Model Predictive Control for Micro Aerial Vehicle Systems (MAV) Systems

TL;DR

This paper develops a real-time model predictive control method for quadcopters, utilizing non-linear guidance and convex optimization to improve trajectory tracking and feasibility in hovering flight.

Contribution

It introduces an explicit MPC control framework with sparse solvers tailored for quadcopter trajectory tracking and feasibility constraints.

Findings

Successful real-time trajectory tracking in hovering flight

Effective use of convex optimization for control

Feasibility constraints derived for quadcopter dynamics

Abstract

This paper presents a method for path-following for quadcopter trajectories in real time. Non-Linear Guidance Logic is used to find the intercepts of the subsequent destination. Trajectory tracking is implemented by formulating the trajectory of the quadcopter using its jerk, in discrete time, and then solving a convex optimization problem on each decoupled axis. Based on the maximum possible thrust and angular rates of the quadcopter, feasibility constraints for the quadcopter have been derived. In this report we describe the design and implementation of explicit MPC controllers where the controllers were executed on a computer using sparse solvers to control the vehicle in hovering flight.