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张兴华 《计算机工程与应用》2004,40(34):22-25
提出了一类Takagi-Sugeno模糊控制器的自适应遗传优化设计方法。采用实数编码方式,并由自适应交叉和变异概率来控制遗传操作,有效地提高了参数优化的精度和算法的寻优效率。在优化过程中引入对称性参数约束条件,大大减小了算法的搜索空间。将该算法用于倒立摆T-S模糊控制器的设计,实现了控制器参数的快速自动整定。仿真结果表明,获得的T-S模糊控制器具有优良的性能。 相似文献
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针对一类带有未知外部扰动的不确定非线性系统,建立自适应模糊滑模控制器。基于Lyapunov稳定性理论,设计系统可调参数的自适应规则,控制器的设计过程中无需知道系统的具体模型及未知非线性函数的先验知识。数值仿真的结果也验证了该方法的有效性。 相似文献
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针对一类具有随机丢包的非线性网络控制系统,研究了系统的故障检测问题.基于T-S模糊模型将对象线性化,考虑了控制器和执行器之间、控制器和传感器之间的随机丢包现象,采用满足Bernoulli分布的二进制序列来描述数据传输的随机丢包.同时利用模糊主导子系统规则,设计了模糊观测器,给出了基于观测器闭环系统渐近稳定的充分条件,并通过数值仿真实验验证了该方法的有效性. 相似文献
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针对非工厂的温度、湿度耦合的烟叶初烤过程,提出了一种模糊控制与解耦规则结合的智能控制实用算法.根据烟叶初烤工艺要求和操作特点,为初烤阶段设计了可变因子模糊控制算法.引入解耦规则修正模糊控制器输出.试验表明,该智能控制保证了稳定性、快速性,提高了适应性. 相似文献
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In this article, we propose a new approach to the virus DNA–based evolutionary algorithm (VDNA‐EA) to implement self‐learning of a class of Takagi‐Sugeno (T‐S) fuzzy controllers. The fuzzy controllers use T‐S fuzzy rules with linear consequent, the generalized input fuzzy sets, Zadeh fuzzy logic and operators, and the generalized defuzzifier. The fuzzy controllers are proved to be nonlinear proportional‐integral (PI) controllers with variable gains. The fuzzy rules are discovered automatically and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the VDNA‐EA. The VDNA‐EA uses the VDNA encoding method that stemmed from the structure of the VDNA to encode the design parameters of the fuzzy controllers. We use the frameshift decoding method of the VDNA to decode the DNA chromosome into the design parameters of the fuzzy controllers. In addition, the gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the VDNA‐EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the VDNA‐EA to add and delete adaptively fuzzy rules. Our encoding method can significantly shorten the code length of the DNA chromosomes and improve the encoding efficiency. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. It is suitable for complex knowledge representation and is easy for the genetic operations at gene level to be introduced into the VDNA‐EA. We show how to implement the new method to self‐learn a T‐S fuzzy controller in the control of a nonlinear system. The fuzzy controller can be constructed automatically by the VDNA‐EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory. © 2003 Wiley Periodicals, Inc. 相似文献
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The DNA evolutionary algorithm(DNA-EA)and the DNA genetic algorithm(DNA-GA)based on a new DNA encoding method are propsed based on the structure and the genetic mechanism of biological DNA. The DNA-EA and the DNA-GA are applied into the optimal design of TS fuzzy control system. The simulation results show the effectiveness of the two DNA algorithms, excellent self-learning capability. However, the DNA-EA is superior to the DNA-GA in the simulation performance. 相似文献
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HAO YING 《Automatica》1998,34(12):1617-1623
In this paper, we first study analytical structure of general nonlinear Takagi-Sugeno (TS, for short) fuzzy controllers, then establish a condition for analytically determining asymptotic stability of the fuzzy control systems at the equilibrium point, and finally use the stability condition in design of the control systems that are at least locally stable. The general TS fuzzy controllers use arbitrary input fuzzy sets, any types of fuzzy logic AND, TS fuzzy rules with linear consequent and the generalized defuzzifier which contains the popular centroid defuzzifier as a special case. We have mathematically proved that the general TS fuzzy controllers are nonlinear controllers with variable gains continuously changing with controllers’ input variables. Using Lyapunov’s linearization method, we have established a necessary and sufficient condition for analytically determining local asymptotic stability of TS fuzzy control systems, each of which is made up of a fuzzy controller of the general class and a nonlinear plant. We show that the condition can be used in practice even when the plant model is not explicitly known. We have utilized the stability condition to design, with or without plant model, general TS fuzzy control systems that are at least locally stable. Three numerical examples are given to illustrate in detail how to use our new results. Our results offer four important practical advantages: (1) our stability condition, being a necessary and sufficient one, is the tightest possible stability condition, (2) the condition is simple and easy to use partially because it only needs the fuzzy controller structure around the equilibrium point, (3) the condition can be used for determining system local stability and designing fuzzy control systems that are stable at least around the equilibrium point even when the explicit plant models are unavailable, and (4) the condition covers a very broad range of nonlinear TS fuzzy control systems, for which a meaningful global stability condition seems impossible to establish. 相似文献
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Genetic reinforcement learning through symbiotic evolution forfuzzy controller design 总被引:1,自引:0,他引:1
Chia-Feng Juang Jiann-Yow Lin Chin-Teng Lin 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2000,30(2):290-302
An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems. 相似文献
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Hao Ying 《Fuzzy Systems, IEEE Transactions on》1998,6(4):582-587
Takagi-Sugeno (TS) fuzzy systems have been employed as fuzzy controllers and fuzzy models in successfully solving difficult control and modeling problems in practice. Virtually all the TS fuzzy systems use linear rule consequent. At present, there exist no results (qualitative or quantitative) to answer the fundamentally important question that is especially critical to TS fuzzy systems as fuzzy controllers and models, “Are TS fuzzy systems with linear rule consequent universal approximators?” If the answer is yes, then how can they be constructed to achieve prespecified approximation accuracy and what are the sufficient renditions on systems configuration? In this paper, we provide answers to these questions for a general class of single-input single-output (SISO) fuzzy systems that use any type of continuous input fuzzy sets, TS fuzzy rules with linear consequent and a generalized defuzzifier containing the widely used centroid defuzzifier as a special case. We first constructively prove that this general class of SISO TS fuzzy systems can uniformly approximate any polynomial arbitrarily well and then prove, by utilizing the Weierstrass approximation theorem, that the general TS fuzzy systems can uniformly approximate any continuous function with arbitrarily high precision. Furthermore, we have derived a formula as part of sufficient conditions for the fuzzy approximation that can compute the minimal upper bound on the number of input fuzzy sets and rules needed for any given continuous function and prespecified approximation error bound, An illustrative numerical example is provided 相似文献
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Hao Ying Author Vitae 《Automatica》2003,39(7):1171-1184
Deriving the analytical structure of fuzzy controllers is very important as it creates a solid foundation for better understanding, insightful analysis, and more effective design of fuzzy control systems. We previously developed a technique for deriving the analytical structure of the fuzzy controllers that use Zadeh fuzzy AND operator and the symmetric, identical trapezoidal or triangular input fuzzy sets. Many fuzzy controllers use arbitrary trapezoidal/triangular input fuzzy sets that are asymmetric. At present, there exists no technique capable of deriving the analytical structure of these fuzzy controllers. Extending our original technique, we now present a novel method that can accomplish rigorously the structure derivation for any fuzzy controller, Mamdani type or TS type, that employs the arbitrary trapezoidal input fuzzy sets and Zadeh fuzzy AND operator. The new technique contains our original technique as a special case. Given the importance of PID control, we focus on Mamdani fuzzy PI and PD controllers in this paper and show in detail how to use the new technique for different configurations of the fuzzy PI/PD controllers. The controllers use two arbitrary trapezoidal fuzzy sets for each input variable, four arbitrary singleton output fuzzy sets, four fuzzy rules, Zadeh fuzzy AND operator, and the centroid defuzzifier. This configuration is more general and complicated than the Mamdani fuzzy PI/PD controllers in the current literature. It actually contains them as special cases. We call this configuration the generalized fuzzy PI/PD controller. 相似文献
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Y. Ding H. Ying S. Shao 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1999,2(4):183-190
In this paper, we first reveal the analytical structure of a simple Takagi–Sugeno (TS) fuzzy PI controller relative to the
linear PI controller. The fuzzy controller consists of two linear input fuzzy sets, four TS fuzzy rules with linear consequent,
Zadeh fuzzy logic AND and the centroid defuzzifier. We prove that the fuzzy controller is actually a nonlinear PI controller
with the gains changing with process output. Utilizing the well-known small Gain Theorem in control theory, we then derive
sufficient conditions for global stability of the fuzzy control systems involving the TS fuzzy PI controller. Finally, as
an application demonstration, we apply the fuzzy PI controller to control issue temperature, in computer simulation, during
hyperthermia therapy. The relationship between heat energy and tissue temperature is represented by a linear time-varying
model with a time delay. The sufficient conditions for global stability are used to design a stable fuzzy control system.
Our simulation results show that the fuzzy PI control system achieves satisfactory temperature control performance. The control
system is robust and stable even when the model parameters are changed suddenly and significantly. 相似文献
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Hao Ying 《IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans : a publication of the IEEE Systems, Man, and Cybernetics Society》1998,28(4):515-520
We have constructively proved a general class of multi-input single-output Takagi-Sugeno (TS) fuzzy systems to be universal approximators. The systems use any types of continuous fuzzy sets, fuzzy logic AND, fuzzy rules with linear rule consequent and the generalized defuzzifier. We first prove that the TS fuzzy systems can uniformly approximate any multivariate polynomial arbitrarily well, and then prove they can also uniformly approximate any multivariate continuous function arbitrarily well. We have derived a formula for computing the minimal upper bounds on the number of fuzzy sets and fuzzy rules necessary to achieve the prespecified approximation accuracy for any given bivariate function. A numerical example is furnished. Our results provide a solid-theoretical basis for fuzzy system applications, particularly as fuzzy controllers and models 相似文献
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Hao Ying 《Fuzzy Systems, IEEE Transactions on》1998,6(2):226-234
We investigated the analytical structure of the Takagi-Sugeno (TS) type of fuzzy controllers, which was unavailable in the literature. The TS fuzzy controllers we studied employ a new and simplified TS control rule scheme in which all the rule consequent use a common function and are proportional to one another, greatly reducing the number of parameters needed in the rules. Other components of the fuzzy controllers are general: arbitrary input fuzzy sets, any type of fuzzy logic, and the generalized defuzzifier, which contains the popular centroid defuzzifier as a special case. We proved that all these TS fuzzy controllers were nonlinear variable gain controllers and characteristics of the gain variation were parametrized and governed by the rule proportionality. We conducted an in-depth analysis on a class of nonlinear variable gain proportional-derivative (PD) controllers. We present the results to show: (1) how to analyze the characteristics of the variable gains in the context of control; (2) why the nonlinear variable gain PD controllers can outperform their linear counterpart; and (3) how to generate various gain variation characteristics through the manipulation of the rule proportionality 相似文献
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Lanka Udawatta Keigo Watanabe Kiyotaka Izumi Kazuo Kiguchi 《Journal of Intelligent and Robotic Systems》2003,38(2):155-173
This paper introduces a new concept for designing a fuzzy logic based switching controller in order to control underactuated manipulators. The proposed controller employs elemental controllers, which are designed in advance. Parameters of both antecedent and consequent parts of a fuzzy indexer are optimized by using evolutionary computation, which is performed off-line. Design parameters of the fuzzy indexer are encoded into chromosomes, i.e., the shapes of the Gaussian membership functions and corresponding switching indices of the consequent part are evolved to minimize the angular position errors. Such parameters are trained for different initial configurations of the manipulator and the common rule base is extracted. Then, these trained fuzzy rules can be brought into the online operations of underactuated manipulators. 2-DOF underactuated manipulator is taken into consideration so as to illustrate the design procedure. Computer simulation results show that the new methodology is effective in designing controllers for underactuated robot manipulators. 相似文献