Neglecting complex aerodynamic effects hinders high-speed yet high-precision multirotor autonomy. In this paper, we present a computationally efficient learning-based model predictive controller that simultaneously optimizes a trajectory that can be tracked within the physical limits (on thrust and orientation) of the multirotor system despite unknown aerodynamic forces and adapts the control input. To do this, we leverage the well-known differential flatness property of multirotors, which allows us to transform their nonlinear dynamics into a linear model. The main limitation of current flatness-based planning and control approaches is that they often neglect dynamic feasibility. This is because these constraints are nonlinear as a result of the mapping between the input, i.e., multirotor thrust, and the flat state. In our approach, we learn a novel representation of the drag forces by learning the mapping from the flat state to the multirotor thrust vector (in a world frame) as a Gaussian Process (GP). Our proposed approach leverages the properties of GPs to develop a convex optimal controller that can be iteratively solved as a second-order cone program (SOCP). In simulation experiments, our proposed approach outperforms related model predictive controllers that do not account for aerodynamic effects on trajectory feasibility, leading to a reduction of up to 55% in absolute tracking error.

翻译：忽视复杂气动效应会限制多旋翼实现高速高精度自主飞行。本文提出一种计算高效的学习型模型预测控制器，该控制器能够在未知气动力作用下，同时优化可在多旋翼系统物理极限（推力和姿态角约束）内跟踪的轨迹，并自适应调整控制输入。为此，我们利用多旋翼经典微分平坦特性，将其非线性动力学转换为线性模型。当前基于平坦特性的规划与控制方法的主要局限在于常忽略动力学可行性，这是因为输入（即多旋翼推力）与平坦状态间映射导致的约束具有非线性特征。在本方法中，我们通过将平坦状态到多旋翼推力矢量（世界坐标系下）的映射学习为高斯过程，从而得到全新的阻力表示。该方法利用高斯过程特性构建凸最优控制器，可迭代求解为二阶锥规划问题。仿真实验表明，相较于未考虑气动效应的相关模型预测控制器，本方法在轨迹可行性方面表现更优，绝对跟踪误差最高降低55%。