Prescribed‐Time Adaptive Robust Optimal Control for Quadrotor UAV Using Game‐Theoretic Reinforcement Learning
TL;DR
An online actor‐critic reinforcement learning algorithm is employed to efficiently approximate the solution of the otherwise intractable HJI equation in real time and verify that prescribed‐time convergence, fast response, and robust flight performance under strong environmental disturbances are achieved by the proposed method.
Abstract
This paper investigates the optimal control problem of quadrotor unmanned aerial vehicles (UAVs) under uncertain dynamics and external environmental disturbances. First, the quadrotor UAV model with disturbances is considered, and a transformation function incorporating the prescribed time and accuracy via an auxiliary function is constructed to reformulate the problem as an optimal control problem. Subsequently, the control input and external disturbance are modeled as adversarial agents, and their interaction is analyzed using zero‐sum game theory, which results in the derivation of a modified Hamilton–Jacobi–Isaacs (HJI) equation. To implement the derived control policy, an online actor‐critic reinforcement learning algorithm is employed to efficiently approximate the solution of the otherwise intractable HJI equation in real time. Finally, numerical simulations are conducted to verify that prescribed‐time convergence, fast response, and robust flight performance under strong environmental disturbances are achieved by the proposed method.