Adaptive Neural Dynamic Surface Control for a Missile with Input and Output Constraints
In this paper,adaptive neural dynamic surface control is applied to design a missile autopilot considering input and output constraints.A gaussian error function based input saturation model is employed such that the backstepping technique can be used in the control design.The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control.A barrier Lyapunov function(BLF)is employed in the control design in order to meet the output constraint requirement.Radius basis function(RBF)neural network based adaptive dynamic control is developed to guarantee that all the signals in the closed-loop systems are globally bounded,with arbitrary small tracking error by appropriately choosing design constants.Simulation results demonstrate the effectiveness of the proposed approach.
adaptive control neural network dynamic surface control barrier lyapunov function constraints
Jianjun Ma Peng Li Lina Geng Zhiqiang Zheng
College of Mechatronic Engineering and Automation,National University of Defense Technology,Changsha 410073,P.R.China
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
8883-8888
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）