Learning from Stable Adaptive NN Output Feedback Control of Uncertain Ship Dynamics

摘要：

This paper studies the problem of learning from stable adaptive neural network (NN) output feedback control of ocean surface ship in uncertain dynamical environments. When only ship position and heading measurements are available for identification and control, stable adaptive output feedback NN tracking controller is proposed by employing a high-gain observer to estimate the other states of ship dynamics. Partial persistent excitation (PE) condition of some internal signals in the closedloop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the uncertain ship dynamics and of storing the learned knowledge in memory. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

关键词： Learning Adaptive Neural Network (NN) Control Uncertain Dynamics Output Feedback PE Condition

作者: Shi-Lu Dai Min Wang Cong Wang Liejun Li

作者单位: College of Automation Science and Engineering, South China University of Technology, Guangzhou 51064 College of Automation Science and Engineering, South China University of Technology, Guangzhou 51064 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 5076-5081

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）