基于模糊树模型的混沌时间序列预测

应用模糊树模型,对混沌时间序列进行建模和预测.该方法可以根据建模数据在空间中的分布信息,基于二叉树结构自适应划分输入空间,得到模糊子空间,在与叶节点对应的子空间上建立线性函数作为模糊规则的后件,用隶属度函数将各分片线性函数光滑连接,最后得到一个精度比较高的非线性映射.通过对Mackey-Glass、Lorenz和Henon混沌时间序列的建模和预测研究,仿真结果表明,该方法具有建模精度高、运行速度快、泛化能力强、预测步数多、适用范围广等优点. 关键词： 模糊树模型 混沌时间序列 预测     

智能预测控制在谷物干燥过程中的应用研究

谷物干燥过程控制是一个高精度智能预测控制模型,为了提高谷物干燥过程控制的可靠性和自适应性,提出一种基于加权组合预测的谷物干燥过程智能预测控制模型,首先建立谷物干燥过程控制的约束参量模型和控制目标函数,采用模糊决策的PID控制方法进行控制模型设计,采用加权组合预测方法进行自适应神经网络加权,提高控制过程的适应度和支持度;然后选用TMS320VC5509A作为核心控制芯片,进行控制系统设计,在Linux2.6.32嵌入式内核平台中进行谷物干燥预测控制软件开发,实现预测控制系统优化设计;最后进行应用测试分析,结果表明,采用该系统进行谷物干燥过程的控制,能有效调节谷物干燥中的温度和湿度,控制精度较好,人机交互性较好。     

基于RBF-ARX模型的四旋翼飞行器的预测控制

四旋翼飞行器模型的准确性对系统的控制效果和安全运行起着关键作用,具有非线性的飞行器系统较难准确建立其物理模型,提出了一种新型基于RBF-ARX模型的预测控制算法,并通过仿真和实控实验,证实了该方法可行性和有效性。     

基于RBF模糊神经网络的垂直轴风力机设计

半转结构垂直轴风力机具有优良的空气动力特性,为了进一步提高风电系统输出电能的质量,在RBF神经网络与模糊逻辑系统的函数等价条件下,探索了基于T-S模糊模型的RBF模糊神经网络控制算法,并依此设计出两叶结构半转式垂直轴风力机控制器;通过与常规PID控制算法的仿真比较,表明基于T-S模型的RBF模糊神经网络控制算法在两叶结构半转式垂直轴风力机的恒转速控制方面具有一定的优越性。     

基于模糊模型的耦合时空混沌H_\infty$跟踪控制

由于子系统的时空耦合作用，实现耦合时空混沌的跟踪控制比较困难。然而模型未知的耦合时空混沌的子系统可由一系列模糊逻辑模型逼近，每个模糊逻辑模型代表子系统在特定运行点的局部线性化模型。基于该系列模糊模型，采用模糊跟踪控制方法实现了耦合时空混沌的模型参考跟踪控制，并用线性矩阵不等式的凸优化方法求解控制器参数，确保系统的全局渐近稳定性。仿真验证了方案的有效性。     

基于模糊神经网络的短时交通流预测方法研究

为满足交通控制和诱导系统的实时性需求,减少交通拥挤状况,降低交通事故突发频率,需要对短时交通流进行预测。当前的短时交通流预测方法是采用K-近邻的非参数回归对其进行预测,预测过程中没有将预测模型中关键因素对交通流的影响进行详细的说明,导致预测结果不准确,存在短时交通流预测误差较大的问题。为此,提出一种基于模糊神经网络的短时交通流预测方法。该方法首先以历史短时交通流数据样本序列为基础,将提取的关联维数作为短时交通流的混沌特征量,然后以该特征量为依据,对短时交通流数据进行聚类,使相同的短时交通流聚合类样本比不同的交通流聚合类样本更为贴近,采用高斯过程回归对短时交通流预测模型进行建设,建设过程中利用差分方法对短时交通流预测序列进行平稳化操作之后,对短时交通流预测模型进行训练,将GPR模型引入至短时交通流预测过程中,得到交通流预测方差估计值,并确定交通流预测值置信区间,由此实现短时交通流的预测。由此实现短时交通流的预测。实验结果证明,所提方法可以准确地预测交通运输系统的实时状况,为车辆行驶的最佳路线进行了有效引导,减少了自然影响方面和人为因素对短时交通流预测结果的干扰,为交通部门对交通路况的控制管理提供了依据。     

基于改进T-S模型的航空发动机振动值监控方法

针对T-S模型在规则库建立过程中存在的重复学习、效率低下等问题,采用模糊关联规则挖掘的方法对T-S模型进行改进;首先采用基于矩阵的模糊关联规则挖掘算法,深入分析和利用频繁项集的性质,减少数据库扫描次数以及挖掘过程中产生的中间结果,提高规则挖掘效率;其次,从挖掘结果中选取模糊规则,对其处理后建立T-S模型规则库;最后,将改进的T-S模型应用于航空发动机振动值监控中;实验结果表明,改进的T-S模型能够有效实现发动机振动监控。     

基于T-S模糊神经网络的智能轮椅避障控制策略

在复杂的环境中,智能轮椅的自主避障是安全的保证。本文提出基于超声波和激光传感器的环境深度分区算法,和T-S模糊神经网路的信息融合方法,实现避障策略。提高了智能轮椅控制系统的实时性和安全性,实现避障控制的高效性。     

基于模型预测控制的低温控温策略设计及实验验证

低温温度计的标定中,控温策略是低温温度计标定效率和精度的重要影响因素。本研究搭建了以液氦为冷源的低温温度计全自动标定系统,通过温控仪和Python/QT编写了上位机控制程序,对标定过程中所遇到的控温性能问题进行研究。提出一种基于模型预测控制和PID结合的控温策略,通过计算机仿真进行参数优化与实验测试的方法,对PID控温和模型预测控制控温进行实验验证。结果表明,采用模型预测控制后,温度超调量仅为PID模式下的45.42%,稳态调节时间缩短为PID下的23.61%。该控制方法能够显著提高低温系统的控温性能。     

一种预测混沌时间序列的模糊神经网络方法

给出了一种预测混沌时间序列的模糊神经网络及其学习方法，给出的方法能直接从数据中提取模糊规则，经过优化得到最佳模糊规则库，并利用神经网络的自学习功能修改隶属函数的参数和网络的权值，减少了规则的匹配过程，加快了推理速度，增强了网络的自适应能力. 使用该神经网络及其学习方法对Lorenz混沌时间序列进行了预测仿真研究，试验结果表明给出的预测工具和方法是有效的. 关键词： 模糊神经网络 模糊规则提取 混沌时间序列预测     

Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.     

燃煤发电CO2捕集系统协调预测控制

基于化学吸附的燃烧后CO2捕集技术是最具应用前景的燃煤电站脱碳方法。然而,捕集系统与电站之间存在强烈的相互干扰,增大了整体系统控制的难度。为此,本文提出一种燃煤发电CO2捕集整体系统协调预测控制策略。为燃煤电站机炉系统和捕集系统分别设计预测控制器,并将当前及未来估计的汽机抽汽流量和烟气流量作为前馈信号送入预测控制器,以此实现两套系统间的相互协调和深度结合。仿真结果验证了所提方法的优越性和有效性。     

Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.     

Dynamic Event-Triggered Predictive Control for Interval Type-2 Fuzzy Systems with Imperfect Premise Matching

This paper investigates the dynamic event-triggered predictive control problem of interval type-2 (IT2) fuzzy systems with imperfect premise matching. First, an IT2 fuzzy systems model is proposed, including a dynamic event-triggered mechanism, which can save limited network resources by reducing the number of data packets transmitted, and a predictive controller, which can predict the state of the system between the two successful transmitted instants to deal with unreliable communication networks. Then, according to the Lyapunov stability theory and imperfect premise matching method, sufficient conditions for system stabilization and the controller gain are obtained. Finally, the validity of the proposed method is demonstrated by the numerical examples.     

三相电压型PWM整流器模型预测直接功率控制

通过对三相电压型PWM整流器传统直接功率控制(DPC)引起的开关频率不固定,网侧电流谐波分量高,系统调节时间长等问题的分析,提出一种电压型三相PWM整流器模型预测直接功率控制方法,应用模型预测理论构建目标函数并对目标函数进行求偏导,对下一个采样周期的有功功率和无功功率变化进行预测,将模型预测理论与二阶拉格朗日插值法相结合进行功率修正,实现了有功和无功功率的实际值与预测值的误差最小,并采用空间矢量脉宽调制(SVPWM)产生PWM信号驱动整流器功率开关,实现固定的开关频率;仿真结果表明,本方法具有良好的动态和稳态性能,系统对电感参数的变化不敏感,有效降低了交流测电流总谐波失真(THD),提高了交流侧功率因数。     

Finite-Time Pinning Synchronization Control for T-S Fuzzy Discrete Complex Networks with Time-Varying Delays via Adaptive Event-Triggered Approach

This paper is concerned with the adaptive event-triggered finite-time pinning synchronization control problem for T-S fuzzy discrete complex networks (TSFDCNs) with time-varying delays. In order to accurately describe discrete dynamical behaviors, we build a general model of discrete complex networks via T-S fuzzy rules, which extends a continuous-time model in existing results. Based on an adaptive threshold and measurement errors, a discrete adaptive event-triggered approach (AETA) is introduced to govern signal transmission. With the hope of improving the resource utilization and reducing the update frequency, an event-based fuzzy pinning feedback control strategy is designed to control a small fraction of network nodes. Furthermore, by new Lyapunov–Krasovskii functionals and the finite-time analysis method, sufficient criteria are provided to guarantee the finite-time bounded stability of the closed-loop error system. Under an optimization condition and linear matrix inequality (LMI) constraints, the desired controller parameters with respect to minimum finite time are derived. Finally, several numerical examples are conducted to show the effectiveness of obtained theoretical results. For the same system, the average triggering rate of AETA is significantly lower than existing event-triggered mechanisms and the convergence rate of synchronization errors is also superior to other control strategies.     

A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control

We introduce the predictive control theory into the study of chaos control and propose a direct optimizing predictive control algorithm based on a neural network model. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems.The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.     

Model Checking Fuzzy Computation Tree Logic Based on Fuzzy Decision Processes with Cost

In order to solve the problems in fuzzy computation tree logic model checking with cost operator, we propose a fuzzy decision process computation tree logic model checking method with cost. Firstly, we introduce a fuzzy decision process model with cost, which can not only describe the uncertain choice and transition possibility of systems, but also quantitatively describe the cost of the systems. Secondly, under the model of the fuzzy decision process with cost, we give the syntax and semantics of the fuzzy computation tree logic with cost operators. Thirdly, we study the problem of computation tree logic model checking for fuzzy decision process with cost, and give its matrix calculation method and algorithm. We use the example of medical expert systems to illustrate the method and model checking algorithm.     

基于模糊PID的UEGO传感器控制方法研究

宽域废气氧(Universal Exhaust Gas Oxygen,简称UEGO)传感器具有较宽的空燃比测量范围,广泛地应用于稀燃发动机空燃比控制系统。UEGO传感器需要配套控制器才能正常工作,通过控制传感器的温度和泵电流,实现空燃比的测量。然而,UEGO传感器温度具有非线性,泵电流参数存在摄动,给其控制带来了困难。为了缩短冷启动时间,采用斜坡加热与模糊PI相结合的分段温度控制策略,以克服温度的非线性和冷启动的加热限制;采用基于OE模型的系统辨识法,建立不同工况下的泵电流模型,据此采用模糊PID的控制方法提高泵电流的控制精度与响应速度,克服泵电流参数不确定性的影响,从而提高传感器的动态性能。在空气环境和混合气配气平台上进行实验,结果表明：UEGO传感器冷启动时间少于20s,温度控制精度较高,泵电流调节时间为191ms,动态响应速度快、抗干扰能力强。