Due to their superior energy efficiency, blimps may replace quadcopters for long-duration aerial tasks. However, designing a controller for blimps to handle complex dynamics, modeling errors, and disturbances remains an unsolved challenge. One recent work combines reinforcement learning (RL) and a PID controller to address this challenge and demonstrates its effectiveness in real-world experiments. In the current work, we build on that using an H-infinity robust controller to expand the stability margin and improve the RL agent's performance. Empirical analysis of different mixing methods reveals that the resulting H-infinity-RL controller outperforms the prior PID-RL combination and can handle more complex tasks involving intensive thrust vectoring. We provide our code as open-source at https://github.com/robot-perception-group/robust_deep_residual_blimp.

翻译：由于具备卓越的能量效率，飞艇有望替代四旋翼无人机执行长时间空中任务。然而，为飞艇设计能应对复杂动力学特性、建模误差及外部干扰的控制器仍是一个未解决的挑战。近期一项研究将强化学习与PID控制器相结合，通过真实世界实验验证了其有效性。本研究在此基础上引入H无穷鲁棒控制器，旨在扩大稳定裕度并提升强化学习代理的性能。对不同混合方法的实证分析表明，所提出的H无穷-鲁棒控制器在性能上优于先前的PID-强化学习组合方案，并能处理涉及大推力矢量控制的复杂任务。我们已将代码开源发布于https://github.com/robot-perception-group/robust_deep_residual_blimp。