给水管网的能量需求建模及其节能研究

为了实现给水管网系统能量的供需平衡,对管网建模进行了研究。针对目前大多数给水系统更适合建立宏观模型的特点,提出了为给水系统建立具有较好精度的输入与输出非线性关系的神经网络宏观模型的方案。在某给水系统中的应用结果表明,该方案的实施能够获得较大的节能空间,为给水系统的能量供给提供节能依据。该建模方法具有普适性,可将其推广应用到其他给水系统。     

模糊预测控制炉窑温度系统中应用

针对炉窑温度系统的大时滞、多扰动和非线性的特点,将T-S模糊状态空间模型作为预测控制的预测模型,并将T-S模糊表示的非线性系统转化为线性时变系统,给出了基于状态空间的多变量复杂系统的T-S模糊模型表达形式,设计出预测时域内多模型的非线性模糊预测控制器。根据实际控制中对控制量和输出的约束,将控制器输出求解转化为二次规划问题。     

基于二次仿射传播聚类的非线性系统多模型LSSVM建模

针对复杂非线性系统单模型建模存在计算量大和精度差的问题,提出一种采用仿射传播聚类的多模型LSSVM建模方法,通过仿射传播聚类对样本数据按输入集和输出集二次聚类划分,并分别建立LSSVM子模型,非线性系统模型通过子模型加权合成.将该方法应用于两电机变频调速系统的张力和速度模型辨识,仿真结果表明,该建模方法具有较高的精度,能准确拟合系统的非线性特性.     

基于Preisach模型的迟滞系统建模与控制

针对一种复杂的非线性系统一迟滞系统,研究了基于KP算子Preisach模型对迟滞系统进行建模的方法。利用Preisach模型与其边界线之间的映射关系,建立了容易在线更新的迟滞模型。基于Preisach模型进行迟滞非线性系统的控制,采用PID方法来控制一类带有未知非线性特性迟滞的单输入单输出非线性系统。对迟滞非线性系统的建模与控制进行的数值仿真研究结果表明,该迟滞非线性系统的建模和控制方法具有理论意义和应用价值。     

多自由度非线性系统动态参数化模型建模方法研究

针对多自由度非线性系统的动态模型辨识问题,基于NARX(Non-linear Autoregressive with Exogenous inputs)模型的建模方法,考虑系统的物理设计参数,建立非线性系统动态参数化模型.首先,根据系统输入、输出数据建立系统不同参数下的NARX模型,并通过EFOR(Extended Forward Orthogonal Regression)算法对不同参数下NARX模型进行修正,以统一辨识得到的系统模型结构.随后,建立NARX模型系数与物理设计参数间的函数关系,得到多自由度非线性系统的动态参数化模型.以单输入、单输出两自由度非线性系统为例,根据数值仿真结果,对系统的动态参数化模型建模过程进行说明.最后,以带非线性涂层阻尼的悬臂梁作为试验对象,建立其动态参数化模型以反映其动力学特性.试验结果表明,非线性系统动态参数化模型能准确预测多自由度非线性系统的输出响应,为非线性系统的分析与优化设计提供了理论基础.     

辨识非线性MIMO系统的多输出ε-SVR模型研究

针对非线性多输入多输出(MIMO)系统的黑箱辨识问题,提出一种基于ε不敏感损失函数的多输出支持向量回归机(SVR)模型,并给出了偏置的有效求取算法.在一个优化问题中,该模型能最小化所有输出带正则项的结构风险总和,并能为不同输出选择不同的核函数及模型参数.将多输出SVR模型应用于非线性MIMO系统的辨识,仿真结果表明,该模型克服了传统支持向量回归机必须为每个输出单独建模这一缺陷,并能提升系统的整体辨识能力.     

基于新型动态神经元网络的逆系统方法

针对提高逆系统建模中神经网络的逼近效果和动态性能问题,根据PID神经元网络工作原理,提出一种具有动态激励函数的新型PID神经元模型—输出反馈型PID神经元(OFPID),输出激励采用连续的Sigmoidal函数,使神经元具有等效的IIR突触,采用梯度下降法实现OFPID神经元网络的权值调整,将其应用于非线性系统的神经网络逆控制系统,从而提高非线性系统的解耦效果和控制性能。仿真实验证明,提出的新型神经元网络是一种良好的非线性系统建模和控制工具。     

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.     

四旋翼飞行仿真器的建模研究

本文针对四旋翼飞行仿真器系统的非线性.采用RBF-ARX模型对四旋翼飞行仿真器系统进行了离线动态特性建模的研究.着重讨论了RBF-ARX模型结构的选取,模型参数辨识,RBF参数优化等问题.RBF-ARX模型与ARX模型的一步预测输出比较的结果证实了RBF-ARX模型在非线性系统建模中的优越性.     

RBF-ARX模型在三容水箱液位控制系统建模中的应用

针对三容水箱液位系统的非线性，采用RBF-ARX模型对三容液位系统进行了离线动态特性建模的研究。着重讨论了RBF-ARX模型结构的选取、模型参数辨识、RBF参数优化等问题。RBF-ARX模型与ARX模型的一步预测输出比较的结果证实了RBF-ARX模型在非线性系统建模中的优越性。     

Piecewise Fuzzy Anti-Windup Dynamic Output Feedback Control of Nonlinear Processes With Amplitude and Rate Actuator Saturations

In this paper, a novel anti-windup dynamic output compensator is developed to deal with the robust H _infin output feedback control problem of nonlinear processes with amplitude and rate actuator saturations and external disturbances. Via fuzzy modeling of nonlinear systems, the proposed piecewise fuzzy anti-windup dynamic output feedback controller is designed based on piecewise quadratic Lyapunov functions. It is shown that with sector conditions, robust output feedback stabilization of an input-constrained nonlinear process can be formulated as a convex optimization problem subject to linear matrix inequalities. Simulation study on a strongly nonlinear continuously stirred tank reactor (CSTR) benchmark plant is given to show the performance of the proposed anti-windup dynamic compensator.     

一类模糊双线性跳变系统的随机镇定问题

研究了一类模糊双线性跳变系统的随机镇定问题. 采用T-S模糊建模技术来构建模糊双线性跳变模型, 然后通过并行分布补偿 (Parallel distributed compensation, PDC) 方法和选择合适的模糊隶属度函数, 将整个非线性控制器表示为一组局部线性控制器的模糊综合. 此外, 还推导出了保证闭环模糊双线性跳变系统随机稳定的充分条件, 并且这些条件最终可归结为一组线性矩阵不等式 (Linear matrix inequalities, LMIs)的可行性问题. 最后, 连续搅拌反应釜(Continuous stirred tank reactor, CSTR)系统的数值示例表明该设计方法的合理性和有效性.     

基于多模糊模型的非线性预测控制

研究了基于多模糊模型的非线性预测控制问题 ,提出了基于多模型融合的非线性预测控制方法 .首先根据实际对象在不同运行点附近的状态建立了非线性系统的线性多模糊模型表示 ,然后给出了基于多模糊模型的预测控制原理结构框图 .非线性多模糊模型被用来作为预测模型 ,CSTR过程的仿真研究表明是一种有前景的非线性预测控制方法 .     

一种新型时滞系统鲁棒控制器设计方法

针对一类用模糊动态模型描述的非线性时滞系统，提出一种基于模糊性能评估器的新型鲁棒控制方案，模糊性能评估器用于检验模糊模型及其控制率的有效性，以线性矩阵不等式的形式，给出了模糊性能评估器和模糊控制器存在的充分条件；分析了闭环控制效果与模糊性能评估器性能之问的关系，从而说明该方法为模糊控制系统提供了一种无损调试方法，最后以CSTR系统为例，通过仿真验证了该方法的有效性。     

Analysis and synthesis of nonlinear time-delay systems via fuzzycontrol approach

Takagi-Sugeno (TS) fuzzy models (1985, 1992) can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input/output (I/O) submodels. In this paper, the TS fuzzy model approach is extended to the stability analysis and control design for both continuous and discrete-time nonlinear systems with time delay. The TS fuzzy models with time delay are presented and the stability conditions are derived using Lyapunov-Krasovskii approach. We also present a stabilization approach for nonlinear time-delay systems through fuzzy state feedback and fuzzy observer-based controller. Sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain are derived through the numerical solution of a set of coupled linear matrix inequalities. An illustrative example based on the CSTR model is given to design a fuzzy controller     

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.     

Interval Fuzzy Model Identification Using$l_infty$-Norm

In this paper, we present a new method of interval fuzzy model identification. The method combines a fuzzy identification methodology with some ideas from linear programming theory. We consider a finite set of measured data, and we use an optimality criterion that minimizes the maximum estimation error between the data and the proposed fuzzy model output. The idea is then extended to modeling the optimal lower and upper bound functions that define the band which contains all the measurement values. This results in lower and upper fuzzy models or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). We also showed that the proposed structure uniformly approximates the band of any nonlinear function. The interval fuzzy model identification is a methodology to approximate functions by taking into account a finite set of input and output measurements. This approach can also be used to compress information in the case of large amount of data and in the case of robust system identification. The method can be efficiently used in the case of the approximation of the nonlinear functions family. If the family is defined by a band containing the whole measurement set, the interval of parameters is obtained as the result. This is of great importance in the case of nonlinear circuits' modeling, especially when the parameters of the circuits vary within certain tolerance bands     

Robust model predictive control of discrete nonlinear systems with time delays and disturbances via T–S fuzzy approach

In this paper, robust fuzzy model predictive control of a class of nonlinear discrete systems subjected to time delays and persistent disturbances is investigated. Based on the modeling method of delay difference inclusions, nonlinear discrete time-delay systems can be represented by T–S fuzzy systems comprised of piecewise linear delay difference equations. Moreover, Lyapunov–Razumikhin function (LRF), instead of Lyapunov–Krasovskii functional (LKF), is employed for time-delay systems due to its ability to reflect system original state space and its advantages in controller synthesis and computation. The robust positive invariance and input-to-state stability with respect to disturbance under such circumstances are investigated. A robust constraint set is adopted that the system state is converged to this set round the desired point. In addition, the controller synthesis conditions are derived by solving a set of matrix inequalities. Simulation results show that the proposed approach can be successfully applied to the well-known continuous stirred tank reactor (CSTR) systems subjected to time delay.     

基于模糊分类的模糊神经网络辨识方法及应用

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)，给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁，逼近能力强，能够显著提高辨识精度，并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中，仿真结果验证了该方法的有效性，表明该网络能够实现复杂非线性系统的建模，而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。     

Structure and stability analysis of general Mamdani fuzzy dynamic models

Mamdani fuzzy models have always been used as black‐box models. Their structures in relation to the conventional model structures are unknown. Moreover, there exist no theoretical methods for rigorously judging model stability and validity. I attempt to provide solutions to these issues for a general class of fuzzy models. They use arbitrary continuous input fuzzy sets, arbitrary fuzzy rules, arbitrary inference methods, Zadeh or product fuzzy logic AND operator, singleton output fuzzy sets, and the centroid defuzzifier. I first show that the fuzzy models belong to the NARX (nonlinear autoregressive with the extra input) model structure, which is one of the most important and widely used structures in classical modeling. I then divide the NARX model structure into three nonlinear types and investigate how the settings of the fuzzy model components, especially input fuzzy sets, dictate the relations between the fuzzy models and these types. I have found that the fuzzy models become type‐2 models if and only if the input fuzzy sets are linear or piecewise linear (e.g., trapezoidal or triangular), becoming type 3 if and only if at least one input fuzzy set is nonlinear. I have also developed an algorithm to transfer type‐2 fuzzy models into type‐1 models as far as their input–output relationships are concerned, which have some important properties not shared by the type‐2 models. Furthermore, a necessary and sufficient condition has been derived for a part of the general fuzzy models to be linear ARX models. I have established a necessary and sufficient condition for judging local stability of type‐1 and type‐2 fuzzy models. It can be used for model validation and control system design. Three numeric examples are provided. Our new findings provide a theoretical foundation for Mamdani fuzzy modeling and make it more consistent with the conventional modeling theory. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 103–125, 2005.