hadar933 / thesis

A Deep Inverse dynamic Model, and its Application on Flapping-Wing Micro Aerial Vehicles

A Deep Inverse dynamic Model, and its Application for Flapping-Wing Micro Aerial Vehicles

Abstract

Deep learning frameworks have become indispensable for modeling complex relationships within multi-dimensional time-dependent data across diverse domains. One useful application of these frameworks is in mechanical systems forces and torques forecasting, such as predicting the lift forces generated by a flapping wing system when it follows a predefined stroke profile. This approach, which we refer to as "forward" dynamics modeling, typically involves learning a mapping from movements to forces that were generated by these movements. This work, however, focuses primarily on a distinct task that we term "inverse" dynamics modeling, where the objective is to understand the motion that generates a given force value. For instance, given a desired lift force, the goal is to find the wing movement that will produce it. This task is particularly useful from a control perspective, as it is akin to specifying the desired force outcome and having a model handle the details of achieving it. We achieve this using deep learning time series and enhance the known time-attention-based sequence-to-sequence network (seq2seq) with a novel neural network layer, the Adaptive Spectrum Layer, which learns weights in Fourier space. We evaluate our model using two datasets: one derived from our proprietary mechanical model and measurement methodology, and another open-sourced. Our model's performance, assessed in terms of Mean Absolute Error (MAE), demonstrates comparable performance with state-of-the-art, and up to 11% improvement in MAE in some cases. The contribution of our work is in the simplification and optimization the control aspects of these complex mechanical systems.

---

This repo contains all the code that was used for my thesis

• Camera: Image processing for wing tracking in 3D and Euler angles extraction
• Forces: Force sensors signal parser
• DataHandler: Data merging, encoding, and preprocessing
• ML: Multi-dataset multivariate time series framework for inverse physical modeling