Lyapunov-based Nonlinear Model Predictive Control for Attitude Trajectory Tracking of Unmanned Aerial Vehicles

TL;DR

This paper introduces a Lyapunov-based nonlinear model predictive control method for UAV attitude tracking, ensuring stability and outperforming existing controllers through simulations and software-in-the-loop tests.

Contribution

A novel Lyapunov-based nonlinear MPC for UAV attitude control that guarantees stability and improves performance over existing methods.

Findings

The proposed controller guarantees system stability.

It outperforms backstepping and sliding mode controllers.

It is effective in simulation and software-in-the-loop tests.

Abstract

This paper presents a new Lyapunov-based nonlinear model predictive controller (LNMPC) for the attitude control problem of unmanned aerial vehicles (UAVs), which is essential for their functioning operation. The controller is designed based on a quadratic cost function integrating UAV dynamics and system constraints. An additional contraction constraint is then introduced to ensure closed-loop system stability. That constraint is fulfilled via a Lyapunov function derived from a sliding mode controller (SMC). The feasibility and stability of the LNMPC are finally proved. Simulation and comparison results show that the proposed controller guarantees the system stability and outperforms other state-of-the-art nonlinear controllers such as the backstepping controller (BSC) and SMC. In addition, the proposed controller can be integrated into an existing UAV model in the Gazebo simulator to perform software-in-the-loop tests. The results show that the LNMPC is better than the built-in PID controller of the UAV, which confirms the validity and applicability of our proposed approach.