This paper proposes a general incremental policy iteration adaptive dynamic programming (ADP) algorithm for model-free robust optimal control of unknown nonlinear systems. The approach integrates recursive least squares estimation with linear ADP principles, which greatly simplifies the implementation while preserving adaptive learning capabilities. In particular, we develop a sufficient condition for selecting a discount factor such that it allows learning the optimal policy starting with an initial policy that is not necessarily stabilizing. Moreover, we characterize the robust stability of the closed-loop system and the near-optimality of iterative policies. Finally, we perform numerical simulations to demonstrate the effectiveness of the proposed method.
@article{arxiv.2508.21367,
title = {Incremental Policy Iteration for Unknown Nonlinear Systems with Stability and Performance Guarantees},
author = {Qingkai Meng and Fenglan Wang and Lin Zhao},
journal= {arXiv preprint arXiv:2508.21367},
year = {2025}
}