摘要
针对自主水下航行器(Autonomous Underwater Vehicle,AUV)在自动巡航任务中的姿态控制问题,提出了一种神经网络与滑模控制相结合的鲁棒自适应姿态控制算法。采用了RBF神经网络对AUV数学模型中的不确定项进行逼近,抑制了未建模动态和参数摄动的影响,进而基于反步法和滑模控制设计了姿态控制律,其中引入鲁棒项以克服外界干扰和神经网络逼近误差,并通过Lyapunov定理证明了控制系统的稳定性。将所设计的控制算法应用在AUV的姿态控制系统中进行数值仿真,验证了该控制算法的有效性和鲁棒性。
For the attitude control problem of Autonomous Underwater Vehicle(AUV)in automatic cruise task, a robust adaptive attitude control algorithm based on neural network and sliding mode control is proposed. The RBF neural network is used to approximate the uncertain terms in the mathematical model of AUV, and the influence of unmodeled dynamics and parameter perturbation is suppressed. Then, the attitude control law is designed based on the backstepping and sliding mode control, and the robust term is introduced to overcome the external disturbance and approximation error of neural network. Meanwhile, the stability of the control system is proved by Lyapunov theorem. The proposed control algorithm is applied to the attitude control system of an AUV for numerical simulation, and the effectiveness and robustness of the control algorithm are verified.
作者
蒋云彪
郭晨
于浩淼
JIANG Yunbiao;GUO Chen;YU Haomiao(School of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning 116026, China)
出处
《计算机工程与应用》
CSCD
北大核心
2019年第17期266-270,共5页
Computer Engineering and Applications
基金
国家自然科学基金（No.51579024,No.61374114）
中央高校基本科研业务费专项资金（No.3132016311,No.3132018128）
关键词
自主水下航行器(AUV)
姿态控制
反步法
神经网络
滑模控制
鲁棒性
Autonomous Underwater Vehicle(AUV)
attitude control
backstepping
neural network
sliding mode control
robustness
