共查询到17条相似文献,搜索用时 187 毫秒
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基于系统辨识的燃料电池系统建模和自适应模糊控制 总被引:1,自引:0,他引:1
熔融碳酸盐燃料电池(MCFC)发电运行时,电堆的工作温度必须控制在一定的范
围内,否则将导致系统发电效率的降低或危及电堆寿命.因此,实现对MCFC运行温度的在线
控制势在必行.但由于MCFC系统的复杂性,已有模型均为复杂的非线性微分方程组描述的解
析模型,难以满足在线计算的实时控制的要求.因此,本文首先利用神经网络辨识技术基于
实验的输入(气体流量)输出(温度)数据建立起MCFC电堆的神经网络模型;然后,基于这
一电堆模型,设计了一个MCFC电堆工作温度的在线改进型自适应模糊控制器.该控制器对传
统的模糊控制方法存在的缺陷进行了改进,它一方面采用BP算法对模糊系统的参数进行修正
,另一方面又通过聚类算法对模糊系统的结构进行自适应调整.最后,用神经网络辨识模型
代替实际的MCFC电堆进行了控制仿真,仿真结果证明对MCFC辨识电堆建模的有效性,以及所
设计的模糊控制器的性能优越性. 相似文献
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基于一种改进的BP神经网络光伏电池建模 总被引:1,自引:1,他引:0
由于光伏电池具有高度非线性特性,难以建模,而传统的数学模型难以满足光伏控制系统设计和应用的要求。该文利用神经网络具有逼近任意复杂非线性函数的能力,将神经网络技术应用到光伏阵的建模中,避开了该模块内部的复杂性。模型以太阳能日照、温度以及负载电压作为神经网络辨识模型的输入量,光伏阵输出电流为输出量,采用改进型BP算法,建立了光伏电池的动态响应模型,然后预测了最大功率点。文中给出模型的结构,训练步骤和仿真结果。仿真结果表明,方法可行,建立的模型精度较高,从而为设计光伏实时控制系统奠定了基础。 相似文献
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基于神经网络建立三元复合驱系统岩电关系的数学模型,神经网络的辨识采用Levenberg-Marquardt(LM)方法。这种方法具有很快的收敛速度和良好的敛精度,适用于非线性系统的建模与辨识。 相似文献
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木材干燥是一个复杂的非线性系统,因此利用传统的系统辨识方法难以建立其准确的模型。本文利用动态递归神经网络的特点,提出了基于动态递归神经网络的木材干燥模型辨识方法,仿真结果表明,利用动态递归神经网络所建立的模型是有效的。 相似文献
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为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器+前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。 相似文献
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《Simulation Modelling Practice and Theory》2002,10(1-2):109-119
Modeling molten carbonate fuel cells (MCFC) is very difficult and the most existing models are based on conversation laws which are too complicated to be used to design a control system. This paper presents an application of radial basis functions (RBF) neural networks identification to develop a nonlinear temperature model of MCFC stack. The temperature characters of MCFC stack are briefly analyzed. A summary of RBF neural networks modeling of MCFC is introduced. The simulation tests reveal that it is feasible to establish the model of MCFC stack using RBF neural networks identification. The modeling process avoids using complicated differential equations to describe the stack and the neural networks model developed can be used to predict the temperature responses online which makes it possible to design online controller of MCFC stack. 相似文献
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Nonlinear modeling and adaptive fuzzy control of MCFC stack 总被引:8,自引:0,他引:8
To improve availability and performance of fuel cells, the operating temperature of molten carbonate fuel cells (MCFC) stack should be controlled within a specified range. However, the most existing models of MCFC are not ready to be applied in synthesis. In this paper, a radial basis function neural networks identification model of MCFC stack is developed based on the input–output sampled data. A novel adaptive fuzzy control procedure for the temperature of MCFC stack is also developed. The parameters of the fuzzy control system are regulated by back-propagation algorithm, and the rule database of the fuzzy system is also adaptively adjusted by the nearest-neighbor-clustering algorithm. Finally using the neural networks model of MCFC stack, the simulation results of the control algorithm are presented. The results show the effectiveness of the proposed modeling and design procedures for MCFC stack based on neural networks identification and the novel adaptive fuzzy control. 相似文献
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本文研究神经网络在光伏电池建模优化问题。由于光伏电池具有高度非线性特性,其输出功率受到外界自然因素的影响,使得传统方法不能满足光伏控制系统动态要求。针对上述问题,本文提出一种粒子群优化的神经网络光伏电池建模算法。改进的方法以日照、温度和负载电压作为提出的RBF神经网络模型的输入值,把光伏电池的输出功率作为神经网络的输出,采用RBF神经网络对光伏电池进行建模,同时利用粒子群算法对神经网络参数进行优化,最后建立光伏电池的动态响应模型。仿真实验结果证明,所提模型更好地克服传统方法的缺点,收敛速度快,具有较高的预测精度和适合能力。 相似文献
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This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper. 相似文献
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Jaho Seo Amir Khajepour Jan P. Huissoon 《International Journal of Control, Automation and Systems》2014,12(4):794-804
The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times. 相似文献
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针对时滞系统、应用神经网络的非线性逼近能力,采用神经网络实现内模控制中被控对象的正模型及内模控制器,用Lyapunov稳定性定理证明神经网络控制系统的稳定性。仿真结果说明神经网络内模控制方案的优越性。 相似文献
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典型人工神经网络的结构、功能及其在智能系统中的应用 总被引:14,自引:1,他引:13
人工神经网络已在各个领域得到广泛的应用,
尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优
化计算等方面更是得到人们的极大关注.本文从网络在智能系统中建模及控制器设计的具体
训练结构入手,详细介绍了BP网络在系统控制中的典型应用方式,并根据不同网络所具有的
功能,从性能对比的角度对人工神经网络在上述各方面的应用给予综述. 相似文献