发表机构
Kyoto University（京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对四旋翼开发了结合在线重力 trim 自适应的泰勒展开式预测成本自适应控制，通过高阶动力学辨识与线性化提升了突发负载变化下的跟踪性能。
AI 中文摘要
本文开发了用于四旋翼的泰勒展开式预测成本自适应控制（PCAC），并结合在线重力 trim 自适应。通过非线性动力学关于标称悬停的一阶、二阶、三阶展开，定义了稀疏采样数据字典，用于带可变遗忘率的逐行递归最小二乘辨识。每一步，将辨识出的预测器在当前状态处线性化，其雅可比矩阵在预测时域内保持固定。辨识出的垂直动力学还能估计飞行器质量和重力 trim 输入，消除了固定标称重力补偿。对突发负载变化和激进螺旋跟踪的仿真表明，高阶预测器在保持标准 PCAC 公式的同时，提升了预测与跟踪性能。
英文摘要
This paper develops Taylor-informed predictive cost adaptive control (PCAC) for quadrotors with online gravity-trim adaptation. First-, second-, and third-order expansions of the nonlinear dynamics about nominal hover define sparse sampled-data dictionaries for row-wise recursive least-squares identification with variable-rate forgetting. At each step, the identified predictor is linearized at the current state, and its Jacobian is fixed over the prediction horizon. The identified vertical dynamics also estimate the vehicle mass and gravity-trim input, eliminating fixed nominal gravity compensation. Simulations with an abrupt payload change and aggressive helix tracking show that the higher-order predictors improve prediction and tracking while preserving the standard PCAC formulation.
Comments6 pages, 4 figures
