关于焦炉火道温度模型辨识的仿真研究

建立对象的模型是控制系统设计的基础,非线性系统的建模是复杂系统建模的难点之一,焦炉火道温度复杂多变,其精准模型的建立事关重要。首先对焦炉加热生产过程采用基于减法聚类和C－均值聚类相结合的模糊T－S辨识算法来简化前提结构辨识,从而实现焦炉对象的模糊辨识。然后通过模糊神经网络结构来优化模型参数从而得到焦炉对象的局部模型,最后通过计算各局部模型的隶属度来得到焦炉对象的全局模型。仿真结果表明T－S模糊模型能自适应生成模糊规则,解决传统模糊系统不能自动将人类专家的知识经验转化为推理规则库的问题,为非线性系统建模奠定了基础。     

RBF-ARX模型在倒立摆系统建模中的应用

针对直线一级倒立摆控制系统的非线性特性,采用RBF-ARX模型对倒立摆系统的全局非线性动态特性进行建模.讨论了RBF-ARX模型结构的选取,模型参数辨识,RBF参数优化等问题.并且分别比较了该倒立摆系统的RBF-ARX模型与全局线性ARX模型,以及将RBF-ARX在某一工作点局部线性化后的模型与局部线性ARX模型的预测输出和模型误差,验证了RBF-ARX模型在倒立摆系统建模和辨识中的有效性.     

T-S模糊模型在跳汰机排料系统中的应用

针对常规的PID控制难以解决实际工业系统的时滞性、非线性等问题,文章提出了一种基于T-S模糊模型的跳汰机排料系统的设计方案,介绍了跳汰机自动排料控制系统的组成,分析了T-S模糊模型辨识原理,详细阐述了基于T-S模糊模型的跳汰机排料系统的建模及仿真。实验结果表明,T-S模糊模型能够比较精确地反映出被控系统中输入与输出之间的关系,也证明了T-S模糊模型可用于非线性控制系统的建模仿真,是现代控制理论与非线性控制系统之间进行沟通的有力工具。     

T-S模糊算法在水箱液位非线性控制中的应用

通过实验手段及机理建模法获得多容水箱液位控制的分段线性化数学模型,从而采用T-S型模糊算法构成控制器,不仅能在每个模糊子区间中建立分段线性模型,还能借助隶属度函数将各分段线性模型平稳地连接成一个整体非线性系统模型,有效克服参数突变而引起的扰动,进一步提高控制系统动态响应性能。     

一类非线性系统的模糊变结构控制

研究了一类非线性系统的模糊变结构控制问题，并给出了稳定性证明。通过将非线性系统化为多个精确T—S模型来建立非线性系统精确的T—S模糊模型，将模糊理论与成熟的线性变结构控制理论相结合设计一种模糊变结构控制器，用Lyapunov稳定性理论证明该控制器能确保模糊动态模型全局渐近稳定，从而使非线性系统稳定。仿真结果表明了该设计方法的有效性。     

神经模糊系统在制动干扰模型中的应用

关于汽车制动优化问题,由于汽车制动器摩擦系数的干扰,产生汽车制动振荡现象,系统的模型是一个由线性与非线性糅合的系统,针对非线性部分难以建立数学模型,采用传统的BP神经网络进行系统辨识,在辨识过程中常常出现收敛速度慢、易导致局部极值等不良现象,从而影响系统辨识的速度及其信号的跟踪性.为解决上述问题,结合模糊推理系统模型具有控制精度高与神经网络自学习的功能,提出了使用神经模糊系统来进行非线性动态部分辨识的方法.分别采用传统的BP网络与模糊神经系统的方法对非线性动态系统进行辨识,通过利用MATLAB软件进行仿真.仿真结果表明,神经模糊系统的非线性动态系统辨识方法,不仅能够提高系统辨识的收敛速度,还能提高辨识精度及信号的跟踪性能.     

一种模糊滑模变结构控制方案及其在混沌系统控制中的应用

采用模糊动态模型逼近非线性系统，将非线性 系统模糊化为局部线性模型．用Lyapunov稳定性理论设计出确保模糊动态模型全局渐近稳 定的变结构控制器．应用到两类混沌系统的稳定控制中，验证了方案的有效性．模糊控制器 简单，规则少．     

基于Sugeno 模糊模型的帆船控制方法研究

针对非线性、时变的帆船航行系统，提出一种基于Sugeno模糊模型的帆船控制新方法．采集舵手的航行经验建立知识库，将专家知识融合到控制系统中，提高了控制系统的智能度；采用Sugeno模糊模型，将舵角的非线性控制局部线性化，并设计相应的局部线性控制器，通过模糊推理综合各局部线性控制器的输出，得到全局控制量．仿真结果表明，该方法能实现对帆船航向的智能控制，具有一定的实用价值．     

非线性时延网络控制系统的模糊建模与控制

针对时变网络诱导时延小于一个采样周期的非线性时延网络控制系统,讨论系统的稳定性及控制器的设计方法.利用基于“IF-THEN”规则的模糊模型近似系统中的非线性,将时延的不确定性转化为系统参数的不确定性,从而将此类非线性网络控制系统建模为一类具有参数不确定性的离散Takagi-Sugeno（T-S）模糊模型.基于建立的模型,利用Lyapunov方法和线性矩阵不等式方法,分析了系统的稳定性及模糊状态反馈控制器的设计方法,最后通过仿真实例验证了所提出方法的有效性.     

基于T—S模糊模型的多变量预测控制

提出一种基于T-S模糊模型的多输入多输出预测控制策略.T-S模糊模型用于描述对象的非线性动态特性,模糊规则将非线性系统划分为多个局部子线性模型.为提高预测控制性能,采用多步线性化模型构成多步预报器,从而将预测控制中的非线性优化问题转化为一个线性二次寻优问题.串接贮槽液位控制系统的仿真结果表明,多步线性化模型预测控制性能优于单步线性化模型预测控制性能.     

基于向量Lyapunov函数方法的模糊控制系统稳定性分析和设计

采用模糊动态模型对连续时间非线性系统进行模糊控制,对闭环模糊系统的稳定性进行分析,并给出系统化的控制器设计程序,在一系列局部模型通过模糊隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并利用向量Lyapunov函数的概念和方法,得到了闭环模糊系统稳定的充分条件;仿真例子验证了该设计方法的正确性。     

基于模糊动态模型的多变量系统模糊控制

采用模糊动态模型对多变量复杂非线性系统进行模糊控制.首先针对局部线性动态模 型设计状态反馈控制器,然后利用模糊推理确定整个系统的控制;在一系列局部模型通过模糊 隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并 利用大系统分散控制关联稳定性的概念和方法,得到了闭环模糊系统稳定的充分条件.仿真例 子验证了该设计方法的正确性.     

Fuzzy model based adaptive control for a class of nonlinear systems

A fuzzy model based adaptive control algorithm for a class of continuous-time nonlinear dynamic systems is presented. The fuzzy model consisting of a set of linear fuzzy local models that are combined using a fuzzy inference mechanism is used to model a class of nonlinear systems. Each fuzzy local model represents a linearized model corresponding to the operating point of the controlled nonlinear system. The proposed control algorithm employs the fuzzy controller that is designed by considering the linear state feedback controller corresponding to the fuzzy local model with the maximum weight and the switching-σ modification adaptive controller to adaptively compensate for the plant nonlinearities. Stability robustness of the closed-loop system is analyzed in Lyapunov sense. It is shown, that the proposed control algorithm guarantees global stability of the system with the output of the system approaching the origin if there are no disturbances and uncertainties, converging to the neighborhood of the origin for all realizations of uncertainties and disturbances. The simulation examples for controlling inverted pendulum system are given to illustrate the effectiveness of the proposed method     

A dynamic fuzzy model for a drum-boiler-turbine system

A nonlinear dynamic fuzzy model for natural circulation drum-boiler-turbine is presented. The model is derived from Åström-Bell nonlinear dynamic system and describes the complicated dynamics of the physical plant. It is shown that the dynamic fuzzy model gives in some appropriate sense accurate global nonlinear prediction and at the same time that its local models are close approximations to the local linearizations of the nonlinear dynamic system. This closeness is illustrated by simulation in various conditions.     

Recurrent neuro-fuzzy networks for nonlinear process modeling

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.     

非线性系统模糊神经网络变结构自适应控制

在非线性系统的模糊动力学模型基础上,提出一种模糊神经网络变结构自适应控制器;网络的结构根据非线性系统特性动态构成,基于该网络提出非线性预测器,基于梯度法提出了一种网络参数学习算法,并分析了收敛性及其性质。将网络预测器与参数学习算法相结合,构成自适应控制算法,证明了算法的收敛性。仿真结果证实了算法的有效性。     

Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.     

CLUSTERED BASED TAKAGI‐SUGENO NEURO‐FUZZY MODELING OF A MULTIVARIABLE NONLINEAR DYNAMIC SYSTEM

This research frame work investigates the application of a clustered based Neuro‐fuzzy system to nonlinear dynamic system modeling from a set of input‐output training patterns. It is concentrated on the modeling via Takagi‐Sugeno (T‐S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Hence, such created initial memberships are then employed to construct suitable T‐S sub‐models. Furthermore, the T‐S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). Compared to other well‐known approximation techniques such as artificial neural networks, fuzzy systems provide a more transparent representation of the system under study, which is mainly due to the possible linguistic interpretation in the form of rules. Such intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of fuzzy if‐then rules. The developed T‐S Fuzzy modeling system has been then applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Validation results have resulted in a very close antenna sub‐models of the original nonlinear antenna system. The suggested technique is very useful for development transparent linear control systems even for highly nonlinear dynamic systems.     

RBF-ARX模型在液位系统建模中的应用

针对单容液位系统紊流时的非线性特征,采用RBF-ARX模型对单容液位系统进行离线动态特性建模的研究;分别在液位高中低三个工作点建立了其局部线性ARX模型,它们的单位阶跃响应存在巨大差异,证实了整个系统具有较强的非线性;讨论了RBF-ARX模型结构的选取,模型参数辨识,RBF参数优化等问题;模型的预测输出和仿真结果,证实了RBF-ARX模型在非线性系统建模和辨识中的有效性.     

Chattering-free fuzzy variable structure control for multivariable nonlinear systems

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) with guaranteed stability for multivariable systems is presented. It is aimed at obtaining an improved performance of nonlinear multivariable systems. The main contribution of this work is firstly developing a generic matrix formulation of the FLC-VSC algorithm for nonlinear multivariable systems, with a special attention to non-zero final state. Secondly, ensuring the global stability of the controlled system. The multivariable nonlinear system is represented by T-S fuzzy model. The identification of the T-S model parameters has been improved using the well known weighting parameters approach to optimize local and global approximation and modeling capability of T-S fuzzy model. The main problem encountered is that T-S identification method cannot be applied when the membership functions (MFs) 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. In order to overcome the chattering problem a 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. A two-link robot system and a mixing thermal system are chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of proposed FLC-VSC method.