摘要
针对船载稳定平台的平稳控制问题,考虑到稳定平台存在未建模动态等不确定性以及未知时变环境扰动,将自适应技术、径向基神经网络技术与矢量逆推的方法相结合,提出一种鲁棒自适应神经网络的稳定平台控制方案。采用矢量逆推的方法,设计稳定平台的平稳控制律;运用径向基神经网络技术对稳定平台系统的未建模动态等不确定性进行估计及补偿;利用自适应技术在线估计径向基神经网络相关参数及环境扰动的上界;并引入最少学习参数方法降低控制方案的计算负载。通过Lyapunov理论证明稳定平台闭环控制系统的所有变量的一致最终有界性。最后,基于稳定平台的仿真结果验证了所提出的鲁棒自适应神经网络控制方案的有效性。
Aiming at the problem of stable control of the ship-borne stabilization platforms, considering the uncertainties such as unmodeled dynamics and unknown time-varying environmental disturbances of the ship-borne stabilization platforms.The adaptive technology, radial basis neural network technology and backstepping method are combined to propose a robust adaptive neural network stable platform control scheme. The method of backstepping is used to design the stable control law of the stable platform;the RBF neural network technology is used to estimate and compensate the uncertainties such as unmodeled dynamics of the stable platform system. The adaptive technology is used to estimate the relevant parameters of the RBF neural network and the upper bounds of environmental disturbances;and the least learning parameter method is introduced to reduce the calculation load of the control scheme. The Lyapunov theory is used to prove that all variables of the closed-loop control system of the stable platform are uniformly ultimately bounded. Finally, the simulation results based on the stable platform verify the effectiveness of the proposed robust adaptive neural network control scheme.
作者
贺广健
彭程
HE Guang-jian;PENG Cheng(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Acalemy of Sciences,Changchun 130033,China)
出处
《电脑知识与技术》
2021年第19期9-13,共5页
Computer Knowledge and Technology
基金
吉林省科技发展计划项目(20200201294JC)。
关键词
稳定平台
自适应技术
径向基神经网络
矢量逆推
最少学习参数
stable platform
adaptive technology
radial basis neural network
backstepping
least learning parameter
