轮式移动机器人驱动系统的动力学模型的研究

本文论述了一种首先应用阶跃响应的面积法得到开环系统的辩识模型，然后结合对开环辨识模型的根轨迹分析进行闭环系统的模型综合的辩识方法。     

发电机励磁系统参数辨识的仿真研究

研究发电机励磁系统参数辩识问题,由于励磁系统是一个非线性系统,造成电力系统不稳定.传统时域或频域辩识方法不能辩识其非线性环节,导致励磁系统辩识的精度低.为了提高发电机励磁系统的辩识精度,提出一种神经网络的发电机励磁系统参数非线性辨识方法.以发电机励磁系统实际输入作为神经网络的输入,以实际励磁系统输出与神经网络输出之间的最小误差作为目标函数,通过不断调整神经网络的权值对神经网络模型进行优化,最后得到满足系统误差要求的发电机励磁系统参数.仿真结果表明,改进方法解决了传统辩识方法无法准确辩识励磁系统非线性环节的难题,有效提高了励磁系统的辨识精度.     

电力系统小波神经网络负荷频率控制器的研究

将小波网络用于电力系统负荷频率辨识和控制中，建立了非线性的电力系统负荷频率控制LFC模型，用递归NARMA模型的小波网络辩识器对LFC模型进行了辩识，利用Akaike’s的最终预测误差准则FPE和信息准则AIC，进行了隐层节点数目和反馈阶次的计算，用辩识结果建立了NARMA模型的小波网络的控制器，对LFC模型进行控制，理论和仿真表明辩识和控制模型可取得较好效果。     

励磁系统参数辨识中智能建模与仿真研究

研究发电机励磁系统性能,是保证电力系统的稳定性的有效手段,发电机励磁系统模型参数采有时域或频域进行辩识,但是发电机励磁系统是一个非线性系,传统方法存在不能辨识其非线性环节的缺点,导致辩识的精度低.为了提高励磁系统辩识的精度,提出一种粒子群算法优化的励磁系统参数辨识的方法.以励磁系统的实际输入作为模型的输入,以模型的输出和实际励磁系统最小误差作为目标函数,通过改进粒子群算法对模型参数进行优化调整,获得满足误差要求的励磁系统参数.在Matlab环境下进行仿真,结果表明方法具有较快的收敛速度和较高的辨识精度,有效解决了发电机励磁系统正确建模,为提高电力系统运行稳定性提供参考.     

系统盲辩识的矩阵算法

盲辩识是知道系统输出和输入的某些统计特性辩识系统.由系统输出导出辩识系统冲击响应的矩阵方程,建立系统的盲辨识方程.用提出的优化算法(QCGA)解矩阵方程辩识系统.计算机模拟表明提出的优化算法优于奇异值分解(SVD)算法,而新的盲辨识算法为一种快速有效算法.     

神经网络在系统辩识中的应用研究

系统辩识是进行系统控制、预报、分析和决策的必不可少的一环，神经网络的出现使传统辩识技术难以胜任的一般非线性系统辩识问题得以解决，本文从仿真着手从最基本的常用工业对象出发，对多变量系统、非线性系统研究，用ＢＰ网络进行仿真，表明神经网络在系统辩识的各个领域都有广阔的应用前景，并且有相当高的辩识精度。     

非线性系统广义脉冲响应函数的盲辨识

探讨减少非线性系统广义脉冲响应函数（GIRF）盲辨识所需计算量问题。 基于线性MIMO模型，应用多项式矩阵理论和子空间盲辨识技术，研究使用部分噪声向量对非线性Volterra系统的GIRF盲辨识方法。该方法的优点是能有效减少GIRF盲辨识所需的计算量。这对GIRF盲辩识方法的在线应用是有利的。仿真结果说明了这一方法的有效性。     

基于神经网络的精密运动系统运动平台辨识

针对精密运动定位系统中宏动平台具有死区与迟滞的复合特性,提出了一种特殊的神经网络结构,将通常用于逼近光滑系统的神经网络模型改进为可以描述非光滑非线性特性的模型,在模型结构中引入一种非光滑激励函数,并引入广义梯度改进麦夸特算法,以用其对精密运动系统的含有非光滑非线性的运动特性进行建模.在所设计的神经网络中,同时也采用了扩展辩识空间方法,首先将迟滞特性的多值映射变为一一映射,而且还证明了采用完备化的算子基对辩识逼近的必要性及其扩展辩识空间的途径.实际辨识结果表明,所提出的建模方法取得了令人满意的结果.     

采用改进PSO的非线性系统T-S模糊模型辩识

提出了一种新的T-S模糊模型的非线性系统辨识方法。采用自适应模糊C均值聚类算法确定模糊模型的前件结构及参数,用改进的粒子群优化(PSO)算法来辩识模糊模型的结论参数以获得系统参数的最优估计。仿真结果表明该方法是有效的。     

带种族遗传算法的神经网络辨识器及其应用

为了解决油田动态系统建立数字模型难和模型训练难的问题,以多层动态前向网络为模型框架,给出了一种新的油田系统神经网络辩识器,它对上述系统具有通用性。为了训练该辩识器和避免极值问题,模拟生物种族进化机制,设计了新的种族遗传算法。该算法是由改进的带共享机制的遗传算法、启发式山峰聚类算法和新的Powell算法有机结合构成的新型混合算法。应用表明,种族遗传算法具有极强的全局和局部搜索能力,这确保了上述神经网络辩识器具有很高的预测精度,平均相对误差在1%以内。     

Exponential convergence of adaptive identification and control algorithms

Output and equation error adaptive identification algorithms are shown to be exponentially convergent under a deterministic or stochastic persistently exciting (or spanning) condition on the system inputs together with several other standard conditions. An adaptive control algorithm is shown to be exponentially convergent under a deterministic or stochastic persistently exciting condition on the reference trajectory together with some standard conditions.     

基于规则熵函数法的结构自适应模糊辨识与控制

提出一种新的结构自适应模糊辨识器,其特点是采用“规则熵函数”法在线调节模糊逻辑系统参数,使规则前件参数趋于一致,从而进行规则合并,简化了系统结构。将该结构自适应模糊辨识器用于一类非线性系统的自适应控制,仿真结果验证了所提出方法的有效性。     

The adaptive control of nonlinear systems using the Sugeno-type offuzzy logic

An adaptive fuzzy controller is synthesized from a collection of fuzzy IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms in the fuzzy IF-THEN rules are changed according to some adaptive laws for the purpose of controlling a plant to track a reference trajectory. In the paper, a direct adaptive fuzzy control design method is developed for the general higher order nonlinear continuous systems. We use the Sugeno-type of the fuzzy logic system to approximate the controller. It is proved that the closed-loop system using this adaptive fuzzy controller is globally stable in the sense that all signals involved are bounded. Finally, we apply the method of direct adaptive fuzzy controllers to control an unstable system     

Design of adaptive fuzzy model for classification problem

The main theme of this paper is to set up an adaptive fuzzy model for a new classification problem. At first, we propose a fuzzy classification model that can automatically generate the fuzzy IF-THEN rules by the features of the training database. The consequent part of the fuzzy IF-THEN rule consists of the confident value of the rule and which class the datum should belong to. Then a novel adaptive modification algorithm (AMA) is developed to tune the confident value of the fuzzy classification model. The proposed model comprises three modules, generation of the fuzzy IF-THEN rules, determination of the classification unit, and setup of the AMA. Computer simulations on the well known Wine and Iris databases have tested the performance. Simulations demonstrate that the proposed method can provide sufficiently high classification rate in comparison with other fuzzy classification models.     

Composite Adaptive Control of Uncertain Euler‐Lagrange Systems with Parameter Convergence without PE Condition

This work proposes a novel composite adaptive controller for uncertain Euler‐Lagrange (EL) systems. The composite adaptive law is strategically designed to be proportional to the parameter estimation error in addition to the tracking error, leading to parameter convergence. Unlike conventional adaptive control laws which require the regressor function to be persistently exciting (PE) for parameter convergence, the proposed method guarantees parameter convergence from a milder initially exciting (IE) condition on the regressor. The IE condition is significantly less restrictive than PE, since it does not rely on the future values of the signal and that it can be verified online. The proposed adaptive controller ensures exponential convergence of the tracking and the parameter estimation errors to zero once the sufficient IE condition is met. Simulation results corroborate the efficacy of the proposed technique and also establishes it's robustness property in the presence of unmodeled bounded disturbance.     

COMPRESSION AND EXPANSION OF FUZZY RULE BASES BY USING CRISP-FUZZY NEURAL NETWORKS

A fuzzy neural network with knowledge discovery FNNKD is designed to perform adaptive compensatory fuzzy reasoning based on more useful and more heuristic primary fuzzy sets. In order to overcome the weakness of the conventional crisp neural network and the fuzzy operation oriented neural network, we have developed a general fuzzy reasoning oriented fuzzy neural network called a crisp-fuzzy neural network CFNN that is capable of extracting high-level knowledge such as fuzzy IF-THEN rules from either crisp data or fuzzy data. A CFNN can effectively compress a 5 5 fuzzy IF-THEN rule base of a cart-pole balancing system to a 3 3 one, then to a 2 2 one, and finally to a 1 1 one, and can expand on invalid sparse 3 3 fuzzy IF-THEN rule base of a cart-pole balancing system to a valid 5 5 one. In addition, a CFNN can control a more complex cart-pole balancing system with random fuzzy noise inputs and outputs i.e., nonconventional using crisp inputs and outputs without any noise . The simulations have indicated that a CFNN is an efficient neurofuzzy system with abilities to discover new fuzzy knowledge from either numerical data or fuzzy data, compress and expand fuzzy knowledge, and do fuzzy reasoning.     

Fuzzy adaptive filters, with application to nonlinear channelequalization

Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come from human experts or are determined during the adaptation procedure by matching input-output data pairs; (3) construct a filter based on the set of rules; and (4) update the free parameters of the filter using the RLS algorithm. The design procedure for the LMS fuzzy adaptive filter is similar. The most important advantage of the fuzzy adaptive filters is that linguistic information (in the form of fuzzy IF-THEN rules) and numerical information (in the form of input-output pairs) can be combined in the filters in a uniform fashion. The filters are applied to nonlinear communication channel equalization problems     

Analysis of adaptive identifiers in the presence of unmodeleddynamics: averaging and tuned parameters

The authors analyze the behavior of a standard identifier when the plant contains additional dynamics, called unmodeled dynamics, which invalidate the known order assumption. The first result of the analysis is an input richness condition which does not depend on the order of the unmodeled dynamics to guarantee persistency of excitation of the regressor. Then it is shown that the persistently exciting (PE) condition leads to a BIBO (bounded-input bounded-output) stability property for the identifier. The method of averaging is used to formally define the notion of tuned parameters as the equilibrium of the identifier averaged system. It is shown that the tuned parameters always exist and that the actual parameters converge to some neighborhood of the tuned parameters. From the definition of the tuned parameters, an explicit expression to calculate and interpret them as the fixed parameter values that minimize the mean-squared output error is derived     

一种新的自适应非单点模糊辨识器

鉴于常规单点模糊逻辑系统在解决不确定性问题中存在的不足,该文在分析非单点模糊逻辑理论的基础之上,提出了一种新的自适应非单点模糊辨识器,并且详细论述了其相关理论、具体实现步骤和参数优化方法。针对一种糖酵解混沌振荡器模型的非线性动态系统辨识问题,采用非单点模糊逻辑系统对其进行了仿真研究,取得了较好的逼进和收敛效果,从而验证了该非单点模糊辨识器的可行性和有效性。该研究结果表明了基于非单点模糊逻辑系统构造的自适应辨识器能够在一定精度和时间区域内跟踪非线性动态系统的输出,并且非单点模糊理论将能够在控制等其它应用领域取得较好的应用效果。