Deep Photonic Reservoir Computer Meets UAV Control: An ultra-fast learning-based compensator for agile flight in confined space

TL;DR

This paper introduces a deep photonic reservoir computer integrated with UAV control, achieving ultra-fast learning and inference to improve flight stability in confined, cluttered environments.

Contribution

It pioneers the use of a hardware-implemented deep photonic reservoir computer for real-time UAV dynamic compensation, reducing training time and inference latency significantly.

Findings

Deep PRC achieves residual-force prediction accuracy comparable to or better than TCN/MLP.

Training time reduced from hours to milliseconds; inference latency to nanoseconds.

Enhanced closed-loop tracking stability in confined spaces.

Abstract

Unmanned aerial vehicles (UAVs) operating in confined, cluttered environments face significant performance degradation due to nonlinear, time-varying unmodeled dynamics-such as ground/ceiling effects and wake recirculation-that are unaccounted for in traditional controllers. While learning based compensators (e.g., MLPs, TCNs, LSTMs) struggle with historical data dependency, vanishing gradients, and prohibitive computational costs, this work pioneers the integration of a deep photonic reservoir computer (PRC) with feedforward control to overcome these limitations. Harnessing semiconductor laser dynamics and optical feedback, our hardware implemented deep PRC architecture achieves intrinsic temporal memory without explicit historical inputs, while reducing training time from hours to milliseconds and slashing inference latency to nanoseconds. Reliable high-performance CFD simulations capturing proximity-induced flows demonstrate that deep PRC delivers residual-force prediction accuracy comparable to or exceeding TCN/MLP baselines, while training only a linear readout layer via ridge regression. By injecting these predictions into a nonlinear feedback PID controller via a feedforward channel, the framework significantly enhances closed-loop tracking stability in confined spaces. Essentially, this work establishes the first deep PRC-based lightweight, ultrafast solution for real-time UAV dynamic compensation, with promising extensibility to unseen scenarios with more complex fluid environments.