Inverse Optimal-Based Attitude Control for Fixed-Wing Unmanned Aerial Vehicles

Abstract

This article presents a novel design of an attitude control system for fixed-wing unmanned aerial vehicles (UAV) based on inverse optimal control theory. The unknown disturbances and model uncertainties of the UAV system are estimated using a simple linear disturbance observer. Under reasonable assumptions and a suitable parameter design method, the estimation errors are ensured to be exponentially asymptotically stable. By appending the estimation errors to the state vector and creating a proper virtual control signal, we transformed the original system into disturbance-free linear systems. Thus, inverse optimal control theory is utilized to design an attitude controller by using the backstepping technique. With theoretical results that are mathematically proven, the proposed controller not only ensures asymptotic stability but also reduces the cost function that penalizes both stabilization errors and control inputs without solving the Hamilton-Jacobi-Bellman or Hamilton-Jacobi-Isaacs equation. Finally, simulation scenarios are set up to compare the performances of the proposed scheme with those of other techniques using sliding mode control and the classic PID controller. The simulation results show that the presented inverse optimal controller (IOC) guarantees the best performance in comparison with other approaches.

Keywords

• Fixed-wing UAV
• inverse optimality
• disturbance observer
• Hamilton-Jacobi-Isaacs