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在分析永磁同步电动机(PMSM)数学模型的基础上,提出了一种基于免疫遗传算法(IGA)的递归模糊神经网络(RFNN)控制器的设计方法,并应用于永磁同步电动机双闭环矢量控制系统中的转速控制器中,对永磁同步电动机实现精确的速度控制.在与传统PI控制和递归模糊神经网络控制仿真比较中,采用该方法的系统显示出良好的控制性能和控制效果. 相似文献
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通过分析永磁同步电动机(PMSM)的数学模型,提出神经网络模糊控制器的设计方法,并应用于永磁同步电动机双闭环矢量控制系统中的转速控制器,用来精确实现永磁同步电动机的速度控制。仿真实验表明,该方法得到的各项性能指标均优于PI控制和递归模糊神经网络控制,具有很强的适应性和鲁棒性,取得了比较理想的控制效果,为实现永磁同步电动机的智能化调速控制提供了切实可行的技术方案。 相似文献
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基于定子电阻观测器的PMSM模糊直接转矩控制系统 总被引:1,自引:0,他引:1
定子电阻的变化以及传统的滞环控制器不可避免地会导致永磁同步电动机直接转矩控制系统产生较大的转矩和磁链脉动,从而影响系统的运行性能。为解决这一问题,可以在永磁同步电动机直接转矩控制系统中利用定子电阻观测器对定子电阻进行补偿,并且用模糊控制器代替传统的滞环控制器。最后的仿真结果表明:基于定子电阻观测器的永磁同步电动机模糊直接转矩控制系统不但具有较小的转矩、磁链脉动,而且在电动机参数发生变化或受到外部扰动的情况下,系统仍然具有快速响应性能。 相似文献
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在分析异步电动机数学模型的基础上。提出了一种基于免疫遗传算法(IGA)的递归模糊神经网络(RFNN)控制器的设计方法,并应用于交流异步电动机双闭环矢量控制系统中的转速控制器中,对异步电动机实现精确的速度控制。为满足控制运算实时性的要求。采用高速数字信号处理器(DSP)作为速度控制与运算单元,成功完成了递归模糊神经网络控制器的DSP实现。实验结果验证了该控制器的可行性。 相似文献
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永磁同步电动机模糊直接转矩控制系统的仿真 总被引:2,自引:0,他引:2
定子电阻的变化将直接影响永磁同步电动机直接转矩控制系统的运行性能,而传统的滞环控制器不可避免地会产生较大的转矩和磁链脉动。为解决这一问题,可以在永磁同步电动机直接转矩控制系统中利用定子电阻观测器对定子电阻进行补偿,并且用模糊控制器代替传统的滞环控制器。仿真实验结果表明,利用该方法建立的基于定子电阻观测器的永磁同步电动机模糊直接转矩控制系统不但具有较小的转矩、磁链脉动,而且在电机参数发生变化或受到外部扰动的情况下,系统仍然具有快速响应性能。 相似文献
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基于模型参考自适应模糊神经网络的直线永磁同步电动机速度伺服系统 总被引:2,自引:0,他引:2
针对直线永磁同步电动机全闭环控制系统易受干扰而降低系统性能指标,甚至造成不稳定等问题,采用模型参考自适应模糊神经网络在线辩识方法,用梯度法实时修正模糊控制器的输入和输出隶属度参数,建立了模型参考自适应模糊神经网络速度伺服系统模型,并给出了模糊神经网络控制器的设计。通过仿真和实验结果证明,这种方法提高了速度检测装置的分辨率和动态响应能力,并且使系统具有很强的鲁棒性。 相似文献
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Application of fuzzy neural networks and artificial intelligence for load forecasting 总被引:13,自引:0,他引:13
An integrated evolving fuzzy neural network and simulated annealing (AIFNN) for load forecasting method is presented in this paper. First we used fuzzy hyper-rectangular composite neural networks (FHRCNNs) for the initial load forecasting. Then we used evolutionary programming (EP) and simulated annealing (SA) to find the optimal solution of the parameters of FHRCNNs (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). We knew that the EP has a good capability for searching for globe optimal value, but a poor capability for searching for the local optimal value. And, the SA only had a good capability for searching for a local optimal value. Therefore, we combined both methods to obtain both advantages, and so improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum. Finally, we use the AIFNN to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed AIFNN load forecasting scheme was tested using data obtained from a sample study including 1 year, 1 month and 24 h time periods. The result demonstrated the accuracy of the proposed load forecasting scheme. 相似文献
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Gwo-Ching Liao 《Electrical Engineering (Archiv fur Elektrotechnik)》2006,88(3):165-176
A hybrid chaos search genetic algorithm (CGA) /fuzzy system (FS), simulated annealing (SA) and neural fuzzy network (NFN) method for load forecasting is presented in this paper. A fuzzy hyper-rectangular composite neural networks (FHRCNNs) was used for the initial load forecasting. Then, we used CGAFS and SA to find the optimal solution of the parameters of the FHRCNNs, instead of back-propagation (BP) (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). First, the CGAFS generates a set of feasible solution parameters and then puts the solution into the SA. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The SA method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional artificial neural networks (ANN) training by BP (where the weights and biases are always trapped into a local optimum, which then leads the solution to sub-optimization). Finally, we used the CGAFS and SA combined with NFN (CGAFSSA–NFN) to see if we could improve the quality of the solution, and if we actually could reduce the error of load forecasting. The proposed CGAFSSA–NFN load forecasting scheme was tested using the data obtained from a sample study, including 1 year, 1 week and 24-h time periods. The proposed scheme was then compared with ANN, evolutionary programming combined with ANN (EP–ANN), genetic algorithm combined with ANN (GA–ANN), and CGAFSSA–NFN. The results demonstrated the accuracy of the proposed load-forecasting scheme. 相似文献
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模糊CMAC神经网络控制系统及混合学习算法 总被引:3,自引:0,他引:3
针对CMAC神经网络和模糊控制的特性,给出了一种能反映人脑认知的模糊性和连续性的模糊CMAC神经网络控制器,该控制器采用高斯函数作为模糊隶属函数,利用神经网络实现模糊推理并可对隶属函数进行实时调整,从而使其具有学习和自适应能力。讨论了这种控制器的混合学习算法,即先采用混沌算法离线优化,再采用BP梯度算法在线调整,并推导了变形Elmam网络的系统辨识算法。对电厂锅炉主蒸汽温度控制的仿真结果表明了此控制器及其学习算法的可行性和有效性。 相似文献
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This article presents a reference adaptive Hermite fuzzy neural network controller for a synchronous reluctance motor. Although synchronous reluctance motors are mathematically and structurally simple, they perform poorly under dynamic modes of operation because certain parameters, such as the external load and non-linear friction, are difficult to control. The proposed adaptive Hermite fuzzy neural network controller overcomes this problem, as using the Hermite function instead of the conventional Gaussian function shortens the training time. Furthermore, the proposed adaptive Hermite fuzzy neural network controller uses an online self-tuning fuzzy neural network to estimate the system's lumped uncertainty. The estimation method involves a fuzzy controller with expert knowledge of the initial weight of the neural network. Finally, the Lyapunov stability theory and adaptive update law were applied to guarantee system convergence. In this article, the responsiveness of the adaptive Hermite fuzzy neural network controller and an adaptive reference sliding-mode controller is compared. The experimental results show that the adaptive Hermite fuzzy neural network controller markedly improved the system's lumped uncertainty and external load response. 相似文献