基于径向基神经网络的空天飞行器鲁棒自适应轨迹线性化控制
Design of robust adaptive trajectory linearization controller for aerospace vehicles based on RBFNN
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摘要: 基于轨迹线性化控制方法(TLC)以及径向基神经网络(RBFNN)技术研究了一种新的鲁棒自适应轨迹线性化控制方案并应用于空天飞行器(ASV)飞行控制系统设计中.首先基于被控对象的分析模型设计系统的TLC控制器,然后利用RBFNN对系统不确定的逼近能力,设计了鲁棒自适应控制器及参数的自适应调节律,并采用Lyapunov方法严格证明了闭环系统所有误差信号一致最终有界.最后应用新控制方案设计了ASV飞行控制系统,仿真结果表明了方法的有效性.
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关键词:
- 轨迹线性化控制(TLC) /
- 径向基神经网络(RBFNN) /
- 鲁棒自适应控制 /
- 空天飞行器(ASV)
Abstract: This paper presents a novel robust adaptive trajectory linearization control(TLC) method based on radial basic function neural network(RBFNN) and introduces its application to an aero-space vehicle(ASV) flight control system.Firstly,the TLCcontroller was designed based on an analytical model of the control plant.Secondly,RBFNNwas utilized to approximate the system's uncertainties,and based on which,a robust adaptive controller as well as an adaptive law were designed.Then,Lyapunov's direct method was used,and a rigorous poof was demonstrated in order to guarantee the ultimate boundedness of all the signals in the closed loop system of the provided method.Finally,the proposed method was applied to the flight control system design of ASV,and its effectiveness was verified by simulation results. -
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