用数据驱动的模糊控制实现多旋翼高速翻滚轨迹的实时精准跟踪。
Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following
- 基于模糊框架构建分段控制器，结合悬停稳定与时间最优轨迹预测。
- 数值求解两点边值问题生成高速翻滚最优轨迹，实现0.8秒内完成空中翻转。
- 适合高动态飞行器控制、无人系统敏捷运动设计等场景应用。
在动态系统中，最小化用户定义代价函数的最优轨迹需通过求解两点边值问题获得。优化过程生成依赖于初始条件与系统参数的最优控制序列，但若初始条件或参数存在误差，可能导致不良行为。本文提出一种数据驱动的模糊控制器合成框架，用于多旋翼的时优激进轨迹跟踪问题。特别地，针对包含空中翻转的激进机动，通过数值求解两点边值问题生成时优轨迹。采用Takagi-Sugeno模糊框架构建模糊控制器，由近悬停稳定控制器和训练以模仿时优激进轨迹的自回归滑动平均（ARMA）控制器组成。该方法有效提升了复杂机动下的跟踪精度与鲁棒性。
原文摘要 · Abstract (English)
Optimal trajectories that minimize a user-defined cost function in dynamic systems require the solution of a two-point boundary value problem. The optimization process yields an optimal control sequence that depends on the initial conditions and system parameters. However, the optimal sequence may result in undesirable behavior if the system's initial conditions and parameters are erroneous. This work presents a data-driven fuzzy controller synthesis framework that is guided by a time-optimal trajectory for multicopter tracking problems. In particular, we consider an aggressive maneuver consisting of a mid-air flip and generate a time-optimal trajectory by numerically solving the two-point boundary value problem. A fuzzy controller consisting of a stabilizing controller near hover conditions and an autoregressive moving average (ARMA) controller, trained to mimic the time-optimal aggressive trajectory, is constructed using the Takagi-Sugeno fuzzy framework.
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