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.     

多变量非线性系统参数自调整的模糊加权控制

本文针对多变量非线性系统,提出了一种参数自调整的模糊加权信息融合方法．利用 模糊组合变量降低模糊控制系统的维数,根据不同的模糊组合变量对最后决策的作用大小, 赋予不同的权重来实现对多变量非线性系统的控制,在利用反向传播算法对量化系数和加权 系数进行自学习后,在线进行基于模糊规则的参数自调整,有效地解决了多变量模糊控制系 统中难于设计多维规则库和在线实现自适应模糊控制的问题．本文还对所提出的方法进行了 仿真实验和实际系统的实验,实验结果证明了该方法的有效性．     

INTELLIGENT MODELING AND CONTROL OF WASHING MACHINE USING LOCALLY LINEAR NEURO‐FUZZY (LLNF) MODELING AND MODIFIED BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC)

Intelligent control of home appliances has, in recent years, attracted much theoretical attention, as well as becoming a major factor for industrial and economic success and rapid market penetration. Washing Machines represent an important market. Intelligent control techniques are capable of providing useful means for both easier use and energy and water conservation. In this paper, the authors use two techniques that have successfully been used in other intelligent modeling and control applications. Firstly, the authors use a neuro‐fuzzy locally linear model tree system for data driven modeling of the machine. Secondly, the authors use a neural computing technique, based on a mathematical model of amygdala and the limbic system, for emotional control of the washing machine. The obtained results indicate the applicability of the proposed techniques in this important business sector.     

一类未知多变量非线性系统的动态神经网络自适应控制

对一类未知的非线性的多变量系统，提出了用动态神经网络实现直接自适应控制的策略，基于Ｌｙａｐｕｎｏｖ理论，获得一个稳定并且连续的学习律，避免了递归训练过程，闭环系统被证明是鲁棒稳定的，跟踪误差收敛到一个小的残集，这种方法的特点是即不需要离线学习阶段也不要求初始的参数误差足够小，仿真结果验证了提出的动态网络的自适应控制算法的有效性。     

A NEURO‐FUZZY SYSTEM DESIGN METHODOLOGY FOR VIBRATION CONTROL

Fuzzy system has been known to provide a framework for handling uncertainties and imprecision by taking linguistic information from human experts. However, difficulties arise in determining effectively the fuzzy system configuration, i.e., the number of rules, input and output membership functions. A neuro‐fuzzy system design methodology by combining neural network and fuzzy logic is developed in this paper to adaptively adjust the fuzzy membership functions and dynamically optimize the linguistic‐fuzzy rules. The structure of a five‐layer feedforward network is shown to determine systematically the correct fuzzy logic rules, tune optimally (in the sense of local region) the parameters of the membership functions, and perform accurately the fuzzy inference. It is shown both numerically and experimentally that engineering applications of the neuro‐fuzzy system to vibration control have been very successful.     

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations.     

竞争式Takagi-Sugeno模糊再励学习

针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络(CTSFRLN),该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法.文中相应提出了两种学习算法,即竞争式Takagi-Sugeno模糊Q-学习算法和竞争式Takagi-Sugeno模糊优胜学习算法,其把CTSFRLN训练成为一种所谓的Takagi-Sugeno模糊变结构控制器.以二级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法.     

Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

Two new and efficient approaches are presented to improve the local and global estimation of the Takagi‐Sugeno (T‐S) fuzzy model. The main aim is to obtain high function approximation accuracy and fast convergence. The main problem is that the T‐S identification method can not be applied when the membership functions are overlapped by pairs. The approaches developed here can be considered as generalized versions of T‐S method with optimized performance. The first uses the minimum norm approach to search for an exact optimum solution at the expense of increasing complexity and computational cost. The second is a simple and less computational method, based on weighting of parameters. Illustrative examples are chosen to evaluate the potential, simplicity and remarkable performance of the proposed methods and the high accuracy obtained in comparison with the original T‐S model. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

一种新型多变量模糊自适应控制系统的研究

针对多变量非线性系统,提出了一种基于动态耦合特性的两级串联结构的模糊自校正控制器,并提出了基于动态灵敏度矩阵和在线测量的自学习算法．同时利用一种智能梯度法确定自校正学习迭代步长．仿真结果表明,算法的收敛性和系统的稳定性均有所改善．利用这些控制策略,可以较好地解决化工反应器等复杂对象的过程控制问题     

MODEL‐REFERENCE OUTPUT‐FEEDBACK SLIDING MODE CONTROLLER FOR A CLASS OF MULTIVARIABLE NONLINEAR SYSTEMS

This paper proposes an output‐feedback sliding mode control design for a class of uncertain multivariable plants with nonlinear disturbances. The approach used here is based on the control parameterization employed in model‐reference adaptive control. The disturbances are allowed to be unmatched and to depend not only on the plant output but also on its unmeas‐urable state. A less restrictive condition on the uncertainty of the high frequency gain matrix is also obtained.     

A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.     

DECENTRALIZED STABILIZATION OF H FUZZY CONTROL FOR NONLINEAR MULTIPLE TIME‐DELAY INTERCONNECTED SYSTEMS

A robustness design of fuzzy control is proposed in this paper to overcome the effect of modeling errors between nonlinear multiple time‐delay systems and fuzzy models. In terms of Lyapunov's direct method, a stability criterion is derived to guarantee the UUB (uniformly ultimately bounded) stability of nonlinear multiple time‐delay interconnected systems with disturbances. Based on this criterion and the decentralized control scheme, a set of fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time‐delay interconnected systems and the Hcontrol performance is achieved in the mean time.     

FUZZY SUPERVISORY CONTROL OF A DSP‐BASED MAGNETIC LEVITATION SYSTEM

This paper proposes a fuzzy supervisory control of a magnetic levitation system which the eZdsp F2812 is utilized as a controller. The discrete‐time model of the magnetic levitation system is derived and the stability is guaranteed by the root locus methodology. A fuzzy supervisory control is used to compensate for system nonlinearities.     

一类大系统的直接自适应分散模糊控制

本文针对一类未知非线性大系统,提出了一种直接自适应模糊分散控制策略．设计中 ,首先在假设各子系统的动态已知的条件下,设计最优分散控制,然后用模糊自适应系统逼 近最优分散控制．同时引入模糊滑模控制消除各个子系统之间的相互作用,外部干扰和模糊 系统的逼近误差．并对所设计的控制系统进行了稳定性分析．     

MODEL-BASED FUZZY LOGIC CONTROL OF A NONLINEAR PLANT

A full-envelope controller for nonlinear systems is developed based on a novel model-based fuzzy logic control (MBFLC) approach. This approach envisages a bank of linear compensators, with each compensator optimized for a different operating point throughout the operational envelope. The fuzzy logic module properly blends the compensator outputs into a composite control signal, and avoids instability when none of the individual compensators are valid. In general practice, in the strongly nonlinear system under consideration, the fuzzy element of the MBFLC dominates due to the extremely small regions of validity of perturbation models of the strongly nonlinear plant. The MBFLC, with the composite control input smoothed by a linear compensator, provides full-envelope control of the benchmark plant. © 1997 by John Wiley & Sons, Ltd. This paper was produced under the auspices of the US Government and it is therefore not subject to copyright in the US.     

Application of two‐loop robust control to air‐conditioning systems

This paper presents the design and application of a two‐loop robust controller for temperature control in air‐conditioning systems. A Takagi‐Sugeno fuzzy model with uncertain local models is developed to describe the associated nonlinearities and uncertainties in the operation of the air handling units. Parallel distributed compensation is used to design the global control law. The local control law consists of two loops: an inner‐loop integral controller and an outer‐loop min‐max predictive controller with short prediction horizon. A discounting scheme is developed to weight the contribution of the two loops. Experimental results are presented which show that the proposed strategy can achieve acceptable control performance with a minimum of onsite tuning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

一类时变非线性系统的参数反馈模糊控制器分析与设计

本文针对工业控制中一类常见的非线性时变系统，提出了一种利用参数反馈在线修正 规则的模糊控制器；在全面介绍控制器结构与功能的同时，详细阐述了模糊规则修正器的原 理和设计方法，并采用基于功能评价的方法进行系统分析．在这个控制系统中，基本模糊控 制器简单实用；模糊规则修正器能充分利用对被控对象的分析成果；GA寻优算法能进一步简 化设计，提高性能．仿真结果表明，这种控制器的效果是令人满意的．     

A new evolving compact optimised Takagi–Sugeno fuzzy model and its application to nonlinear system identification

A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive clustering and non-dominated sorting genetic algorithm. The proposed method consists of two parts. The first part is related to the selection of most relevant or influencing inputs to the system and the second one is related to the tuning of fuzzy rules and parameters of the membership functions. The main purpose of the proposed scheme is to reduce the complexity and increase the accuracy of the model. In particular, three objectives are considered in the process of optimisation, namely, the number of inputs, number of rules and the root mean square of the modelling error. The performance of the developed method is validated by identifying the Box–Jenkins nonlinear benchmark system, and to the modelling of the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper. The latter is also a challenging problem due to the inherent hysteretic and highly nonlinear dynamics of the MR damper. It is shown that the developed evolving Takagi–Sugeno (T–S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose.     

Variable Structure Control with Chattering Reduction of a Generalized T‐S Model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non‐linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the 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. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi‐Sugeno (T‐S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T‐S identification method can not be applied when the membership functions 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. The approach developed here can be considered as a generalized version of the T‐S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T‐S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC‐VSC shows that both alleviation of chattering and robust performance are achieved.     

Performance‐oriented quasi‐LPV modeling of nonlinear systems

Polytopic quasi–linear parameter‐varying (quasi‐LPV) models of nonlinear processes allow the usage linear matrix inequalities (LMIs) to guarantee some performance goal on them (in most cases, locally, over a so‐called modeling region). In order to get a finite number of LMIs, nonlinearities are embedded on the convex hull of a finite set of linear models. However, for a given system, the quasi‐LPV representations are not unique, yielding different performance bounds depending on the model choice. To avoid such drawback, earlier literature on the topic used annihilator‐based approaches, which require gridding on the modeling region, and nonconvex BMI conditions for controller synthesis; optimal performance bounds are obtained, but with a huge computational burden. This paper proposes building a model by minimizing the projection of the nonlinearities onto directions, which are deleterious for performance. For a small modeling region, these directions are obtained from LMIs with the linearized model. Additionally, these directions will guide the selection of the polytopic embedding's vertices. The procedure allows gridding‐free LMI controller synthesis, as in standard LPV setups, with a very reduced performance loss with respect to the aforementioned BMI+gridding approaches, at a fraction of the computational cost.