基于神经网络多电机偏差耦合同步控制研究

针对多电机同步驱动系统,以四台电机为控制对象,基于智能控制技术,并结合多电机同步控制系统的非线性、时变、容易受负载扰动等特性,设计了神经网络PID控制器,提出了基于神经网络PID控制器与偏差耦合控制结构相结合的多电机同步控制策略;采用Matlab/Simulink构建系统仿真平台,对该控制策略进行仿真,结果表明:该控制算法稳定性能高,鲁棒性能好,收敛速度快,能有效的克服外部扰动和参数变化带来的同步误差,相对于传统的同步控制方案能够更好的实现多电机的同步控制.     

基于现场总线的多电机同步控制

以实现印刷机组多电机同步控制为目标,设计了一个基于现场总线PROFIBUS．DP的多电机同步控制系统。在PLC主站中采用BP神经网络算法,实现多电机转速的智能分配,从而达到同步控制,在专门设计的智能从站中运用双模自适应模糊PID,对单个电机的速度控制很精确。     

舞台调速吊杆群同步控制策略研究与仿真

提出了基于内模与模糊PID混合的多电机同步控制策略。将内模控制器引入速度并联同步控制系统,将其取代常规PI速度跟踪控制器,以其较强的鲁棒性来抑制系统参数摄动及负载扰动等不确定因素造成的不同步现象;同时结合现代智能控制技术,将专家的控制经验及推理过程融入系统当中,设计了模糊PID同步补偿器,从而实现了多电机的高精度同步控制。     

改进型相邻耦合误差的多电机同步控制策略

针对目前多电机同步控制策略的发展现状,本文提出了一种基于改进型相邻耦合误差的同步控制算法,其中任意轴的控制函数不仅包含该轴的跟踪误差,同时与其相邻的两轴的同步误差及其积分量有关,该控制策略不仅能够保证多电机传动系统同步性能,而且比传统相邻耦合控制结构控制器减少了1/3.仿真结果表明,该控制策略具有更高的同步精度和更好的动态性能.     

基于GGAP-RBF神经网络逆的复杂多电机系统同步控制

针对以矢量控制工作方式的复杂多电机同步系统,以3台电机同步系统为研究对象,证明了该系统可逆,提出了基于增长和修剪的RBF(GGAP-RBF)神经网络逆的多电机同步控制方法.将RBF神经网络逆串接在三电机系统之前,组成由速度和张力子系统组成的伪线性复合系统,分别对速度和张力子系统设计闭环控制器,实现了对速度和张力的解耦控...     

基于BP神经网络的多升降台同步控制研究

大型分布式升降台控制系统中,其同步控制是最关键的问题之一;针对传统神经网络PID控制器在多升降台同步控制的结构复杂及同步时间长等问题,提出一种基于相邻偏差耦合控制结构的BP神经网络PID同步控制策略,在确定同步误差定义的基础上,建立异步电机的矢量控制模型,改进了BP神经网络同步控制器;对四升降台同步控制系统的仿真实验表明:所研究的控制策略同步误差小,收敛速度快,实用性强。     

隔膜泵定转角差同步控制的电气实现

为解决多台隔膜泵向同一条管道输送料浆时产生的流量峰值叠加问题,在同一时刻,隔膜泵曲轴之间必须保持恒定的转角差,从而实现料浆的平稳输送。为此,针对机械解决方案的局限性,本文给出了隔膜泵定转角差同步控制的电气解决方案,并采用模糊PID控制算法设计多电机协调运行的同步控制器,成功实现了多个电机的良好同步控制。     

基于虚拟仪器的同步伺服系统PID模糊控制器设计

双电机直流同步伺服系统是颤振激励系统的驱动单元,控制器作为伺服系统的核心,对整个系统性能的优劣起着非常重要的作用.结合LabVIEW强大的数据采集功能及其PID和Fuzzy logic两个工具箱,基于模糊逻辑推理、分段线性化、同步控制算法开发出一个模糊PID控制器,较好地实现了同步伺服系统的速度跟随、速度同步、位置同步、差动同步功能.     

基于DSP的卷绕同步控制系统的研发

本文介绍了卷绕同步控制的工作原理,设计了卷绕同步控制系统.在系统设计中采用了交流电机和多级独立电机同步驱动策略,系统采用DSP实现了交流电机的转子磁场定向SVPWM矢量控制变频控制器和多级独立同步控制的硬件和软件设计.系统中独立电机间采用了CAN总线实现同步信号的通讯,本文对系统控制器的硬件和软件设计、同步驱动策略进行了介绍.     

混合时滞非恒同模糊神经网络的同步控制

主要研究一类具有混合时滞的非恒同模糊神经网络的同步控制问题,基于滑模控制技术设计了一个线性反馈控制器,通过构造Lyapunov函数,对同步的充分条件进行了讨论,并给出了相应的理论证明.数值仿真结果证实了该控制器的有效性。     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

基于神经网络的多机械臂固定时间同步控制

针对动力学模型未知的多机械臂系统,提出了一种基于神经网络的固定时间终端滑模的位置同步控制器。首先结合相邻交叉耦合同步控制策略,设计固定时间终端滑模面与控制器,保证系统的跟踪误差与同步误差在固定时间内收敛,且收敛时间上界与初始状态无关。其次,设计RBF神经网络权值更新律估计系统多机械臂未知非线性动力学模型,该方法无需对系统模型参数的先验知识。利用Lyapunov函数证明系统的固定时间收敛性与稳定性。最后,仿真结果验证了所提方法的有效性。     

Pinning control of a generalized complex dynamical network model

This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single controller can pin the generalized complex network to a homogenous solution. Some previous synchronization results are generalized. In this paper, we first discuss how to pin an array of delayed neural networks to the synchronous solution by adding only one controller. Next, by using the Lyapunov functional method, some sufficient conditions are derived for the local and global synchronization of the coupled systems. The obtained results are expressed in terms of LMIs, which can be efficiently checked by the Matlab LMI toolbox. Finally, an example is given to illustrate the theoretical results.     

Exponential synchronization of Cohen-Grossberg neural networks via periodically intermittent control

In this paper, a class of Cohen-Grossberg neural networks with time-varying delays are studied by designing a periodically intermittent controller. Some novel and effective exponential synchronization criteria are derived by applying some analysis techniques. These results generalize a few previous known results and remove some restrictions on control width and time-delays. Finally, a chaotic Cohen-Grossberg neural network is represented to show the effectiveness and feasibility of our results.     

Delay‐dependent criteria for general decay synchronization of discontinuous fuzzy neutral‐type neural networks with time‐varying delays

This paper aims to investigate the general decay synchronization analysis of discontinuous fuzzy neutral‐type neural networks. Under the framework of Filippov solutions, based on functional differential inclusions theory, inequality technique and by constructing a modified delay‐dependent Lyapunov‐Krasovskii functional, some new delay‐dependent criteria are provided to guarantee the general decay synchronization via the effective nonlinear feedback controller. The neural network model considered is more generalized, some previous fuzzy neural networks can be regard as the special cases. Moreover, some recent results on the delay‐independent criteria ensuring the general decay synchronization can also be extended and concluded. These can be seen from the given corollaries and remarks which are provided to make the comparisons and show the advantages. Finally, some numerical examples and simulations are provided to illustrate the correctness.     

模糊神经网络pH控制器的DSP实现

针对pH值控制过程具有较强非线性、纯滞后性的特点，传统PID控制往往达不到满意控制效果。介绍一种将模糊控制技术与神经网络技术相结合构成的模糊神经网络pH控制器，通过数字仿真显示了该控制算法的控制效果优于传统的PID控制和一般的模糊控制算法。并将提出的模糊神经网络控制算法在DSP上进行了实现．通过模拟实验验证了该控制器的可行性。     

三级倒立摆的加权变量模糊神经网络控制

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani型摸糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比,表明该Sugeno型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。     

基于自适应模糊神经网络控制器的网络控制系统

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。     

Synchronization of two different chaotic systems using Legendre polynomials with applications in secure communications

In this study, a new controller for chaos synchronization is proposed. It consists of a state feedback controller and a robust control term using Legendre polynomials to compensate for uncertainties. The truncation error is also considered. Due to the orthogonal functions theorem, Legendre polynomials can approximate nonlinear functions with arbitrarily small approximation errors. As a result, they can replace fuzzy systems and neural networks to estimate and compensate for uncertainties in control systems. Legendre polynomials have fewer tuning parameters than fuzzy systems and neural networks. Thus, their tuning process is simpler. Similar to the parameters of fuzzy systems, Legendre coefficients are estimated online using the adaptation rule obtained from the stability analysis. It is assumed that the master and slave systems are the Lorenz and Chen chaotic systems, respectively. In secure communication systems, observer-based synchronization is required since only one state variable of the master system is sent through the channel. The use of observer-based synchronization to obtain other state variables is discussed. Simulation results reveal the effectiveness of the proposed approach. A comparison with a fuzzy sliding mode controller shows that the proposed controller provides a superior transient response. The problem of secure communications is explained and the controller performance in secure communications is examined.     

Design and application of a sliding mode controller for accurate motion synchronization of dual servo systems

This paper presents a continuous time sliding mode controller (SMC) design to deal with the problem of motion synchronization in dual spindle servo systems. Synchronization error is defined as the differential position error between the two servo drives that follow identical reference motion trajectory. Proposed SMC controller penalizes three error states; namely individual axis tracking errors and the synchronization error for accurate synchronization. The control law is derived from Lyapunov energy function without switching condition. The controller shows robust motion synchronization against disturbances and parameter variations. Proposed SMC control is implemented in conventional double-sided machining operation.