共查询到18条相似文献,搜索用时 140 毫秒
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《航空学报》2015,(10)
为避免子控制器切换时控制量的跳变,提出了一种非线性自适应切换控制混合方法。针对输入输出反馈线性化子控制器在使用中存在的逆误差及模型不确定性,采用多层神经网络进行在线补偿,为实现此类非线性自适应子控制器的平滑切换,实际控制律采用各子控制律的凸组合,各组合系数值由切换参数确定。通过合适的设计参数选取与神经网络权值更新律设置,寻找到了闭环切换系统的公共Lyapunov函数,保证了此类系统在切换控制混合下的稳定性。在倾转旋翼机轨迹跟踪控制的应用中,设计了直升机模式、过渡模式与飞机模式的非线性子控制器,应用神经网络在线补偿与随短舱角的控制混合,仿真结果表明该方法具有对系统不确定性的鲁棒性及平滑切换的特性。 相似文献
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基于RBF神经网络提出了一种H∞自适应控制方法。控制器由等效控制器和H∞控制器两部分组成。用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能。H^∞控制器用于减弱外部及神经网络的逼近误差对跟踪的影响。所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标。最后给出的算例验证了该方法的有效性。 相似文献
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基于小波神经网络的自适应飞/推控制系统设计 总被引:1,自引:1,他引:0
基于小波神经网络提出了一种H∞自适应控制方法。控制器由等效控制器和H∞控制器两部分组成。用小波神经网络逼近非线性函数,并把逼近误差引入到权值的自适应律中用以改善系统的动态性能。H∞控制器用于减弱外部及神经网络的逼近误差对跟踪的影响。所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标。最后基于所设计的控制方法对新一代歼击机设计了飞/推控制系统,并对飞机作大迎角机动仿真。仿真结果表明所设计的飞/推控制系统是有效的,同时验证了所设计的非线性控制方法是有效性的。 相似文献
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针对具有未知动态模型的非线性无人机群的编队问题,设计了基于神经网络的反步控制策略。首先,通过径向基(RBF)神经网络来逼近无人机的未知非线性动态,增加系统的鲁棒性和抗扰能力;然后,引入方向刚性图理论,结合反步控制策略,设计了仅基于方向信息的无人机群编队分布式控制器,并通过Lyapunov方法证明了控制系统的稳定性;最后,通过Simulink仿真验证了控制器的有效性。 相似文献
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基于滑模神经网络的自主飞艇姿态控制 总被引:2,自引:0,他引:2
针对自主飞艇飞行环境的不确定性,提出了一种基于自适应滑模神经网络的姿态控制系统.平流层高空飞行环境对飞艇控制产生了许多不确定性因素,利用自适应变结构控制和神经网络方法设计了飞艇的俯仰通道控制器.非线性仿真结果表明:控制器能够适应对象结构参数及外部扰动的大范围变化,满足姿态控制稳定性要求,同时也消除了变结构控制系统的抖振,具有良好的鲁棒性和动态性能. 相似文献
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制导炸弹传统的自动驾驶仪设计过程中需要一个高精度的空气动力学模型,以表示系统非线性的增益图表。本文提出一种简化自动驾驶仪设计过程的方法,就是设计一个相应于简单飞行条件下的反相控制器和一个在线神经网络,神经网络用于处理因近似逆控制产生的误差,这样不仅简化了设计过程,也无需准确的空气动力学数据,尤其是大攻角I晦界值或其它空气动力的非线性部分,在设计过程中逆过程的选择本身可以建立相应的临界值,并在学习过程中进行比较,文章最后给出了完全六自由度制导炸弹应用上述方法仿真后的结果。 相似文献
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将动态逆控制技术应用于飞翼式布局无人机的姿态控制回路,以适应飞翼布局无人机控制系统要求。介绍了动态逆控制器解耦控制原理,以及神经网络补偿结构的作用和设计方法,并基于无人机非线性姿态运动学和动力学模型设计了基于神经网络补偿的动态逆控制器。在强耦合、强非线性的飞翼布局无人机模型上,通过数学仿真验证了系统具有良好的动态性能和稳态特性,控制器具有很强的鲁棒性。 相似文献
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基于神经网络的MIMO非线性最小相位系统鲁棒自适应控制 总被引:1,自引:1,他引:0
针对一类模型未知的具有不确定性和外部干扰的多输入多输出(MIMO)非线性最小相位系统提出了鲁棒自适应输出反馈跟踪控制方案。用高斯径向基函数(RBF)神经网络逼近对象未知非线性,用高增益观测器估计系统不可测量状态。所设计的鲁棒自适应控制器不仅能使闭环系统稳定,所有状态有界,而且跟踪误差一致最终有界,并保证最终边界足够小。仿真结果表明了所提出方法的有效性。 相似文献
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A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output (MIMO) aeroelastic system in the presence of wind gust, system uncertainties, and input nonlinearities consisting of input saturation and dead-zone. In regard to the input nonlinear-ities, the right inverse function block of the dead-zone is added before the input nonlinearities, which simplifies the input nonlinearities into an equivalent input saturation. To deal with the equiv-alent input saturation, an auxiliary error system is designed to compensate for the impact of the input saturation. Meanwhile, uncertainties in pitch stiffness, plunge stiffness, and pitch damping are all considered, and radial basis function neural networks (RBFNNs) are applied to approximate the system uncertainties. In combination with the designed auxiliary error system and the backstep-ping control technique, a constrained adaptive neural network controller is designed, and it is pro-ven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method. Finally, extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust, system uncertainties, and input nonlinearities. 相似文献
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基于反馈误差学习的神经网络控制 总被引:1,自引:0,他引:1
研究了应用神经网络和PD反馈控制实现非线性系统的自适应跟踪问题。PD反馈控制器不但保证闭环系统的稳定性,同时其输出又作为训练神网络的参考信号。证明了通过选择适当的初始加权和加权的调节速率可以实现非线性系统的固定点跟踪。 相似文献
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YAO Jianyonga b JIAO Zongxiaa YAO Binc SHANG Yaoxinga DONG Wenbind a Science Technology on Aircraft Control Laboratory Beihang University Beijing China b 《中国航空学报》2012,25(5):766-775
This paper deals with the high performance force control of hydraulic load simulator. Many previous works for hydraulic force control are based on their linearization equations, but hydraulic inherent nonlinear properties and uncertainties make the conventional feedback proportional-integral-derivative control not yield to high-performance requirements. In this paper, a nonlinear system model is derived and linear parameterization is made for adaptive control. Then a discontinuous projection-based nonlinear adaptive robust force controller is developed for hydraulic load simulator. The proposed controller constructs an asymptotically stable adaptive controller and adaptation laws, which can compensate for the system nonlinearities and uncertain parameters. Meanwhile a well-designed robust controller is also developed to cope with the hydraulic system uncertain nonlinearities. The controller achieves a guaranteed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities; in the absence of uncertain nonlinearities, the scheme also achieves asymptotic tracking performance. Simulation and experiment comparative results are obtained to verify the high-performance nature of the proposed control strategy and the tracking accuracy is greatly improved. 相似文献
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He Mingyi Jiang Hailin Wei Jiang Li Yong Yang Xiangyu Li Jun 《Aerospace and Electronic Systems Magazine, IEEE》1998,13(9):27-29
Modeling of angle tracking systems in the presence of actuator non-linearity such as angle, position and rate limits is a very significant and difficult task in the design and implementation of aircraft, target-tracking, and missile guided systems. A new recurrent neural network with time-delayed inputs and output feedback is used for the modeling of angle tracking systems, with emphasis on the neural network architecture, principles and algorithms. The neural network controller with modeling units for angle tracking is designed by using TMS320C25 processors. For time and size requirements, limited precision technology and look-up table technology are used in the design of the hardware and software systems. Given a set of input commands, the network is trained to control the system within the constraints imposed by actuators. The results show that the proposed networks are able to model the angle tracking system through learning without separate consideration of the non-linearity of actuators 相似文献
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提出一种基于RBF神经网络的一类非线性系统反演鲁棒自适应控制器设计方法。使用RBF神经网络逼近系统不确定性,并和控制器与虚拟控制器中的鲁棒项一起消除不确定性的影响,由Lyapunov稳定性理论推出的RBF神经网络权值矩阵的自适应律能保证闭环系统的所有信号有界,且误差能够全局指数收敛于原点的邻域。该方法不需要系统不确定性的上界以及其任意阶导数,最后的仿真结果验证了方法的有效性。 相似文献
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Lin C.L. Shieh N.C. Tung P.C. 《IEEE transactions on aerospace and electronic systems》2002,38(3):918-932
Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results. 相似文献