基于模糊控制的锡炉温度控制系统的设计

针对传统温度控制系统建模困难,控制性能差的缺点,本文介绍了一种以STC89C52单片机为核心,采用模糊控制算法的温度控制系统,并对模糊控制思想及系统的软、硬件设计进行了详细阐述。实践表明,模糊控制方法提高了控制的实时性、稳定性和精确度,并且实现了操作过程的简化。目前,该系统处于实际工业炉温控制的应用和试行阶段,取得了良好的效果。     

高阶模糊控制器在网络控制系统中的应用

当网络控制系统存在非线性随机延迟时，不能直接使用传统的控制方法。针对这种随机延迟该文提出了高阶模糊控制方法。该方法在经典PID控制器的理论基础上进行改进，增加了系统输出的高阶信息，同时利用模糊控制理论对控制器参数进行在线自适应调节。为此该文首先简要地介绍了网络控制系统中存在的随机延迟，然后分析了延迟引起的非线性特征。结合该文的算法思想和模糊控制器的设计原理给出模糊控制器的设计过程。最后给出了应用此方法的一个仿真实例。     

一类脉冲T—S模糊控制方法及其应用

本文利用模糊控制理论中的T-S模糊控制模型,将之和脉冲控制方法结合,提出了一种脉冲T-S模糊控制方法。给出了控制器的设计方法并进行控制系统稳定性能的讨论。这种控制方法能综合脉冲控制的简易性和模糊控制的非线性处理的能力．是一种有效的控制方法。     

活性污泥系统中溶解氧模糊控制器的研究

分析了活性污泥系统中溶解氧（DO）控制的重要性及复杂性后，提出用模糊控制的方法控制污水处理中的溶解氧，并设计了溶解氧模糊控制系统，为污水处理的自动控制提供了一种新的设计方法。     

基于遗传算法的发动机模糊控制

模糊控制不依赖被控对象的数学模型，模糊控制应用于航空发动机控制取得了较好的效果。但是，模糊控制设计，较多地依赖设计者的主观经验，工作量大、负担重。遗传算法具有良好的寻优能力，该文将遗传算法应用到了航空发动机模糊控制中，使大量的设计寻优工作，可以通过程序自动寻优，对遗传算法寻优的发动机模糊控制系统进行了仿真，结果表明遗传算法不但改进了发动机模糊控制器的设计，而且整个控制系统具有良好的性能。     

用PLC实现模糊控制的两种程序设计方法

本文依据PLC的控制特点，提出了两种用PLC实现模糊控制的程序设计方法，实验表明，这两种设计方法是可行的，基于这两种方法实现的控制系统可靠性高，智能程序明显优于传统的PLC控制系统，具有广泛的应用前景。     

预测模糊控制在聚合反应釜中的仿真研究

聚合反应釜温度控制系统的数学模型具有非线性、大惯性、纯滞后以及时变等特点，传统的PID控制依赖于被控对象的精确数学模型，很难达到令人满意的控制效果。模糊控制和预测控制都是对不确定系统进行控制的有效方法，而预测模糊控制作为二者相结合的产物，可进一步提高控制的效果。该文提出了将预测模糊控制运用于聚合反应釜温度控制器的设计，为聚合反应釜的控制提出了一种新算法。理论分析和基于MATLAB的仿真研究表明，该控制方法具有使系统超调量小、调整时间短、对系统参数变化和外界干扰有较强的鲁棒性等优点，是一种提高聚合反应釜温度控制效果的有效方法。     

结晶器液位模糊控制系统的优化设计与仿真

结晶器液位控制系统中存在时变、滞后等非线性特性,使得它的控制变得特别复杂。针对常规PID控制和模糊控制所存在的问题,提出了一种优化模糊控制方案以提高系统的控制性能。首先分析了结晶器液位控制系统的结构和原理,介绍了模糊控制器的设计过程,最后利用MATLAB软件进行了仿真实验。结果表明,优化模糊控制的引入有效地克服了系统的扰动,提高了系统的控制性能。     

锅炉汽包水位控制系统的研究

锅炉汽包水位是一种非线性、时变大、强耦合的多变量系统。在建直了锅炉汽包水位调节对象的数学模型的基础上，利用模糊控制理论设计锅炉水位控制系统，运用模糊控制与PID控制分别对汽包水位进行控制。介绍了控制器的设计和仿真分析，并应用MATLAB软件对汽包水位控制系统进行验证和仿真，给出了模糊控制方法与传统方法的比较结果，表明了模糊控制明显地改善了汽包水位控制系统的静、动态特性，从而实现了对锅炉汽包水位的最件实时控制。     

基于模糊推理的热轧带钢宽度控制

带钢热连轧宽度控制过程是一个复杂的非线性过程、难以建立精确的数学模型，对于传统的数学模型的轧机计算很难实现精度要求。设计一种用于宽度控制的模糊控制器。模糊控制器根据推理规则获得控制系统的查询表。并用Matlab语言的Simulink仿真工具，进行了常规控制与模糊控制的动态性能的仿真比较，结果表明模糊控制可明显提高宽度控制系统的动态性能。     

SOME APPLICATIOINAL ASPECTS OF FUZZY RELATIONAL EQUATIONS IN SYSTEM ANALYSIS

The paper deals with the notion of fuzzy systems described by means or fuzzy relational equations with triangular norms. Some fundamental pro Menu concerned with identification and control arc formulated and several algorithms are provided. An applicability of a concept of fuzzy discretization in system analysis is pointed out. Numerical results obtained form an illustration of theoretical background considered.     

模糊控制在张力辊双闭环系统中的应用

随着卷取机自动化技术的发展,描述卷取机运动的重要参数--速度控制显得越加重要.本文主要研究模糊控制理论在张力辊速度控制中的应用.介绍了两种模糊控制器的设计,并利用MATLAB(SIMULINK)进行了计算机仿真.仿真结果表明,该模糊控制系统具有较好的控制性能,对进一步应用研究具有较大的参考价值.     

Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

This paper proposes another adaptive control scheme for nonlinear systems using a Takagi-Sugeno fuzzy model. Takagi-Sugeno fuzzy models have been widely used to identify the structures and parameters of unknown or partially known plants, and to control nonlinear systems. This scheme shows a good approximation capability by the fuzzy blending of local dynamics. Since a Takagi-Sugeno fuzzy model is a nonlinear system in nature, and its parameters are not linearly parameterized, it is difficult to design an adaptive controller using conventional design methods for adaptive controllers which are derived from linearly parameterized systems. In this paper, the functional form of the local dynamics are assumed to be known, but the corresponding parameters are unknown. This additional information about system nonlinearity makes it possible to design an adaptive controller for a nonlinearly parameterized system. The control law is similar to that of a conventional adaptive control technique, while its parameter-update rule is based on the local search method. A parameter-update law is derived so that the time-derivative of the Lyapunov function is negative in the region of interest. Simulation results have shown that this adaptive controller is capable of a good performance. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

陀螺仪温度的模糊控制系统

为了提高陀螺仪温度控制系统的动态性能和控制精度,对传统的PID算法存在的不足进行了分析,提出了基于C8051F120单片机的陀螺仪温度模糊控制系统的设计方法,包括系统的硬件设计、软件设计和模糊控制器设计的方法;并通过计算机仿真和现场调试,结果表明模糊控制器比PID控制器具有更高的稳态精度和更快的动态响应速度.在系统设计中,所采取的软、硬件设计措施及模糊控制器的设计方法具有一定通用性,也可以应用到其它设备的温度控制系统中.     

T-S模糊控制系统的稳定性分析及系统化设计

研究了输入采用双交叠模糊分划的模糊控制系统的性质,提出了一个新的判定T—S模 糊控制系统稳定的充分条件.该条件只需在各最大交叠规则组内分别寻找公共的正定矩阵，减 小了以往稳定性判定方法的局限性和难度.运用并行分布补偿法(PDC)进一步探讨了闭环T-S 模糊控制系统的稳定性分析和模糊控制器系统化设计方法.通过两个例子的仿真研究验证了本 文方法的有效性.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

New approaches to relaxed stabilization conditions and H-infinity control designs for T-S fuzzy systems

This paper focuses on the problem of fuzzy control for a class of continuous-time T-S fuzzy systems.New methods of stabilization design and H infinity control are derived based on a relaxed approach in...     

Optimal design for robust control of uncertain flexible joint manipulators: a fuzzy dynamical system approach

A novel fuzzy dynamical system approach to the control design of flexible joint manipulators with mismatched uncertainty is proposed. Uncertainties of the system are assumed to lie within prescribed fuzzy sets. The desired system performance includes a deterministic phase and a fuzzy phase. First, by creatively implanting a fictitious control, a robust control scheme is constructed to render the system uniformly bounded and uniformly ultimately bounded. Both the manipulator modelling and control scheme are deterministic and not IF-THEN heuristic rules-based. Next, a fuzzy-based performance index is proposed. An optimal design problem for a control design parameter is formulated as a constrained optimisation problem. The global solution to this problem can be obtained from solving two quartic equations. The fuzzy dynamical system approach is systematic and is able to assure the deterministic performance as well as to minimise the fuzzy performance index.