共查询到19条相似文献,搜索用时 203 毫秒
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磁悬浮球系统是一个典型的非线性的不稳定的系统,基于对其建模的复杂性和不准确性,文章利用神经网络能逼近任意非线性函数这一特性,对磁悬浮球系统进行辨识;再根据滑模变结构控制原理设计了磁悬浮球系统的变结构控制器,利用MATLAB对系统进行建模仿真,仿真结果表明,RBF网络能很好地逼近本磁悬浮球系统;滑模变结构控制对于此非线性系统有较好的控制效果,小球能很快地悬浮在平衡位置,该控制系统具有较好的稳态特性和抗干扰性. 相似文献
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基于Elman网络的非线性系统神经元自适应预测控制 总被引:5,自引:0,他引:5
提出在非线性系统的E1man网络辨识模型的基础上,用单神经元设计预测控制器的方案。Elman网络在BP网络的基础上,加入反馈信号,利用内部状态反馈来描述系统的非线性动力学行为,提高了学习速度,适合于动态系统的实时辨识。神经元结构简单,且有很强的自学习和自适应能力,它根据系统的期望输出与一步超前预测输出之间的偏差,并通过某种特定的学习算法在线调整控制器的参数,使控制器能够适应对象参数的变化,从而实现对一类非线性系统的有效控制。仿真实验证明了该方案的有效性。 相似文献
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神经网络在细纱机中的应用 总被引:1,自引:0,他引:1
介绍了一种基于PLC和DSP的细纱杌控制系统.该系统针对细纱机控制系统的非线性与传统PID控制方法的不足,提出了一种改进型基于RBF神经网络在线辨识的单神经元PID自适应控制方法.该方法构造了一个RBF网络对系统进行在线辨识,建立起在线参考模型,由单神经元控制器完成控制器参数的学习,从而实现控制器参数的在线调整.仿真试验结果表明.该控制器控制精度高,动态性能好,其控制效果优于传统的PID控制器. 相似文献
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磁悬浮系统的HOPF分岔自适应控制研究 总被引:2,自引:1,他引:1
磁悬浮固有系统是非线性的,也是本质不稳定的,其稳定性设计比较复杂,特别是在受到较大干扰和对象参数发生较大变化时,系统容易失去稳定并发散.理论分析与试验表明,这种现象的数学解释就是系统出现了HOPF分岔.为此,本文提出了一种用HOPF分岔规律调整非线性系统PID控制器参数的自适应设计方法,通过辨识干扰或者对象参数的变化,自动调整控制参数,使闭环系统远离HOPF分岔点,从而继续保持稳定.以悬浮质量突变为例的仿真表明,由此整定悬浮控制比例增益参数,可使磁悬浮系统获得较大的状态稳定范围,并有效回避自激振动. 相似文献
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设计了一种基于嵌入式系统的神经网络PID控制器,以ARM芯片为控制器核心,实现对难以建立精确数学模型的非线性系统的自适应控制;控制器采用RBF神经网络对被控对象进行在线辨识,并根据辨识结果对控制器的参数进行在线修正,实现PID控制器的自适应;该控制器体积小、适应能力强且省电;实验结果表明,该控制器可靠性高,响应快,可以在无法确定被控系统数学模型的情况下达到理想的控制效果. 相似文献
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Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly. 相似文献
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双电磁铁悬浮系统的非线性解耦控制器设计 总被引:3,自引:0,他引:3
LIU De-Sheng LI Jie ZHANG Kun 《自动化学报》2006,32(3):321-328
Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling from the input to the output, the model is linearized exactly by means of feedback linearization, andan equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system. 相似文献
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Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online
identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally
efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling
air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained
offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using
active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model
simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives
satisfactory performance and also adapts to the change in engine systems very quickly. 相似文献
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Design of Nonlinear Decoupling Controller for Double-electromagnet Suspension System 总被引:1,自引:0,他引:1
Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling
from the input to the output, the model is linearized exactly by means of feedback linearization, and an equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension
controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system. 相似文献
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A robust adaptive neural network controller is presented for flexible joint robots using feedback linearization techniques. The controller is based on an approach of using an additional neural network to provide adaptive enhancements to a bask fixed nonlinear controller which can be either neural-network-based or model-used. The weights of the additional neural network are updated on-line based on direct adaptive techniques. It is shown that if Gaussian radial basis function networks are used for the additional neural network, uniformly stable adaptation is assured and asymptotic tracking of the position reference signal is achieved. Intensive computer simulations on a two-link flexible joint robot have shown that the controller can belter handle dynamical model changes and parameter uncertainties than the conventional feedback linearization controller 相似文献
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常规主动刹车系统采用在线辨识跑道特征的算法,但仍需依赖摩擦模型先验知识,难以应对复杂跑道工况.为克服上述问题,提出一种滑模极值搜索控制策略并应用于无人机全电式自主刹车系统.考虑电动作动机构非线性特性,建立系统的状态空间模型并合理简化为严格反馈形式,采用超扭曲算法估计结合系数的梯度,结合反馈线性化控制律得到刹车压力参考值,证明此控制作用下可实现对未知最优滑移率的渐近跟踪.采用反演控制的思想设计无抖振滑模控制器实现对参考刹车压力的跟踪.利用Lyapunov方法获得系统的渐近稳定性条件并分析控制参数对系统的影响.半实物仿真试验结果表明控制策略的有效性. 相似文献
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磁悬浮列车的双环控制 总被引:7,自引:2,他引:7
为了增强控制系统对磁悬浮列车系统参数变化的适应性,将磁悬浮系统分为电流环和位置环进行控制.对于电流环系统,采用模型参考自适应控制方法进行控制,使得在电磁线圈参数变化的情况下,电流环的性能能够保持稳定,这样就将三阶非线性磁悬浮系统降阶为二阶系统;对于二阶系统,首先采用反馈线性化控制方法将系统线性化,然后采用PD控制算法进行控制,从而确保系统在不同工作点处的性能一致.仿真实验结果证明,该方法使磁悬浮列车的适应能力得到了明显的提高. 相似文献