论文标题
使用基于DNN的MPC与FPGA实现的对峙跟踪
Standoff Tracking Using DNN-Based MPC with Implementation on FPGA
论文作者
论文摘要
这项工作研究了僵持的跟踪问题，以驱动无人机（UAV）以恒定高度在移动目标上滑到所需的圆圈上。我们提出了一个新颖的Lyapunov引导矢量（LGV）领域，具有无人机轨迹计划的可调收敛速率和基于深神经网络（DNN）基于基于的模型预测控制（MPC）方案，以跟踪参考轨迹。然后，我们展示了如何收集样品以训练DNN离线并设计一个积分模块（IM），以完善基于DNN的MPC的跟踪性能。此外，使用FPGA@200MHz的硬件中的硬件（HIL）仿真表明，我们的方法是嵌入MPC实现的有效替代方法，用于解决复杂的系统和应用程序，这对于直接解决MPC优化问题是不可能的。
This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance vector (LGV) field with tunable convergence rates for the UAV's trajectory planning and a deep neural network (DNN)-based model predictive control (MPC) scheme to track the reference trajectory. Then, we show how to collect samples for training the DNN offline and design an integral module (IM) to refine the tracking performance of our DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with an FPGA@200MHz demonstrates that our method is a valid alternative to embedded implementations of MPC for addressing complex systems and applications which is impossible for directly solving the MPC optimization problems.
