TY - GEN
T1 - A Fixed-Time Convergence Adaptive Fast Sliding Mode Guidance Law with Angle Constraint
AU - Liu, Yuan
AU - Yang, Zhen
AU - Huo, Weiyu
AU - He, Yupeng
AU - Zhou, Deyun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a fixed-time convergence adaptive fast sliding mode guidance law with line-of-sight (LOS) angle constraint is designed for the terminal guidance problem of large maneuvering targets in the air. Firstly, the three-dimensional (3D) spatial missile-target relative equations of motion are constructed, and the terminal guidance model with LOS angle constraints is established. Secondly, a novel fixed-time nonsingular terminal sliding mode surface is designed, and by designing a fast convergence law, while avoiding system singularity and reducing end jitter, the LOS angle and angular rate of the missile are guaranteed to converge quickly to the desired value in a fixed time, and have the advantage that the upper bound of the convergence time can be artificially set. In addition, the interference observer is constructed using the good approximation property of RBF neural network for continuous functions, which provides an effective estimation of the unknown acceleration of the intercepted target, and increases the self-adaptive capability of the system. Finally, the effectiveness and superiority of the designed guidance law is verified by simulation.
AB - In this paper, a fixed-time convergence adaptive fast sliding mode guidance law with line-of-sight (LOS) angle constraint is designed for the terminal guidance problem of large maneuvering targets in the air. Firstly, the three-dimensional (3D) spatial missile-target relative equations of motion are constructed, and the terminal guidance model with LOS angle constraints is established. Secondly, a novel fixed-time nonsingular terminal sliding mode surface is designed, and by designing a fast convergence law, while avoiding system singularity and reducing end jitter, the LOS angle and angular rate of the missile are guaranteed to converge quickly to the desired value in a fixed time, and have the advantage that the upper bound of the convergence time can be artificially set. In addition, the interference observer is constructed using the good approximation property of RBF neural network for continuous functions, which provides an effective estimation of the unknown acceleration of the intercepted target, and increases the self-adaptive capability of the system. Finally, the effectiveness and superiority of the designed guidance law is verified by simulation.
KW - adaptive sliding mode
KW - angular constraint
KW - fixed time convergence
KW - neural network observer
KW - terminal guidance
UR - http://www.scopus.com/inward/record.url?scp=85183576385&partnerID=8YFLogxK
U2 - 10.1109/ICCMA59762.2023.10375028
DO - 10.1109/ICCMA59762.2023.10375028
M3 - 会议稿件
AN - SCOPUS:85183576385
T3 - 2023 11th International Conference on Control, Mechatronics and Automation, ICCMA 2023
SP - 380
EP - 385
BT - 2023 11th International Conference on Control, Mechatronics and Automation, ICCMA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Control, Mechatronics and Automation, ICCMA 2023
Y2 - 1 November 2023 through 3 November 2023
ER -