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

Abstract

This article proposes a data-driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy-based model. Using data-driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance.