Y. Chen, Y. Li and D.J. Braun, Learning Finite-Horizon Nonlinear Optimal Control Policies with Unknown Control-Affine Dynamics, Systems & Control Letters, vol. 203, no. 106161, 2025.
This paper introduces a model-free method for designing optimal controllers over a fixed time horizon. Instead of relying on a detailed system model, the method adapts simple linear rules that learn to approximate the optimal control policy. The authors prove that the process converges to the true optimal solution and demonstrate it with a numerical example.
Why it matters: Many real-world systems are too complex to model accurately. This method learns high-performance control strategies directly from interaction, reducing dependence on precise models and advancing applications in robotics, automation, and beyond.