论文标题
异步深度模型参考自适应控制
Asynchronous Deep Model Reference Adaptive Control
论文作者
论文摘要
在本文中，我们介绍了基于深神网络的模型参考自适应控制（DMRAC）的异步实现。我们通过小四轮驱动器的飞行测试评估了这种新的神经自适应控制架构。我们证明，由于严重的系统故障和故意的风干扰，单个DMRAC控制器可以在执行高带宽态度控制的同时处理重要的非线性。我们还表明，该体系结构具有不同飞行制度的长期学习能力，并且可以概括地驾驶不同的飞行轨迹，而不是受过训练的轨迹。这些结果证明了这种体系结构对在不良情况下运行的不稳定和非线性机器人的高带宽闭环态度控制的功效。为了实现这些结果，我们设计了一个软件+通信体系结构，以确保在高带宽计算限制平台上对深层网络的在线实时推断。我们预计，这种体系结构将使机器人的闭环实验中的其他深入学习受益。
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.
