Non-linear System Identification from Partial Observations via Iterative Smoothing and Learning

Kunal Menda, Jean de Becdelièvre, Jayesh K Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester

TL;DR: This work presents a scalable algorithm for non-linear offline system identification from partial observations.

Abstract: System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs. It is a key step for model-based control, estimator design, and output prediction. This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable. The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters. We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches. We also use SISL to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.

Code: https://drive.google.com/drive/folders/1M4aOCo5HW9MjibSNJqKnMOZAmFCKovBc?usp=sharing

Keywords: System Identification, Dynamical Systems, Partial Observations, Non-linear Programming, Expectation Maximization, Neural Networks