Flapping-wing micro air vehicles (FWMAVs) present a significant control challenge due to their complex nonlinear dynamics and severe hardware constraints, which preclude the use of computationally intensive controllers. This thesis addresses this challenge by developing and validating a pipeline to convert a high-performance neural network policy, trained via Reinforcement Learning (RL), into a sparse, hardware-efficient symbolic controller using the Sparse Identification of Nonlinear Dynamics (SINDy) framework. The primary contribution of this work is the introduction and evaluation of novel, hardwareaware optimizations within the SINDy distillation process. Specifically, we introduce Sparse Bit Quantization (SBQ), a new quantization scheme that represents coefficients as combinations of powers of two to enable efficient implementation using bit-shift operations on an FPGA. We systematically analyze the impact of applying SBQ both post-training and during the optimization loop (Quantization-Aware Training), and further explore the use of a custom, hardware-efficient function library designed to map directly to DSP block structures. The complete pipeline was validated on the ‘Pendulum-v1‘ benchmark. Our results demonstrate that while standard SINDy can accurately approximate the RL teacher policy, our hardware-oriented function library, struggles to capture the full complexity of the control task. This highlights a key trade-off between hardware-efficiency and model expressiveness. This work serves as a successful proof-of-concept and contributes novel techniques essential for deploying modern control algorithms on resource-constrained robotic systems.