共查询到19条相似文献,搜索用时 500 毫秒
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神经网络在弹性连杆机械振动主动控制中的应用 总被引:1,自引:0,他引:1
首次将神经网络理论应用于弹性连杆机械的振动主动控制,设计、建造了具有压电陶瓷作动器与电阻就变计传感器的弹性连杆机构实验装置及其振动控制系统。根据实验数据离线设计了动态递归神经网络控制器,并采用基于神经网络的直接自校正控制策略对弹性连杆机构实施了在线控制。控制后,弹性构件输出点的应变峰值降低了50%左右,机械的动力学品质得到显著改善。 相似文献
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弹性连杆机构残余振动主动控制的研究 总被引:6,自引:2,他引:6
采用压电陶瓷作为作动器,应变片作为传感器,基于修正的独立模态空间控制理论,对弹性连杆机构残余振动的控制进行了研究。本文首先建立了由作动器和机构原始单元组成的智能单元、传感器和被控系统的数学模型,根据被控系统的特点设计了具有良好稳定性的控制器。给出了智能单元控制电压的计算方法,结合算例讨论了本文方法的有效性 相似文献
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基于神经网络的弹性对象压力控制方法,研究出动态递归神经网络间接自适应控制器的结构和数学模型,利用实验数据离线设计了神经网络辨识器与神经网络控制器,并应用到出口自行车手闸和脚闸性能测试的静负荷实现的控制中.实用结果证明了该方法的有效性. 相似文献
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《振动工程学报》2019,(4)
针对一类单自由度含间隙和预紧弹簧的弹性碰撞振动系统的分岔控制问题,提出了一种基于Lyapunov指数及径向基函数神经网络的分岔预测及控制方法。首先建立了系统的Poincaré映射,推导了弹性碰撞振动系统周期运动存在的条件,研究了在主要分岔参数平面中的动力学分布;其次利用Lyapunov指数分析了系统的稳定性,提出利用追踪Lyapunov指数谱分岔点来预测周期倍化分岔发生的方法;最后基于径向基函数神经网络设计了参数反馈分岔控制器、基于周期倍化分岔点处的最大Lyapunov指数构造适应度函数,并利用Lyapunov指数判断是否实现了分岔控制,以引导自适应混合引力搜索算法对控制器的参数进行优选,从而实现周期倍化分岔控制。 相似文献
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基于磁流变阻尼器的车辆悬架半主动控制研究--间接自适应控制与实验 总被引:10,自引:1,他引:9
在分析磁流变阻尼器车辆悬架非线性特性的基础上,设计了一类神经网络间接自适应控制器,并根据系统的低频特性和作动器的快响应,实现了悬架振动的神经网络实时控制。计算机仿真和悬架实验的结果均表明,神经模拟器能够逼近非线性系统,神经控制器能在时域和频哉内以较高的精度控制悬架系统的振动。 相似文献
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由于高速柔性并联机构系统的非线性和不确定性,提出一种鲁棒模型预测振动控制策略以抑制系统的振动响应。以压电陶瓷为作动器,电阻应变片为传感器,采用有限元方法和模态截断技术建立机构不精确动力学模型。机构动力学模型中的非线性因素、耦合因素及系统高阶模态影响作为扰动,将模态力视为不确定扰动,并且考虑输出噪声对系统的影响,建立系统动态响应的预测模型,以预测输出值。采用Kalman滤波估计器估计系统状态量,以控制电压及其变化率为约束条件,将系统性能指标和约束条件化为一个标准二次规划优化问题,通过求解这一优化问题来得到最优控制输出,形成滚动优化控制输出来抑制系统振动响应。采用表征作动能量的可控性指标和表征观测信号能量的可观性指标,确定作动器和传感器的最优位置。以新型2自由度并联机构为实例,采用实验模态方法得到系统的前2阶固有频率和阻尼比,与有限元方法得到的结果比较分析表明理论模型不精确。基于该模型采用dSPACE实时仿真系统和MATLAB/Simulink搭建鲁棒控制系统,进行振动主动控制试验研究。试验结果表明,所设计的控制器能有效地抑制柔性构件产生的弹性振动,验证了控制器的有效性和鲁棒性。 相似文献
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Visakha K. Nanayakkara Yasuyuki Ikegami Haruo Uehara 《International Journal of Refrigeration》2002,25(6)
This paper presents a novel neural network (NN) to control an ammonia refrigerant evaporator. Inspired by the latest findings on the biological neuron, a dynamic synaptic unit (DSU) is proposed to enhance the information processing capacity of artificial neurons. Treating the dynamic synaptic activity after the nonlinear somatic activity helps to capture the dynamics demarcated by the Gaussian activation pertaining to the input space. This practice leads to a remarkable reduction in curse of dimensionality. The proposed NN architecture has been compared with two other conventional architectures; one with dynamic neural units (DNUs) and the other with nonlinear static functions as perceptrons. The objective is to control evaporator heat flow rate and secondary fluid outlet temperature while keeping the degree of refrigerant superheat in the range 4–7 K at the evaporator outlet by manipulating refrigerant and evaporator secondary fluid flow rates. The drawbacks of conventional approaches to this problem are discussed, and how the novel method can overcome them are presented. An evolutionary approach is adopted to optimize the parameters of the NN controllers. Then evaporator process model is accomplished as a combination of governing equations and a sub NN resulting in a simple and sufficiently accurate model. The effectiveness of the proposed dynamic NN controller for the evaporator system model is validated using experimental data from the ammonia refrigeration plant. 相似文献
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Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection 总被引:2,自引:0,他引:2
This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme. 相似文献
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This paper investigates the feasibility of using artificial neural networks (NNs) to predict the shear capacity of concrete members reinforced longitudinally with fibre reinforced polymer (FRP) bars, and without any shear reinforcement. An experimental database of 138 test specimens failed in shear is created and used to train and test NNs as well as to assess the accuracy of three existing shear design methods. The created NN predicted to a high level of accuracy the shear capacity of FRP reinforced concrete members.Garson index was employed to identify the relative importance of the influencing parameters on the shear capacity based on the trained NNs weightings. A parametric analysis was also conducted using the trained NN to establish the trend of the main influencing variables on the shear capacity. Many of the assumptions made by the shear design methods are predicted by the NN developed; however, few are inconsistent with the NN predictions. 相似文献
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空间站大柔性太阳电池翼驱动装置的滑模伺服控制 总被引:1,自引:0,他引:1
针对空间站大柔性太阳电池翼高稳定对日跟踪驱动控制问题,提出了一种带运动规划和振动抑制的非线性快速终端滑模伺服控制方案。推导了太阳电池翼驱动状态方程、永磁同步电机动力学模型,同时考虑驱动装置传动间隙、静/动摩擦力矩切换等非线性传动特性,在动力学建模基础上设计高次样条运动规划、位置环积分分离调节器、速度环快速终端滑模变结构调节器的组合控制系统。通过对太阳电池翼对日跟踪过程的仿真校验,表明设计的控制方案可实现大惯量、超低频太阳电池翼较高的驱动速度稳定度和较好的跟踪精度。 相似文献
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A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN model, simulations were carried out to obtain the trends in element degradation with varying pH values, and the results showed correct trends. The preliminary results obtained demonstrate that through a comprehensive study, a better corrosion resistant material can be designed by controlling the degradation of the chemical elements during the corrosion process through neural network methods. 相似文献