Existing nonlinear guidance methods are difficult to reconcile performance optimality with stability assurance. This study proposes a concept of robust incremental learning for approximate optimal control of nonlinear terminal guidance problems. It transitions incrementally and stably from a traditional analytically formulated guidance law to an approximate optimal guidance policy. Specifically, we propose an incremental policy iteration algorithm, where a base guidance law is utilized to mitigate the initial instability and warm-start the learning process. Then, a robustness enhancement technique is proposed to theoretically guarantee the stability of learning process, where the guidance command is refined leveraging a virtual Lyapunov-based energy function. As a result, a robust and efficient learning method for nonlinear optimal guidance problems is developed. Simulation results for a specific impact-angle-constrained guidance problem verify advantages of the proposed method on efficiency, stability, and optimality.
Robust Incremental Learning of Approximate Dynamic Programming for Nonlinear Optimal Guidance
IEEE Transactions on Aerospace and Electronic Systems ; 61 , 3 ; 6041-6052
2025-06-01
1878894 byte
Article (Journal)
Electronic Resource
English