Sparse Identification of Nonlinear Dynamics-based Model Predictive Control for Multirotor Collision Avoidance

TL;DR

This paper introduces a data-driven control method combining sparse identification of nonlinear dynamics with model predictive control to enable multirotor collision avoidance, even with uncertain payloads and unknown system models.

Contribution

It integrates SINDy with MPC for multirotor collision avoidance, effectively handling uncertainties and unknown dynamics with limited data.

Findings

SINDy accurately identifies multirotor dynamics with payload uncertainty.

The combined approach successfully avoids obstacles in simulation.

The method achieves precise trajectory tracking despite uncertainties.

Abstract

This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with low data, assuming that few functions have the dominant characteristic of the system. Model predictive control (MPC) is utilized to obtain accurate trajectory tracking performance by considering state and control input constraints. To avoid a collision during operation, MPC optimization problem is again formulated using inequality constraints about an obstacle. In simulation, SINDy can discover a governing equation of multirotor system including mass parameter uncertainty and aerodynamic effects. In addition, the simulation results show that the proposed method has the capability to avoid an obstacle and track the desired trajectory accurately.