浮选生产过程专家模糊控制

介绍了由专家系统和模糊控制器相结合起来实现复杂工业过程的仿人智能控制，该系统已在老虎台选煤厂浮选生产过程控制中得到应用，取得了较好经济效益     

基于T-S模型的网络控制系统故障诊断

针对一类参数不确定并具有时延和丢包情况的非线性网络控制系统,为达到快速准确的进行故障诊断的目的,提出了一种基于T-S模糊模型的故障诊断方法.通过建立此系统的T-S模糊模型,利用平行分布补偿时延的思想设计了满足系统稳定性条件的状态反馈控制器,以及通过引入随机切换系统表示数据有无丢失情况下的基于模糊观测器的鲁棒故障诊断方法,然后基于 Lyapunov函数和线性矩阵不等式方法给出了该闭环网络控制系统渐近稳定的充分条件,最后通过仿真例子验证了该方法能够使闭环控制系统渐进稳定以及能够准确的进行故障诊断,验证了所设计方法的有效性.     

H∞Fuzzy State‐Feedback Control Plus State‐Derivative‐Feedback Control Synthesis for Photovoltaic Systems

This paper addresses the problem of designing an H_∞fuzzy state‐ feedback (SF) plus state‐derivative‐feedback (SDF) control system for photovoltaic (PV) systems based on a linear matrix inequality (LMI) approach. The TS fuzzy controller is designed on the basis of the Takagi‐Sugeno (TS) fuzzy model. The sufficient condition is found such that the system with the fuzzy controller is asymptotically stable and an H_∞performance is satisfied. First, a dc/dc buck converter is considered to regulate the power output by controlling state and state‐derivative variables of PV systems. The dynamic model of PV systems is approximated by the TS fuzzy model in the form of nonlinear systems. Then, based on a well‐known Lyapunov functional approach, the synthetic is formulated of an H_∞fuzzy SF plus SDF control law, which guarantees the L₂‐gain from an exogenous input to the regulated output to be less than or equal to some prescribed value. Finally, to show effectiveness, the simulation of the PV systems with the proposed control is assessed by the computer programme. The proposed control method shows good performance for power output and high stability for the PV system.     

Static Output Feedback Control for Interval Type‐2 T‐S Fuzzy Systems Based on Fuzzy Lyapunov Functions

This study aims to design an interval type‐2 (IT2) fuzzy static output feedback controller to stabilize the IT2 Takagi‐Sugeno (T‐S) fuzzy system. Conservative results may be obtained when a common quadratic Lyapunov function is utilized to investigate the stability of T‐S fuzzy systems. A fuzzy Lyapunov function is employed in this study to analyze the stability of the IT2 fuzzy closed‐loop system formed by the IT2 T‐S fuzzy model and the IT2 fuzzy static output feedback controller. Stability conditions in the form of linear matrix inequalities are derived. Several slack matrices are introduced to further reduce the conservativeness of stability analysis. The membership‐function shape‐dependent analysis approach is also employed to relax the stability results. The numerical examples illustrate the effectiveness of the proposed conditions.     

Adaptive Composite Fuzzy Dynamic Surface Control for Electro‐Hydraulic‐System,with Variable‐Supply‐Pressure

This paper presents a novel adaptive composite fuzzy dynamic surface controller for a variable‐supply‐pressure electro‐hydraulic‐system in the presence of unknown nonlinear friction effects. To avoid analytic calculation, command filters are utilized to produce certain virtual controllers and their derivatives. A fuzzy logic system is designed to approximate and compensate the unknown nonlinear friction influences of the electro‐hydraulic‐system. To achieve a precise approximation, the prediction error of a designed serial‐parallel estimation model and the compensated tracking error are both used to develop the composite adaptive law. Comparative simulation and experimental results are obtained to verify the effectiveness of the proposed control method.     

基于模糊聚类算法的工控网络控制故障检测方法研究

针对传统大型工控网络控制故障检测过程中,没有考虑故障延时特性,从而导致的故障信号检测准确率下降,检测效率降低;为此,提出一种基于模糊算法的大型工控网络控制故障检测方法;引入模糊算法,对大型工控网络控制中的故障信号延迟进行模糊化建模,通过随机时延切换设计故障观测参数和故障观测参数的残差对大型工控网络控制系统进行故障检测,克服信号延迟弊端;实验结果表明,利用文章方法进行大型工控网络控制系统故障检测,能够有效提高故障的准确率,效果令人满意。     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

Neuro‐filters used to enhance the image quality of video displays

Abstract— Digital images can be affected by external factors. There are many types of noise which affect digital images. Image filtration is a basic method used to suppress such hindrances. The disadvantage of most filtration methods and hardware filters created on their behalf is their inability to react to changes in the input signal. The structure of the filters used for image processing is similar to the structure of a bi‐dimensional neural‐network matrix. Investigations have shown that a system with serial‐parallel filters of any degree of complexity can be created on the basis of the neural‐network matrices. Each neural‐network matrix layer acts as a separate neuro‐filter which can be trained and adapted to changes in the characteristics of the images. The neural‐network matrices allow for the creation of various types of linear and nonlinear filters, as well as combinations on the basis of a uniform structure. It allows for the design of a universal hardware neuro‐filter structure that can perform as different types of filters by means of loading the connectors weight. In our paper, we consider the realization of neuro‐filters based on a neural‐network matrix, which allows the processing of both static and moving images and increases the image sharpness, suppresses the noise, and detects movable objects in the processed image.     

Simultaneous Design of Parallel Distributed Output Feedback and Anti‐windup Compensators for Constrained Takagi‐Sugeno Fuzzy Systems

This paper is devoted to developing a novel approach to deal with constrained continuous‐time nonlinear systems in the form of Takagi‐Sugeno fuzzy models. Here, the disturbed systems are subject to both input and state constraints. The one‐step design method is used to simultaneously synthesize the dynamic output feedback controller and its anti‐windup strategy. A parameter‐dependent version of the generalized sector condition is used together with Lyapunov stability theory to derive linear matrix inequality design conditions. Based on this result and for different design specifications, the synthesis of an anti‐windup based dynamic output feedback controller is expressed on the form of convex optimization problems. A physically motivated example is given to illustrate the effectiveness of the proposed method.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.     

交叉路口交通灯实时模糊控制系统设计与实现

提出了一种基于模糊控制的交叉路口交通灯控制系统.该模糊控制系统以单交叉路车长、车长之差为输入,以绿灯延时为输出.并简单介绍了基于单片机的智能交通控制系统的实现.验证计算结果表明,所提出的模糊控制算法能有效地减少交叉口平均车辆延误,为智能交通系统实现提供了一条新途径.     

智能车辆路径跟踪的模糊预瞄控制方法研究

论文首先介绍了智能车辆的基本硬件组成,然后结合智能车辆路径跟过程中转向控制方面的特点,提出了一种将模糊与预瞄控制理论相结合的控制策略,介绍了模糊预瞄控制器的原理、结构及其设计过程.试验表明,论文提出的控制策略可以较好地跟踪既定轨迹,并且在道路弯曲度较大时能够实时调节预瞄点个数,从而提高车辆对弯道路径跟踪的预测性、智能性和鲁棒性.     

专家模糊控制在球磨机上的应用

中储式球磨机制粉系统是一个多变量、强耦合的对象,数学模型难以建立。本针对各被控过程的具体要求,分别提出简单、实用的不同控制策略。对温度和负压采用专家模糊控制,既能保证系统的安全性,又能提高经济性。     

Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.     

Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model

For the non‐Gaussian stochastic distribution control system using Takagi‐Sugeno fuzzy model, a new fault diagnosis and sliding mode fault tolerant control algorithm is presented. First, a new adaptive fault diagnosis algorithm is adopted to diagnose the fault that occurred in the system, and the observation error system is proven to be uniformly bounded. Second, the sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post‐fault probability density function can still track the given distribution, leading to fault tolerant control of non‐Gaussian stochastic distribution control systems using Takagi‐Sugeno fuzzy model. Finally, simulation results show the effectiveness of the proposed method.     

基于模糊控制的嵌入式智能轮椅控制研究

设计了一种新的智能轮椅,该轮椅的控制器应用了嵌入式技术和模糊控制技术,运用了先进的传感设备,使之能够感知环境信息,具有实时避障功能.时智能轮椅的避障过程进行了仿真,得到比较好的控制效果,并证明用模糊控制策略实现实时避障功能的可行性.     

基于神经网络和模糊理论的智能控制技术

将神经网络方法和模糊理论结合起来，讨论一种基于神经网络（NN）和模糊理论实现智能控制的方法。仿真表明这类智能控制器可用于难以建立数学模型的控制系统。     

NON‐PDC Observer‐Based T‐S Fuzzy Tracking Controller Design and its Application in CHAOS Control

In many mechanical devices with chaotic behavior, stabilizing unstable periodic orbits (UPOs) of the system has positive effects in the lifetime and effectiveness of these devices. In this study, a new non‐parallel distributed compensation (non‐PDC) observer‐based tracking controller is presented for Takagi–Sugeno fuzzy systems to control the chaotic behavior of such systems. Asymptotic stability synthesis of the closed‐loop system is investigated using a fuzzy Lyapunov function to derive less conservative conditions than common quadratic Lyapunov function‐based approaches. To tackle the main drawback of the fuzzy Lyapunov‐based approaches, which assume some upper bounds on the derivatives of the fuzzy grade functions, we propose a new procedure by considering a constraint on the control signal. The new design conditions are given in the form of linear matrix inequalities (LMIs). The proposed control structure is applied to spinning disks in which chaos phenomena appear in lateral vibration. Simulation results are given to show the applicability of the proposed tracker to the UPO problem.     

Dynamic Output‐Feedback Control for T‐S Fuzzy Systems with Input Time‐Varying Delay

This paper considers the problem of the control for T‐S fuzzy systems with input time‐varying delay via dynamic output feedback. Firstly, by applying the reciprocally convex approach, new delay‐dependent sufficient condition for performance analysis is obtained. Then, a less conservative condition for the existence of the controllers is given in terms of linear matrix inequalities (LMIs). Moreover, in the considered system, the time‐delay term is included in the measured output. This results in the difficulty in designing the controllers being increased and the obtained results being applied to a wider class of fuzzy systems than the most existing ones. The main contribution of this work lies in the application of the reciprocally convex inequality and the time‐delay term included in the measured output. Finally, the advantages and effectiveness of the present results are shown by several numerical examples.     

Spacecraft Anti‐Unwinding Attitude Control Using Second‐Order Sliding Mode

This paper presents an anti‐unwinding control method for the attitude stabilization of a rigid spacecraft. Quaternion has double stable equilibrium and this may cause unwinding problems in spacecraft attitude control if both the equilibria are not considered in control design. Here, the initial condition of scalar quaternion is included in sliding surface and an anti‐unwinding control method is formulated in second‐order sliding mode. The presented second‐order sliding mode controller can alleviate chattering and ensure a smooth control for actuator. Further, to eliminate the need of advance information about bounds of uncertainty and external disturbance, adaptive laws are applied to estimate the controller gains. The closed‐loop stability is proved using the Lyapunov stability theory. In conclusion, a simulation is conducted in the presence of inertia uncertainty and external disturbances and it is found that the presented control method is efficient to negate the effect of inertia uncertainty and external disturbances, alleviate chattering, eliminate unwinding, and ensure high accuracy and steady state precision.