A Finite-Horizon Game-Theoretic Learning Algorithm for Constrained Differential Games With Application to Robust Control of Quadrotor UAV

In this article, an adaptive game-theoretic learning algorithm is derived for finite-horizon differential games with control constraints. By using this learning algorithm, a general framework for solving the finite-horizon robust attitude control problem of quadrotor unmanned aerial vehicle (UAV) is provided. With instantaneous and recorded data, concurrent learning (CL) technique is adopted to identify the unknown parameters of the system model. Based on the online identification, an adaptive iterative algorithm is developed to learn the solution to the differential games, where the convergence of the saddle point is guaranteed. Simulation results are provided to verify the effectiveness of the proposed approach.