Adaptive Sliding Mode Guidance Law with Impact Angle Constraint

2021 ◽  
pp. 539-554
Author(s):  
Hui-bo Zhou ◽  
Hong-liang Liu ◽  
Ping Li
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tianning Wang ◽  
Shengjing Tang ◽  
Jie Guo ◽  
Haoqiang Zhang

The implementation of advanced guidance laws with bearings-only measurements requires estimation of the range information. To improve estimation accuracy and satisfy the impact angle constraint, this paper proposes a two-phase optimal guidance law consisting of an observing phase and an attacking phase. In the observing phase, the determinant of Fisher information matrix is maximized to achieve the optimal observability and a suboptimal solution expressed by leading angle is derived analytically. Then, a terminal sliding-mode guidance law is designed to track the desired leading angle. In the followed attacking phase, an optimal guidance law is integrated with a switching term to satisfy both the impact angle constraint and the field-of-view constraint. Finally, comparison studies of the proposed guidance law and a traditional optimal guidance law are conducted on stationary targets and maneuvering targets cases. Simulation results demonstrate that the proposed guidance law is able to improve the range observability and achieve better terminal performances including impact angle accuracy and miss distance.


Author(s):  
Peng Li ◽  
Qi Liu ◽  
Chen-Yu He ◽  
Xiao-Qing Liu

This paper investigates the three-dimensional guidance with the impact angle constraint, actuator faults and input constraint. Firstly, an adaptive three-dimensional guidance law with impact angle constraint is designed by using the terminal sliding mode control and nonhomogeneous disturbance observer. Then, in order to solve the problem of the input saturation and actuator faults, an adaptive anti-saturation fault-tolerant three-dimensional law is proposed by using the hyperbolic tangent function based on the passive fault-tolerant control. Finally, the effectiveness of the designed guidance laws is verified by using the Lyapunov function and simulation.


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