Adaptive dynamic programming-based adaptive-gain sliding mode tracking control for fixed-wing UAV with disturbances

TL;DR

This paper introduces an innovative control scheme combining adaptive dynamic programming and sliding mode control with adaptive gains to enhance fixed-wing UAV tracking performance amidst disturbances, ensuring stability and near-optimal control.

Contribution

It develops a novel adaptive-gain generalized super-twisting algorithm and integrates it with adaptive dynamic programming for improved UAV disturbance rejection.

Findings

Superior disturbance rejection demonstrated in simulations

System trajectories converge to sliding manifolds in finite time

Tracking and neural network errors are bounded

Abstract

This paper proposes an adaptive dynamic programming-based adaptive-gain sliding mode control (ADP-ASMC) scheme for a fixed-wing unmanned aerial vehicle (UAV) with matched and unmatched disturbances. Starting from the dynamic of fixed-wing UAV, the control-oriented model composed of attitude subsystem and airspeed subsystem is established. According to the different issues in two subsystems, two novel adaptive-gain generalized super-twisting (AGST) algorithms are developed to eliminate the effects of disturbances in two subsystems and make the system trajectories tend to the designed integral sliding manifolds (ISMs) in finite time. Then, based on the expected equivalent sliding-mode dynamics, the modified adaptive dynamic programming (ADP) approach with actor-critic (AC) structure is utilized to generate the nearly optimal control laws and achieve the nearly optimal performance of the sliding-mode dynamics. Furthermore, through the Lyapunov stability theorem, the tracking errors and the weight estimation errors of two neural networks (NNs) are all uniformly ultimately bounded (UUB). Finally, comparative simulations demonstrate the superior performance of the proposed control scheme for the fixed-wing UAV.