Minimal fuzzy memberships and rules using hierarchical geneticalgorithms

A new scheme to obtain optimal fuzzy subsets and rules is proposed. The method is derived from the use of genetic algorithms, where the genes of the chromosome are classified into two different types. These genes can be arranged in a hierarchical form, where one type of gene controls the other. The effectiveness of this genetic formulation enables the fuzzy subsets and rules to be optimally reduced and, yet, the system performance is well maintained. In this paper, the details of formulation of the genetic structure are given. The required procedures for coding the fuzzy membership function and rules into the chromosome are also described. To justify this approach to fuzzy logic design, the proposed scheme is applied to control a constant water pressure pumping system. The obtained results, as well as the associated final fuzzy subsets, are included in this paper. Because of its simplicity, the method could lead to a potentially low-cost fuzzy logic implementation     

Synergistic modeling and applications of hierarchical fuzzy neuralnetworks

Many common foundations exist between neural networks and fuzzy inference systems in terms of their mathematical models and system structures. This paper explores such a rich synergy and uses it to form the basis for a unifying framework under which fuzzy logic processing and neural networks may be integrated to achieve more robust information processing. It in turn leads to a family of hierarchical fuzzy neural networks (FNNs) which incorporate an adaptive and modular design of neural networks into the basic fuzzy logic systems. Several important models which are critical to the development of the the hierarchical FNN family are studied. We demonstrate how existing unsupervised and supervised learning strategies can be an integral part of a fuzzy processing framework. In addition, hierarchical structures involving both expert modules and class modules are incorporated into the FNNs. Also presented are some promising application examples     

异构无线网络中基于模糊逻辑的分级垂直切换算法

在异构无线网络中,针对综合考虑网络端和用户端参数的垂直切换算法,参数权重难以确定,同时基于模糊逻辑的垂直切换算法存在复杂度高的问题,该文提出一种基于模糊逻辑的分级垂直切换算法。首先,将接收信号强度(RSS)、带宽、时延输入到1级模糊逻辑系统,结合规则自适应匹配,推理出QoS模糊值,并通过QoS模糊值对网络进行初步筛选得到候选网络集;然后通过触发机制触发2级模糊逻辑系统,并将候选网络的QoS模糊值、网络负载率、用户接入费用输入2级模糊逻辑系统,同时结合规则自适应匹配,得到输出判决值,从而选择最佳接入网络。最后,实验结果表明,该算法能保证网络性能的同时,降低系统的时间开销。     

Fuzzy logic approach to VLSI placement

A contemporary definition of VLSI placement problem is characterized by multiple objectives. These objectives are: timing, chip area, interconnection length and possibly others. In this paper, fuzzy logic has been used to facilitate multiobjective decision-making in placement for standard cell design style. A placement process has been defined in terms of linguistic variables, linguistic values and membership functions. Various objectives have been related by hierarchical fuzzy logic rules implemented as object-oriented programming objects. It is demonstrated that a designed fuzzy logic system is flexible in selecting goals and considering tradeoffs. Details of implementation, experimental results and comparisons with other systems are provided     

基于模糊控制的电动汽车再生制动系统的研究

再生制动能够降低能源的消耗量和延长电动汽车的行驶里程。它广泛的受到诸多学者的关注。本文提出了一个新颖的再生制动控制系统。该系统基于无刷直流电机的控制特性和电动汽车刹车时的制动特性,直流无刷电机采用传统的PID控制,刹车力采用模糊逻辑控制,刹车力矩可以由PID控制器实时的控制。通过Matlab/Simulink软件,仿真分析了电池的充电状态、制动力和直流侧线电流。实验和仿真结果均证实了在具有良好的刹车性能的前提下,该方法可以实现良好的再生制动性能和延长电动汽车的行驶里程,在工程上更加易于实现也具有更好的鲁棒性和更高的效率。     

Fuzzy behavior integration and action fusion for robotic excavation

This paper discusses control behavior integration and bucket action fusion for excavation control of a robotic front-end-loader type machine. To utilize the experience and expertise from skilled human operators, a fuzzy-logic based control approach is developed. A hierarchical excavation control architecture decomposes excavation goals to tasks, then tasks to behaviors, and finally behaviors to actions. The excavation actions are primitive and can be executed directly by an excavation machine. Finite state machines are used to specify the coordination and integration of behaviors for task execution and actions for behavior implementation. A simple strategy for action fusion is proposed based on fuzzy logic reasoning and the COA defuzzification method. Finally, laboratory experiments are conducted using a PUMA 560 robot arm and a Zebra force/torque sensor in a simulated rock excavation environment. Experimental results indicate that the proposed approach in this paper has led to more efficient task execution than previous approaches     

Hardware solutions for fuzzy control

A large fraction of software designs using microcontrollers is today adopting fuzzy logic algorithms and this fraction is likely to increase in the future. Hardware implementation of fuzzy logic ranges from standard microprocessors to dedicated ASICs and each different approach is targeted to a different application domain or market area. In this paper, we present an overview of the computational complexity of the fuzzy inference process and the various techniques adopted for fuzzy control tasks, highlighting the tradeoffs that can guide a system designer toward correct choices according to application features and cost/performance issues. In addition, we detail three case studies of architectures that address three different market segments in the fuzzy hardware scenario: dedicated fuzzy coprocessors, RISC processors with specialized fuzzy support and application specific fuzzy ASICs     

A unified approach to position and force control by fuzzy logic

This paper proposes a unified control strategy of position and force. The authors' technique is based on impedance control with fuzzy logic and realizes the smooth shift from position control to force control and vice versa. At first, a robust impedance controller based on a disturbance observer is shown, and the method to unify the position and the force control through one controller is described. Next, an algorithm to estimate the dynamic characteristics of the environment is shown, and a force tracking control using the estimated parameters is proposed. Finally, the unified control algorithm of position and force based on fuzzy logic is established. The validity of this method is confirmed by several experimental results     

A novel fuzzy friction compensation approach to improve theperformance of a DC motor control system

The compensation of friction nonlinearities for servomotor control has received much attention due to undesirable and disturbing effects that the friction often has on conventional control systems. Compensation methods have generally involved selecting a friction model and then using model parameters to cancel the effects of the nonlinearity. In this paper, a method using fuzzy logic for the compensation of nonlinear friction is developed for the control of a DC motor. The method is unique in that a single fuzzy rule is used to compensate directly for the nonlinearity of the physical system. As a result, the method introduces fewer adjustable parameters than a typical fuzzy logic approach while still incorporating many advantages of using fuzzy logic such as the incorporation of heuristic knowledge, ease of implementation and the lack of a need for an accurate mathematical model. The general approach, analysis and experimental results obtained for an actual DC motor system with nonlinear friction characteristics are presented and the effectiveness of the fuzzy friction compensation control technique is discussed. The smoothness of response, response times and disturbance rejection of a PI control system with and without the proposed fuzzy compensator are analyzed and discussed to illustrate the effectiveness of the proposed method     

模糊控制和模糊控制芯片

本文对模糊逻辑及模糊控制的基本概念、发展过程、应用领域进行了论述，探讨了模糊算法各个部分（模糊化、规则的选取与调整、各种算子、去模糊等）的功能和作用，介绍了目前受到广泛关注的模糊逻辑与人工神经元网络及专家系统相结合的一些方法，最后讨论了各种模糊控制的实现方法以及各种模糊控制芯片，特别是电流型多值模糊控制芯片的发展．     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

A Hybrid Neuro-Fuzzy System for Adaptive Vehicle Separation Control

The primary purpose of this paper is to develop a robust adaptive vehicle separation control in the increasingly important roles of intelligent transportation system (ITS). A hybrid neuro-fuzzy system (HNFS) is proposed for developing the adaptive vehicle separation control to minimize the distance (headway) between successive cars. This hybrid system consists of two modules: a headway identification (prediction) module and a control decision module. Each of these modules is realized with a distinct neuro-fuzzy network that upgrades hierarchical granularity and reduces the complexity in the control system. Given the current headway and relative velocity between the two consecutive cars, the headway identification module predicts the headway of the next time instant. This identified headway, together with the desired velocity are input to the control decision module whose output is the actual acceleration/deceleration control output. The HNFS encapsulates the adaptive learning capabilities of a neural network into a fuzzy logic control framework to fine-tune the fuzzy control rules. On the other hand, rules derived initially from well-defined fuzzy phase plane accelerate the training of the neural network. Simulation results are very encouraging. It is observed that the headway decreases significantly without sacrificing speed. Furthermore, both stability and robustness of HNFS are demonstrated.     

基于广义T-S模糊辨识模型的混沌系统模糊控制

该文针对混沌系统辨识引入广义T-S模糊模型,并对T-S模糊模型自适应参数进行遗传退火算法优化,使系统具有最佳结构和参数。在此基础上给出了广义T-S模糊模型使系统渐近稳定模糊控制算法,并证明了广义T-S模型有足够的精度, 控制的精度就能得到满足,系统可以跟踪目标。控制的目标可以为周期轨道,也可以为连续变化的目标函数。以一维的Logistic 系统和二维的Henon系统为例进行仿真分析,结果表明该方法的有效性和可行性。     

基于模糊感觉强度的无规则模糊逻辑系统的仿真设计

基于模糊逻辑方法在心理语言学研究领域的有效性,该文提出基于模糊感知强度和韦伯定律的一类新的无规则模糊逻辑系统及其自适应控制应用的方法。首先,应用心理物理学中的概念,模糊逻辑系统的知识库是利用模糊感觉强度来描述专家经验感受;模糊推理后应用广义韦伯定律进行解模糊得到系统输出。然后,针对一类非线性系统,利用构造的新的无规则模糊逻辑系统进行自适应控制设计,得到相应的控制器和参数自适应律。最后,通过Duffing混沌系统仿真算例验证了该文方法的可行性和有效性。     

Hierarchical rule-based and self-organizing fuzzy logic control fordepth of anaesthesia

A multi-stage hierarchical fuzzy control system with a multi-echelon structure for depth of anaesthesia (DOA) is described in this paper. There are four echelons that monitor, control, interpret and assess the whole surgical operation. Echelon 1 is a measurement and control action level that involves: instrument sensing (Dinamap); anaesthetist observations [measurement of sweating (SW), lacrimation (LA) and pupil response (PR)]; and a syringe pump (Graseby pump). Echelon 2 is an interpretation level that involves: interpreting systolic arterial pressure and heart rate to provide the primary DOA; interpreting SW, LA and PR to provide the degree of lightness; and interpreting bolus drug effects to estimate the sensitivity of patients. Echelon 3 is a regulation level that involves: controlling the drug from either a hand-crafted anaesthetists' rule-base or a self-organizing fuzzy logic controller algorithm; planning the drug profile to avoid long recovery; and managing alarm situations. Finally, echelon 4 is an assessment level that assesses the whole surgical procedure according to the patient recovery time. Testing this system in clinical trials as an intelligent adviser has provided a preliminary proof-of-concept of the applicability of this hierarchical structure for DOA management     

Self-learning fuzzy logic system for in situ, in-processdiagnostics of mass flow controller (MFC)

An improvement in the yield of better quality wafers requires an accurate control of various process variables. The control should include timely diagnosis and appropriate in-situ, in-process adjustments for drifts in these variables. One such scheme, a self-learning fuzzy logic system, is developed in this study for correcting drifts in the calibration of mass flow controllers (MFC's) that control the flow of gases into a process chamber. It consists of two components; a diagnostic system and a self-learning system. The diagnostic system uses fuzzy logic to diagnose the problem and initiate suitable remedial action, The self-learning system automatically builds the knowledge base used for diagnosis. The knowledge base is initialized using clustering principle and is tuned for better performance using a set of heuristic rules. The system is capable of learning the behavior of different types of makes and models of MFC's under various flow rates. It has been tested on two different types of MFC's under different flow rates and encouraging results have been obtained     

模糊PID算法在血液蛋白自动检测仪中的应用

介绍了一种用于生物芯片血液蛋白自动检测仪的伺服控制系统,该系统使用机械臂控制系统为平台,以伺服电机为主要驱动装置。本文建立了该机械伺服控制系统模型,对模糊自整定PID控制算法进行了研究,并将其应用到系统中,在MATLAB软件中进行仿真,以实现系统对机械臂高精度定位的要求。仿真结果表明,机械臂模糊控制系统定位精度高,并且较常规PID具有更强适应性,发挥了传统控制与Fuzzy控制各自的长处,参数自适应模糊PID控制能使系统达到满意的控制效果。     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

Application of fuzzy logic to reliability engineering

The analysis of system reliability often requires the use of subjective-judgments, uncertain data, and approximate system models. By allowing imprecision and approximate analysis fuzzy logic provides an effective tool for characterizing system reliability in these circumstances; it does not force precision where it is not possible. Here we apply the main concepts of fuzzy logic, fuzzy arithmetic and linguistic variables to the analysis of system structures, fault trees, event trees, the reliability of degradable systems, and the assessment of system criticality based on the severity of a failure and its probability of occurrence