Adaptive iterative learning control method for finite-time tracking of an aircraft track angle system based on a neural network - Zhang, Chunli; Tian, Xu; Yan, Lei (Frontiers in Physics, 2022)
Based on neural network, this paper presents a new adaptive iterative learning control method for the finite time tracking control problem of uncertain aircraft track angle system, which can control the aircraft track inclination through the designed control input rudder deflection angle, so that it can track the preset trajectory in a finite time interval. Firstly, the flight path angle system of the aircraft is abstractly modeled by variable substitution to obtain a triangular model in the form of strict feedback. Secondly, RBF neural network approximation is used to model the uncertain part of the system, aiming at the abstract strict feedback model, two virtual quantities are designed through the three-layer inversion design method, and then Lyapunov functions are designed for each subsystem to derive virtual control laws, the actual control law and the neural network weight adaptive laws. Through Lyapunov stability analysis, it can be seen that the designed controller and adaptive laws can make the whole closed-loop system tend to be stable, and realize the tracking of target trajectory in a finite time interval. Finally, the feasibility and effectiveness of the theory are verified by a simulation example.