摘要
给出了基于观测器的水下机器人神经网络自适应控制算法 .控制算法由 3部分组成 :输出反馈控制、神经网络以及滑模项 ,其中输出反馈控制为了保证系统的初始稳定性 ;神经网络用于逼近系统的非线性动力学 ;滑模项用于补偿和抑制系统的外部扰动、神经网络逼近误差等 .控制算法中所需要的速度量由状态观测器来提供 .基于Lyapunov稳定理论给出了系统闭环稳定条件和稳定域 .
Observer based neural network adaptive control(OBNC) algorithm for underwater vehicles is provided in this paper. The algorithm is composed of three parts: output feedback control, neural network and sliding mode item. Among them, the output feedback control is used to guarantee the stability of the system in initial phase, the neural network is used to approach the nonlinear dynamics of underwater vehicles, and the sliding mode item is used to compensate and bate the internal and external disturbances. The stable conditions and attraction region of the proposed observer based NN control algorithm is provided with Lyapunov based approach. The effectiveness of this control scheme is demonstrated with pool experiment.
出处
《机器人》
EI
CSCD
北大核心
2004年第6期515-518,共4页
Robot
基金
中国科学院创新基金资助项目 (A0 1 0 60 3)
关键词
水下机器人
神经网络
控制
观测器
underwater vehicles
neural network
control
observer
