Novel fuzzy adaptive finite-time nonsmooth control for uncertain nonlinear systems

In this article, a novel fuzzy adaptive finite-time nonsmooth controller is developed to handle the finite-time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite-time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite-time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.     

Selecting supplier combination based on fuzzy multicriteria analysis

Existing multicriteria analysis (MCA) methods are probably ineffective in selecting a supplier combination. Thus, an MCA-based fuzzy 0-1 programming method is introduced. The programming relates to a simple MCA matrix that is used to select a single supplier. By solving the programming, the most feasible combination of suppliers is selected. Importantly, this result differs from selecting suppliers one by one according to a single-selection order, which is used to rank sole suppliers in existing MCA methods. An example highlights such difference and illustrates the proposed method.     

Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection

In a deregulated multi-area electrical power system the objective is to determine the most economical generation dispatch strategy that could satisfy the area load demands, the tie-line limits and other operating constraints. Usually, economic dispatch (ED) deals only with the cost minimization, but minimization of emission content has also become an equally important concern due to the mandatory requirement of pollution reduction for environmental protection. Environmental economic dispatch (EED) is a complex multi-objective optimization (MOO) problem with conflicting goals. Normally a fuzzy ranking is employed to rank the large number of Pareto solutions obtained after solving a MOO problem. But in this paper the preference of the decision maker (DM) is used to guide the search and to select the population for the next generation. An improved differential evolution (DE) method is proposed where the selection operation is modified to reduce the complexity of multi-attribute decision making with the help of a fuzzy framework. Solutions are assigned a fuzzy rank on the basis of their level of satisfaction for different objectives before the population selection and then the fuzzy rank is used to select and pass on better solutions to the next generation. A well distributed Pareto-front is obtained which presents a large number of alternate trade-off solutions for the power system operator. A momentum operation is also included to prevent stagnation and to create Pareto diversity. Studies are carried out on three test cases and results obtained are found to be better than some previous literature.     

Comparison of fuzzy logic based models for the multi-response surface problems with replicated response measures

A replicated multi-response experiment is a process that includes more than one responses with replications. One of the main objectives in these experiments is to estimate the unknown relationship between responses and input variables simultaneously. In general, classical regression analysis is used for modeling of the responses. However, in most practical problems, the assumptions for regression analysis cannot be satisfied. In this case, alternative modeling methods such as fuzzy logic based modeling approaches can be used. In this study, fuzzy least squares regression (FLSR) and fuzzy clustering based modeling methods, which are switching fuzzy C-regression (SFCR) and Takagi–Sugeno (TS) fuzzy model, are preferred. The novelty of the study is presenting the applicability of SFCR to the multi-response experiment data set with replicated response measures. Three real data set examples are given for application purposes. In order to compare the prediction performance of modeling approaches, root mean square error (RMSE) criteria is used. It is seen from the results that the SFCR gives the better prediction performance among the other fuzzy modeling approaches for the replicated multi-response experimental data sets.     

基于三支决策的模糊信息系统OWA算子参数选择

在模糊信息系统中，通过有序加权平均（Ordered weighted averaging,OWA）算子计算对象相似度，可以建立λ截集的相容关系。当λ的值确定后，OWA算子中量词参数(α,β)的选取直接关系到不可区分关系的建立以及信息粒的粗细。因此合理选取参数(α,β)是值得进一步研究的问题。本文采用粗糙集三支决策理论，研究模糊信息系统 OWA算子参数选择的相关内容。基于参数的激进、中庸和消极语义，确定OWA算子(α,β)的3种常用取值；进而研究相似度、相容类、双向近似和三支区域在3种参数选择下的性质关系。最后利用实例验证分析了模糊量词参数语义解释的合理性。本文采用三支决策创新视角，得到模糊信息系统OWA算子的深入性质，为相关模糊量词参数提供语义解释与选择依据。     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Application and validation of a statistically derived risk-based sampling plan to improve efficiency of inspection and enforcement

The statistically derived risk-based sampling plan for surveillance sample assignments of chemical and biological hazards was designed using binomial probability distribution. The binomial statistics was applied to the past 3-year data to estimate a confidence interval and a sample size aiming to improve efficiency and effectiveness of the agency's sampling and inspectional activities. The accuracy of the statistical models and computed estimates were validated in the following years. The ranges of confidence interval and sample size appeared to be significantly influenced by a level of the violation rate of feed product samples, an acceptable error, a number of the analyzed samples, and a statistical significance level. The violation rates of feed products for target analytes (aflatoxins, fumonisins, Salmonella, and dioxin) in the validation data were lower than those of the average 3-year data in most feed products. Besides, the actual violation rates of the validation samples did not exactly fall within the anticipated range of the confidence interval estimates. Such a discrepancy is considered introduced by several factors such as sample size adequacy, skewed distribution of a target analyte in feed products, and unique analyte/product combination. The overall study results indicate that the risk-based plan of work would provide a more effective and efficient risk management tool to help improve the oversight of the feed industry and the compliance to feed safety standards.     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

基于LabVIEW的软管脉冲实验模糊PID控制系统研究(英文)

软管脉冲实验台一般使用电液伺服系统,占用空间小,原理结构简单,信号跟踪能力强,在工业控制领域应用广泛。然而,由于液压反应速度慢、干扰因素多,因而控制精度低,系统不稳定;而传统的液压脉冲实验台单纯PID的控制方式原理简单,但参数调节困难。本文针对控制系统的特殊要求,提出了一种模糊PID控制方法,既保留了原控制系统的优点,又改善了原系统存在的参数时变、不稳定、滞后等缺点,提高了系统的适应能力和精度。经反复调试和试验,结果表明,该系统有效地解决了系统不稳定、滞后严重和参数时变等问题。