feature of granular type-2 membership functions is illustrated by modeling a nonlinear system having dead zone with uncertain system parameters. The simulation results show that type-2 fuzzy logic systems formed by granular type-2 membership functions have more modeling capabilities than the systems using conventional type-2 membership functions and they are more robust to system parameter changes and noisy inputs.     

Book Review [review of Fuzzy Modeling and Fuzzy Control (Zhang, H. and Liu)]

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.     

基于模糊逼近的一类不确定非线性系统的容错控制

针对一类不确定非线性系统,提出了一种模糊容错控制方案.采用模糊T-S模型来逼近非线性系统,由线性矩阵不等式设计模糊模型的控制律.构建了模糊逻辑系统作为补偿器来抵消对非线性系统的建模误差和因故障引起的不确定性,并证明了闭环系统能够满足期望的跟踪性能.仿真实例表明了所提出容错控制方案的有效性.     

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.     

不需持续激励条件的非线性离散时间系统的自适应模糊逻辑控制

针对一类非线性离散时间系统，根据模糊逻辑系统的逼近性质，给出了一种自适应模糊逻辑控制器的设计方法。利用李亚普诺夫稳定性理论，证明了控制算法是全局稳定的，跟踪误差收敛于零的某一领域中。该设计方法克服了要求模糊基函数向量满足持续激励（PE）条件这一难以验证和满足的假设条件。     

Fuzzy Non-Homogeneous Markov Systems

In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory of Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.     

Three-dimensional fuzzy logic system for process modeling and control

The traditional fuzzy logic system (FLS) can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.     

基于自适应模糊逻辑系统的一类混沌系统同步控制

针对一类带有未知函数和干扰的混沌系统,进行了基于自适应模糊逻辑系统的自适应同步控制器的设计。首先基于模糊逼近原理,通过对该混沌系统中未知函数的输入输出进行采样,根据采样数据信息设计出具有参数自适应功能的Mamdani型模糊逻辑系统;然后利用该模糊逻辑系统给出一种带有参数自适应的驱动响应同步控制器设计方法;最后通过数值仿真算例表明了该方法的有效性。     

On the applicability of the minimal configured hierarchical fuzzy control and its relevance to function approximation

This paper presents an approach to minimal hierarchical fuzzy control systems design. It maintains the idea of distributing behaviors inside a fuzzy structure, while the minimal hierarchical fuzzy control is an effective procedure for dealing with systems complexity and their control. The MCHFLS, minimal configured hierarchical fuzzy logic systems, is minimizing the fuzzy inferences parameters by adjusting essentially the variables associations and levels structures. This approach performs function approximation under conditions and it was tested on textile mill control.     

一类欠驱动机械系统的模糊与变结构控制

针对体操机器人这类欠驱动机械系统,提出一种模糊与变结构控制策略.首先用逻辑 模糊控制实现快速平滑的摇起;然后用模糊变结构控制确保从摇起区快速进入平衡区;最后用 基于Takagi-Sugeno模糊模型的模糊控制达到较大范围内的平衡控制.