Optimal Impact Angle Guidance via Numerical Optimization Under Non-convex Constraints

초록

Many optimal guidance problems can be formulated as non-convex optimization problems, which can be solved indirectly by relaxation, convexification, or linearization. Although these methods are guaranteed to converge to the global optimum of the modified problems, the obtained solution may not guarantee global optimality or even the feasibility of the original non-convex problems. In this paper, we propose an ADMM-based optimal guidance law that directly handles the non-convex constraints encountered in formulating the guidance problems. The proposed ADMM-based guidance law alternatingly solves the least square problems and projects the solution onto non-convex feasible sets, which rapidly converges to feasible suboptimal solutions or sometimes to the globally optimal solutions. The proposed algorithm is verified via a series of numerical simulations on impact angle guidance problems, and it is demonstrated that the proposed algorithm provides better guidance performance than conventional techniques. © ICROS 2023.

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