Nonlinear model predictive control (NMPC) has proven to be an effective control method, but it is expensive to compute. This work demonstrates the use of hardware FPGA neural network controllers trained to imitate NMPC with supervised learning. We use these Neural Controllers (NCs) implemented on inexpensive embedded FPGA hardware for high frequency control on physical cartpole and F1TENTH race car. Our results show that the NCs match the control performance of the NMPCs in simulation and outperform it in reality, due to the faster control rate that is afforded by the quick FPGA NC inference. We demonstrate kHz control rates for a physical cartpole and offloading control to the FPGA hardware on the F1TENTH car. Code and hardware implementation for this paper are available at https:// github.com/SensorsINI/Neural-Control-Tools.

翻译：非线性模型预测控制（NMPC）已被证明是一种有效的控制方法，但其计算成本高昂。本研究展示了使用硬件FPGA神经网络控制器，通过监督学习训练以模仿NMPC。我们将这些在低成本嵌入式FPGA硬件上实现的神经控制器（NCs）用于物理CartPole和F1TENTH赛车的高频控制。结果表明，在仿真中NCs与NMPCs的控制性能相当，而在实际应用中，由于快速的FPGA NC推理所提供的更高控制频率，NCs的表现优于NMPC。我们展示了物理CartPole的kHz级控制频率，以及在F1TENTH赛车上将控制任务卸载至FPGA硬件的过程。本文的代码与硬件实现可在 https://github.com/SensorsINI/Neural-Control-Tools 获取。