Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal 'bang-bang' control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance & Control Networks by learning to take consecutive waypoints into account. We fly a 4x3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance & Control Networks offer.

翻译：实现快速自主飞行需要计算高效且鲁棒的算法。为此，我们训练制导与控制网络来近似从能量最优到时间最优飞行的最优控制策略。研究表明，越接近时间最优的"bang-bang"控制剖面，策略学习的难度越大。我们还评估了知晓四旋翼最大转子角速度的重要性，并表明高估或低估这一限制会导致飞行鲁棒性下降。我们提出了一种在线识别当前最大转子角速度的算法，以及一个根据所识别限制自适应调整策略的网络。最后，通过对先前制导与控制网络工作的扩展，我们使其能够考虑连续航路点。在4×3米赛道上的飞行实验中，我们实现了与基于微分平坦性的最小加加速度基准控制器相近的圈速，同时受益于制导与控制网络提供的灵活性。