Event-Triggering-Learning-Based ADP Control for Post-Stall Pitching Maneuver of Aircraft

In this article, an improved event-triggering-learning (ETL)-based adaptive dynamic programming (ADP) method for the post-stall pitching maneuver of aircraft is proposed to achieve the robust optimal control and reduce the computational cost. First, a feedforward control with the nonlinear disturbance observer (NDO) technique is designed to attenuate the adverse effects caused by the unsteady aerodynamic disturbances. Subsequently, the ADP method with a critic neural network which is constructed to approximate the value function in the Hamilton–Jacobi–Bellman equation is employed to conduct the optimal control of aircraft. In addition, to reduce the computational cost of learning, the event-triggering (ET) mechanism with an improved ET condition is applied. The Lyapunov stability theory is utilized to prove that all signals in the closed-loop control system are uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness of the proposed ETL-based ADP method.