热流计校准装置自适应温度控制方法研究

以热流计校准装置为研究对象,建立了热板温度仿真模型。根据模糊控制原理和BP神经网络原理,分别设计了模糊PID控制器和BP神经网络PID控制器,并在Matlab的Simulink下进行了不同控制方法的仿真对比实验。仿真结果表明:模糊PID有更快的响应速度、更短的稳定时间,BP神经网络PID有更小的超调量。     

时滞离散神经网络的同步控制

利用既有效又便于实施的时滞状态反馈控制器,根据所给定的条件构造相应的不等式,研究了带有时滞的离散神经网络模型的同步控制问题,给出了该离散系统指数同步的充分条件.在设计同步控制的时候,没有假设激励函数的有界性、可微性和单调性,给出的条件简便易实施.数值结果进一步证明了该控制方法的有效性. 关键词： 离散神经网络 时滞 同步     

电机温度时滞耦合系统自抗扰控制仿真研究

电机温度过高会造成绝缘性能老化,电机安全性能下降。电机控制系统是典型的非线性系统,电机温度也因此具有时滞性和耦合性的特点,难以建立准确的数学模型。传统的方法对电机温度的控制精度较差,从而导致电机温度失控。为此,提出基于BP神经网络自抗扰控制算法的电机时滞耦合关系下温度控制方法。将BP神经网络与PID控制方法相结合建立电机温度网络自抗扰控制器模型,利用梯度下降法修正电机温度控制器模型的权值系数,从而实现了BP神经网络自抗扰控制器参数的实时调整。实验结果表明,利用BP神经网络自抗扰算法进行电机时滞耦合关系下温度调整,能够有效提高控制的精确度,缩短了控制过程中的时间延时。     

基于BP神经网络模型时钟同步误差补偿算法

误差补偿是保证水下传感器网络时钟同步精度的一个重要保障,现有研究方法主要采用线性拟合和最小二乘法对时钟同步参数进行误差补偿,但该类方法并未考虑受海流影响时节点移动所导致的时钟同步精度问题.针对此问题,本文提出一种基于BP神经网络模型的时钟同步误差补偿算法.首先采用深海拉格朗日洋流模型描述水下节点运动规律,模拟水下节点运动速度,进而建立时钟同步参数模型,最后构建符合水下环境的BP神经网络时钟同步误差补偿模型,通过定义激励函数,引入正则项因子和补偿性因子避免模型过拟合,建立误差反向传播的BP神经网络模型时钟同步误差补偿算法.仿真实验表明,本文提出的算法与TSHL算法、MU-sync算法、MM-sync算法相比,在时钟同步精度(即时钟同步时间与标准时间的误差)上分别提升了37.42%, 17.29%和21.86%,并且均方误差得到显著降低.     

基于模糊神经网络的自适应PID控制器

提出了一种应用于自适应PID控制器的神经网络与模糊控制相结合的算法,该算法可以有效地解决普通PID控制器依赖于对象的数学模型的缺点,可实现控制系统的在线自适应调整,可满足实时控制的要求。仿真结果表明,基于模糊神经网络整定的PID控制器具有较好的自学习和自适应性,具有较快的响应速度。     

BP神经网络PID控制算法在农作物干燥控制系统中的应用研究与设计

针对传统PID控制算法对于农作物烘干控制方法存在的不足,以实现对农作物高效、节能的干燥为目的,设计了一种新型农作物干燥控制系统。系统采用DS18B20、SHT10为信息采集源,将采集到的温湿度信息传递到以C8051F340单品机为核心的控制器进行整个干燥过程的控制决策。系统应用了BP神经网络PID控制算法调节温湿度。     

基于模糊PID的多类型轴同步运动控制技术研究

在大型复杂设备中,存在着大量多轴联动或同步运动驱动机构。为提高多轴同步运动精度,对常用的同步控制模型进行了研究和分析,提出了以模糊PID控制算法为核心的主-从式与耦合式相结合的控制模型,并开展了双电机轴系统、双液压轴系统以及混合轴系统的建模仿真与试验分析。结果表明该控制方法不仅能够减小多轴同步系统的同步运动误差,还能有效减小外部干扰的影响。     

单动薄板冲压液压机的数控技术

介绍了数控单动薄板冲压液压机的计算机控制系统，说明了该控制系统所实现的各项功能，讨论了系统的硬件结构和软件功能，着重讨论了将带有同步误差反馈，并在同步误差调节中加入积分实现了高精度的同步控制的原理和方法，指出了计算机控制技术在单动薄板冲压机中的应用。     

基于PLC的三维调整机同步控制系统设计与实现

针对三维调整机难以实现多台同步工作，设计了基于PLC的同步控制方案。在同步控制系统方案中，采用西门子PLC通过工业无线以太网进行同步控制，由各维度油缸的位移传感器来保证同步控制的精确性。经实验，证实了此系统的精度满足设计要求，实现了同步控制的效果。     

基于改进LMBP神经网络的地源热泵空调控制研究

针对BP神经网络训练速度慢并且容易陷入局部最小点的缺点,研究了LM优化算法;为解决LM算法中求解逆矩阵这一最耗时因素的问题,提出将共轭斜量法与LMBP算法结合起来,并通过Matlab编程语言实现,并将其应用到地源热泵冷冻水控制回路中;通过仿真对比,结果表明这种通过算法改进的控制器比PID控制器调节时间快约10 s、超调量小0.4,稳态误差小,具有明显的控制效果。     

Synchronization of impulsively coupled complex systems with delay

This paper investigates the synchronization of complex systems with delay that are impulsively coupled at discrete instants only. Based on the comparison theorem of impulsive differential system, a distributed impulsive control scheme is proposed to achieve the synchronization for systems with delay. In the control strategy, the influence of all nodes to network synchronization relies on its weight. The proposed control scheme is applied to the chaotic delayed Hopfield neural networks and numerical simulations are presented to demonstrate the effectiveness of the proposed scheme.     

Synchronization Scheme for Uncertain Chaotic Systems via RBF Neural Network

A sliding mode adaptive synchronization controller is presented with a neural network of radial basis function （RBF） for two chaotic systems. The uncertainty of the synchronization error system is approximated by the RBF neural network. The synchronization controller is given based on the output of the RBF neural network. The proposed controller can make the synchronization error convergent to zero in 5s and can overcome disruption of the uncertainty of the system and the exterior disturbance. Finally, an example is given to illustrate the effectiveness of the proposed synchronization control method.     

Synchronization of different chaotic systems via active radial basis functions sliding mode controller

This paper presents a new method to synchronize different chaotic systems with disturbances via an active radial basis function （RBF） sliding controller. This method incorporates the advantages of active control, neural network and sliding mode control. The main part of the controller is given based on the output of the RBF neural networks and the weights of these single layer networks are tuned on-line based on the sliding mode reaching law. Only several radial basis functions are required for this controller which takes the sliding mode variable as the only input. The proposed controller can make the synchronization error converge to zero quickly and can overcome external disturbances. Analysis of the stability for the controller is carried out based on the Lyapunov stability theorem. Finally, five examples are given to illustrate the robustness and effectiveness of the proposed synchronization control strategy.     

Phase synchronization in two coupled chaotic neurons

Chaotically-spiking dynamics of Hindmarsh–Rose neurons are discussed based on a flexible definition of phase for chaotic flow. The phase synchronization of two coupled chaotic neurons is in fact the spike synchronization. As a multiple time-scale model, the coupled HR neurons have quite different behaviors from the Rössler oscillators only having single time-scale mechanism. Using such a multiple time-scale model, the phase function can detect synchronization dynamics that cannot be distinguished by cross-correlation. Moreover, simulation results show that the Lyapunov exponents cannot be used as a definite criterion for the occurrence of chaotic phase synchronization for such a system. Evaluation of the phase function shows its utility in analyzing nonlinear neural systems.     

Adaptive synchronization of a class of fractional-order complex-valued chaotic neural network with time-delay

This paper is concerned with the adaptive synchronization of fractional-order complex-valued chaotic neural networks (FOCVCNNs) with time-delay. The chaotic behaviors of a class of fractional-order complex-valued neural network are investigated. Meanwhile, based on the complex-valued inequalities of fractional-order derivatives and the stability theory of fractional-order complex-valued systems, a new adaptive controller and new complex-valued update laws are proposed to construct a synchronization control model for fractional-order complex-valued chaotic neural networks. Finally, the numerical simulation results are presented to illustrate the effectiveness of the developed synchronization scheme.     

Robust impulsive synchronization of complex delayed dynamical networks

This Letter investigates robust impulsive synchronization of complex delayed dynamical networks with nonsymmetrical coupling from the view of dynamics and control. Based on impulsive control theory on delayed dynamical systems, some simple yet generic criteria for robust impulsive synchronization are established. It is shown that these criteria can provide a novel and effective control approach to synchronize an arbitrary given delayed dynamical network to a desired synchronization state. Comparing with existing results, the advantage of the control scheme is that synchronization state can be selected as a weighted average of all the states in the network for the purpose of practical control strategy. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control methodology.     

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov–Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.     

Finite-time complex projective synchronization of fractional-order complex-valued uncertain multi-link network and its image encryption application

Multi-link networks are universal in the real world such as relationship networks, transportation networks, and communication networks. It is significant to investigate the synchronization of the network with multi-link. In this paper, considering the complex network with uncertain parameters, new adaptive controller and update laws are proposed to ensure that complex-valued multilink network realizes finite-time complex projective synchronization (FTCPS). In addition, based on fractional-order Lyapunov functional method and finite-time stability theory, the criteria of FTCPS are derived and synchronization time is given which is associated with fractional order and control parameters. Meanwhile, numerical example is given to verify the validity of proposed finite-time complex projection strategy and analyze the relationship between synchronization time and fractional order and control parameters. Finally, the network is applied to image encryption, and the security analysis is carried out to verify the correctness of this method.     

不确定分数阶时滞混沌系统自适应神经网络同步控制

针对带有完全未知的非线性不确定项和外界扰动的异结构分数阶时滞混沌系统的同步问题,基于Lyapunov稳定性理论,设计了自适应径向基函数(radial basis function,RBF)神经网络控制器以及整数阶的参数自适应律.该控制器结合了RBF神经网络和自适应控制技术,RBF神经网络用来逼近未知非线性函数,自适应律用于调整控制器中相应的参数.构造平方Lyapunov函数进行稳定性分析,基于Barbalat引理证明了同步误差渐近趋于零.数值仿真结果表明了该控制器的有效性.     

迟滞混沌神经元/网络的控制策略及应用研究

为了保持神经网络在优化计算求解过程中结构不被改变, 以迟滞混沌神经元和迟滞混沌神经网络为研究对象, 提出了一种基于滤波跟踪误差的控制策略来实现神经元/网络的稳定控制. 采用该控制策略, 在不改变非线性特性发生机理的情况下, 神经元/网络可实现函数优化计算问题的求解. 所设计的控制律包含两部分: 一部分是系统进入滤波跟踪误差面时的等效控制部分, 另一部分为确保系统快速进入滤波跟踪误差面的控制部分. 采用Lyapunov方法对神经元/网络的控制进行了稳定性证明. 根据待寻优函数直接求得神经元的控制律, 在该控制律的作用下, 神经元/网络可逐渐稳定到优化函数的极值点, 从而实现优化问题的求解, 仿真实验结果验证了该控制方法在优化计算中的可行性和有效性.