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To address the challenge of unmanned aerial vehicle (UAV) attacking dynamic target with unknown state variations, a singularity-free fixed-time sliding mode based impact angle control guidance (SFSMIACG) law is proposed. We first utilize the radial basis function (RBF) neural network to estimate and predict the target acceleration information. Then, this paper develops a singularity-free fixed-time sliding mode to improve the convergence speed of sliding surface while eliminating singularities. This guidance law ensures that the UAV can rapidly converge to the desired impact angle within a predetermined time range, regardless of the initial conditions. Simulation results verify the effectiveness of the proposed algorithm.

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UAV Attack Guidance Law for Dynamic Target with Impact Angle Constraint

  • 会议论文

  • 发表日期：

    2026年

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  • Dechuan Wang,
  • Peng Yao,
  • Yingming Sun,
  • Zhipeng Deng

摘要

To address the challenge of unmanned aerial vehicle (UAV) attacking dynamic target with unknown state variations, a singularity-free fixed-time sliding mode based impact angle control guidance (SFSMIACG) law is proposed. We first utilize the radial basis function (RBF) neural network to estimate and predict the target acceleration information. Then, this paper develops a singularity-free fixed-time sliding mode to improve the convergence speed of sliding surface while eliminating singularities. This guidance law ensures that the UAV can rapidly converge to the desired impact angle within a predetermined time range, regardless of the initial conditions. Simulation results verify the effectiveness of the proposed algorithm.