用稀疏识别建模无人机动力学，实现避障与精准轨迹跟踪
Sparse Identification of Nonlinear Dynamics-based Model Predictive Control for Multirotor Collision Avoidance
- 基于稀疏非线性动力学识别，从少量数据中提取系统方程
- 在质量不确定和气动效应下仍能准确建模并实现轨迹跟踪
- 适合需要高鲁棒性的无人机自主避障场景
本文提出一种数据驱动的多旋翼无人机避障模型预测控制方法，考虑负载带来的不确定性及未知模型问题。通过稀疏非线性动力学识别（SINDy）方法，在数据有限条件下，自动发现多旋翼系统的主导动力学方程，该方法假设系统行为由少数关键函数主导。利用模型预测控制（MPC）在状态与控制输入约束下实现高精度轨迹跟踪。为避免碰撞，将障碍物距离约束加入MPC优化问题。仿真结果表明，该方法可有效识别包含质量参数不确定性和气动影响的系统方程，并在复杂环境下同时实现障碍物规避与期望轨迹的精确跟踪。
原文摘要 · Abstract (English)
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.
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