Experimental validation of an explicit flatness-based MPC design for quadcopter position tracking

TL;DR

This paper presents an explicit flatness-based MPC approach for quadcopter position control that reduces computational complexity while maintaining high performance, validated through simulations and experiments.

Contribution

It introduces a novel explicit MPC design using flat output space to linearize nonlinear quadcopter dynamics, enabling scalable and efficient control.

Findings

Achieves comparable performance to state-of-the-art methods

Reduces computational effort significantly

Validated through both simulations and experimental tests

Abstract

Due to the nonlinearities and operational constraints typical to quadcopter missions, Model Predictive Control (MPC) encounters the major challenge of high computational power necessary for the online implementation. This problem may prove impractical, especially for a hardware-limited or small-scale setup. By removing the need for online solvers while keeping the constraint satisfaction and optimality, Explicit MPC (ExMPC) stands out as a strong candidate for this application. Yet, the formulation was usually hindered by the two main problems: nonlinearity and dimensionality. In this paper, we propose an ExMPC solution for the quadcopter position stabilization by analyzing its description (dynamics and constraints) in the flat output space. With the former issue, the system is exactly linearized into a concatenation of three double integrators at a price of cumbersome constraints in the new coordinates. For the latter, with a suitable characterization of these constraints, the stabilizing ExMPC can be computed for each double integrator separately. The controller is then validated via simulations and experimental tests. The proposed scheme achieves similar performance and guarantees to the state-of-the-art solution but with notably less computational effort, allowing scalability in a centralized manner