Data-Driven Tracking Control for Nonaffine Yaw Channel of Helicopter via Off-Policy Reinforcement Learning

This article presents an off-policy tracking control scheme for the continuous-time nonaffine yaw channel of uncrewed aerial vehicle helicopter. First, the article constructs an affine augmented system (AAS) within a parallel control structure to convert the original nonaffine tracking error dynamics into affine dynamics. Second, the article derives a stability criterion linking the nonaffine system and the AAS, demonstrating that the obtained zero-sum policy from the AAS can achieve the $H_\infty$ performance of the nonaffine system. Third, a data-driven off-policy tracking algorithm is designed for approximating the zero-sum solution of the Hamilton–Jacobi–Isaacs equations with unknown dynamics. Moreover,…

• NN
  National Natural Science Foundation of China

  Award: 62103408
• BN
  Beijing Nova Program

  Award: 20240484516