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.

翻译：本文提出了一种考虑不确定性和未知负载模型的数据驱动模型预测控制方法，用于多旋翼飞行器的避障控制。为应对这一挑战，采用非线性动力学稀疏辨识方法获取多旋翼系统的控制方程。SINDy方法能够在数据量有限的条件下发现目标系统的控制方程，其基本假设是少数函数能够表征系统的主导特性。通过考虑状态和控制输入的约束，模型预测控制被用于实现精确的轨迹跟踪性能。为避免运行过程中的碰撞，MPC优化问题通过引入关于障碍物的不等式约束进行重构。仿真结果表明，SINDy能够辨识出包含质量参数不确定性和气动效应的多旋翼系统控制方程。此外，仿真结果验证了所提方法在精确跟踪期望轨迹的同时具备有效避障的能力。