异步深度模型参考自适应控制
Asynchronous Deep Model Reference Adaptive Control
摘要：
本文提出了基于深度神经网络的模型参考自适应控制（DMRAC）的异步实现方法.我们评估这种新的神经自适应控制架构，通过小型四轴飞行器的飞行测试。我们证明了一个单一的DMRAC控制器可以处理显着的非线性，由于严重的系统故障和故意的风干扰，同时执行高带宽的姿态控制。我们还表明，该架构在不同的飞行状态下具有长期的学习能力，并且可以概括为飞行不同的飞行轨迹。这些结果表明，这种架构的高带宽闭环姿态控制的不稳定和非线性的机器人在不利的情况下操作的有效性。为了实现这些结果，我们设计了一个软件+通信架构，以确保在高带宽计算有限的平台上对深度网络进行在线实时推理。我们期望这种架构将有利于机器人闭环实验中的其他深度学习。
英文摘要：
In this paper, we present Asynchronous implementation of Deep Neural Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new neuro-adaptive control architecture through flight tests on a small quadcopter. We demonstrate that a single DMRAC controller can handle significant nonlinearities due to severe system faults and deliberate wind disturbances while executing high-bandwidth attitude control. We also show that the architecture has long-term learning abilities across different flight regimes, and can generalize to fly different flight trajectories than those on which it was trained. These results demonstrating the efficacy of this architecture for high bandwidth closed-loop attitude control of unstable and nonlinear robots operating in adverse situations. To achieve these results, we designed a software+communication architecture to ensure online real-time inference of the deep network on a high-bandwidth computation-limited platform. We expect that this architecture will benefit other deep learning in the closed-loop experiments on robots.
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