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超机动飞机的动态建模与控制律设计及仿真 总被引:1,自引:0,他引:1
建立了带推力矢量的超机动飞机非线性动态模型,重点分析了气动力、气动力矩以及发动机的建模过程.采用基于神经网络的自适应逆方法,设计了超机动飞机大迎角机动下的控制律.首先应用动态逆方法,分别设计了快慢回路的飞行控制律;然后利用BP神经网络,在线补偿飞机模型不确定性以及外界干扰.眼镜蛇机动的仿真结果表明,所设计的控制律在大迎角机动条件下具有良好的控制性能,能够保证闭环系统的稳定性. 相似文献
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《计算机应用与软件》2019,(11)
针对存在时延以及丢包的多包传输直流伺服电机网络控制系统(networked control system,NCS),提出一种利用滑动窗口策略多核LS-SVM丢包在线补偿的神经网络PID趋近律滑模控制器。将系统模型进行等价变换,建立无时延多包传输离散系统模型;利用滑动窗口多核LS-SVM对多包传输的数据丢包进行在线预测补偿,建立系统补偿模型。提出神经网络PID趋近律滑模控制器设计方法,通过神经网络非线性映射实现对PID趋近律参数的在线调整。利用Truetime对该方法进行仿真,结果表明,该策略可以提升丢包补偿的精度,滑模控制能够在较快响应速度的条件下减小系统抖振,对直流伺服电机网络控制系统实现了较好的跟踪控制。 相似文献
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基于模型跟随的神经网络PID飞行控制律设计 总被引:2,自引:1,他引:1
为了抑制飞行控制系统的外部扰动和建模误差,应用模型跟随自适应神经网络PID控制方法,进行飞行控制律设计。首先使用RBF神经网络进行飞行系统模型辨识,在线学习系统正向输入输出特性,辨识对象的Jacobian信息;然后应用BP神经网络实时在线调整PID参数,设计自适应神经网络PID控制器,控制飞行状态变量跟随模型输出;最后以F-8飞机纵向飞行控制系统为研究对象进行控制仿真。仿真结果表明,设计的控制器具有很强的自适应和抗干扰能力。 相似文献
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针对含有建模误差和不确定干扰的多关节机械臂轨迹跟踪控制,提出了一种神经滑模控制方法。该方法采用全局快速终端滑模面保证了系统状态能够在有限时间内到达滑模面和平衡点。采用径向基函数神经网络自适应地补偿系统的建模误差和外界干扰,保证了滑模控制在滑模面的运动。利用李亚普诺夫稳定性判据推导出了控制器的控制律和神经网络的目标函数,通过神经网络的在线学习,削弱了滑模控制的抖振。仿真结果表明了其有效性。 相似文献
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压力是工业过程控制常用参数,为了探索黄金分割自适应控制在工业过程控制中的应用技术,本文将其应用于水流管道的压力控制.利用控制实验平台,搭建了以变频调速驱动水泵的压力控制实验系统.阐述了特征建模与黄金分割自适应控制对该系统的适用性和稳定性,并通过分析确定了特征模型类型和控制算法.考虑到时延的影响,采用数字外推预测算法对管道压力误差进行处理.对控制方案进行了仿真分析,完成了与黄金分割自适应控制相关的管道压力控制实验.实验结果表明,黄金分割自适应控制能够实现管道压力的高精度控制,现场调试简单,该控制方案的优越性得到了验证. 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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污水处理过程具有多变量、非线性、大滞后和强耦合的特点,建立精确的数学模型十分困难,为了精确的描述污水处理过程,本文将1种改进型的递归神经网络应用在污水处理过程建模中,建立了污水处理过程关键水质参数的智能模型。Elman网络作为1种动态神经网络,网络的动态特性可以很好的反映系统的内部动力学特征,但是标准的Elman网络只对隐含层的输出进行了反馈,并且只反馈给了隐含层的输入,反馈信息较少。针对此问题,本文提出1种改进型的Elman网络(OAF Elman网络),增加了输出层的反馈信息,将反馈信息既传给隐含层输入又传给输出层的输入,同时将隐含层的反馈也作为输入层的输入,使网络的输出包含更多的输入信息,能够更好的反映系统的时变、非线性等特征。采集污水处理厂生化反应过程实际运行数据,取对COD影响较大的MLVSS、进水COD、pH值、氨氮4个种指标作为递归神经网络模型的输入,对污水出水的关键水质参数COD进行建模,网络的训练误差达到0.011,测试误差达到0.4875。实验结果表明:与传统的Elman网络和其他几种改进型的Elman网络相比,本文提出的OAF Elman网络具有更丰富的动力学特性,建立的污水处理模型达到... 相似文献
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Zhou Chungui Zhang Xinong Xie Shilin Zhou Tong Zhu Changchun 《Mathematics and computers in simulation》2009
A hybrid modeling method based on neural network (NN) is developed and used to model the hysteretic restoring force of a wire cable vibration isolation system for electronic equipment. Firstly, a knowledge-based model for the nonlinear hysteretic restoring force is identified using the measured data obtained from period loading tests. Secondly, the remaining characteristic of hysteretic restoring force, which cannot be modeled in an easy way, is identified using the NN method through network training. By building up a parallel hybrid NN model for the nonlinear hysteretic restoring force, the dynamic responses of the vibration isolation system under harmonic and broad band random excitations are predicted. The predicted results are compared with the measured ones to validate the effectiveness and prediction accuracy of the model. The comparative studies show the developed hybrid NN model possesses good prediction accuracy and generalization capability in contrast with the pure black box NN model. 相似文献
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一般二维模糊控制器的等效神经网络建模与验证 总被引:1,自引:0,他引:1
针对模糊控制器的计算复杂性,实时性能差,易产生维度灾难等问题,利用神经网络的万能函数逼近能力,构建一个神经网络模型,精确的逼近已知的模糊控制器,从而减少运算量,实现实时控制.以一个已知的二输入单输出模糊控制器为例,建立一个与之等效的神经网络,通过训练,使得精确的逼近模糊控制系统.最后,给定输入信号,分别用模糊控制器和神经网络控制同一个被控对象.结果表明,用一个与模糊控制器等效的神经网络来控制同一个对象,控制效果非常相似.因此,用模糊控制器的等效神经网络模型代替,在实现环节上可以减少计算复杂性,维度灾难,提高实时性能. 相似文献
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针对四旋翼无人机吊挂空运系统存在的模型不确定性及欠驱动性问题,本文提出了一种基于能量耦合的自适应控制设计.首先,基于能量整形控制方法构造了一种新型的能量存储函数以处理状态耦合.然后利用神经网络对系统未建模动态特性进行在线估计,同时设计参数自适应律在线估计模型中的未知参数,并采用基于符号函数的鲁棒控制算法补偿神经网络的估计误差.本文运用李雅普诺夫方法和拉塞尔不变性原理对闭环系统的稳定性进行了证明,并且证明了负载摆动和无人机位置误差的渐近收敛性.最后,在室内实验平台上进行了飞行实验.实验结果表明,本文提出的非线性控制方法能够在有效抑制吊挂负载摆动的同时,实现无人机位置的精确控制. 相似文献
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The intelligent autonomous control of hypersonic vehicles has aroused great interest from the field of spacecraft. To solve the problem of longitudinal attitude control of hypersonic vehicle in gliding phase, a new intelligent controller is proposed in this paper. This new controller is based on the fuzzy dynamic characteristic modeling method. The fuzzy logic is introduced into the characteristic modeling by dividing the whole restriction range into several subspaces. Simulations show that this modificatio... 相似文献
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Xiong Luo Zengqi Sun Fuchun Sun 《International Journal of Control, Automation and Systems》2009,7(1):123-132
The study on nonlinear control system has received great interest from the international research field of automatic engineering.
There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design
nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods.
However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies,
a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile,
the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement
parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy
dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is
designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition
for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via
an example.
Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under
Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant
SIC07010202.
Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral
Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor
in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests
include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system.
Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University
of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and
Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and
intelligent flight control.
Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow
in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science
and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems,
and robotics. 相似文献
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Characteristic modeling and the control of flexible structure 总被引:11,自引:0,他引:11
Appropriate modeling for a controlled plant has been a remarkable problem in the control field. A new modeling theory, i.e. characteristic modeling, is roundly demonstrated. It is deduced in detail that a general linear constant high-order system can be equivalently described with a two-order time-varying difference equation. The application of the characteristic modeling method to the control of flexible structure is also introduced. Especially, as an example, the Hubble Space Telescope is used to illustrate the application of the characteristic modeling and adaptive control method proposed in this paper. 相似文献
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简介线性联想存贮器的工作原理和实现方法,它们已应用于光通讯、谱分析、衍射光学、射电天文学、电子显微术等与信息处理、图像恢复或图像识别相关的近代光电子技术领域、然而本文该方法在自动控制建模中的应用是一种新的尝试,尤其是对复杂对象的研究,我们把它与神经网络法比较,发现在非线性对象的线性化过程,它具有花时更省、精度更高和鲁棒性的特点,也讨论了计算机模拟实验,结果由图表示。 相似文献