Optimal Selection Method for Terminal-Guidance Parameters with Complex Constraints Based on the TLBO Algorithm

Simultaneously satisfying multiple constraints while maintaining a relatively simple guidance law remains challenging in terminal guidance. This paper proposes a parameter optimization method based on teaching–learning-based optimization (TLBO). Taking proportional navigation as an example, a bias term is designed to achieve a desired impact angle and two bias term adjustment coefficients are introduced as additional optimization variables to satisfy the seeker blind-zone constraint. The lead-angle and normal acceleration rate constraints are converted into normal acceleration magnitude constraints. With impact time and impact speed as optimization objectives, TLBO is employed to optimize the initial conditions and guidance parameters. Numerical simulations demonstrate the feasibility of the proposed method, and the Monte Carlo results show that satisfactory terminal guidance performance is maintained under the specified normal-acceleration perturbations.