Identification of Legged Locomotion via Model-Based and Data-Driven Approaches

TL;DR

This thesis combines experimental validation of mechanics-based models with data-driven system identification techniques to improve understanding and modeling of legged locomotion, specifically focusing on a one-legged hopping robot.

Contribution

It introduces a hybrid approach that validates analytical models experimentally and develops new data-driven methods for identifying state space models of legged locomotion systems.

Findings

Validated predictive performance of spring-mass models on a real robot.

Developed a state space identification method for hybrid LTP systems.

Extended identification techniques to unknown stable LTP systems using subspace methods.

Abstract

In the first part of this thesis, we present our efforts on experimental validation of the predictive performance of mechanics-based mathematical models on a physical one-legged hopping robot platform. We extend upon a recently proposed approximate analytical solution developed for the lossy spring--mass models for a real robotic system and perform a parametric system identification to carefully identify the system parameters in the proposed model. We also present our assessments on the predictive performance of the proposed approximate analytical solution on our one-legged hopping robot data. The second part considers estimating state space models of legged locomotion using input--output data. To accomplish this, we first propose a state space identification method to estimate time periodic state and input matrices of a hybrid LTP system under full state measurement assumption. We then release this assumption and proceed with subspace identification methods to estimate LTP state space realizations for unknown stable LTP systems. We utilize bilinear (Tustin) transformation and frequency domain lifting methods to generalize our solutions to different LTP system models. Our results provide a basis towards identification of state space models for legged locomotion.