Identification of legged locomotion via model-based and data-driven approaches
Author(s)
Advisor
Date
2017-05Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Robotics is one of the core areas where the bioinspiration is frequently used
to design various engineered morphologies and to develop novel behavioral controllers
comparable to the humans and animals. Biopinspiration requires a solid
understanding of the functions and concepts in nature and developing practical
engineering applications. However, understanding these concepts, especially
from a human or animal point of view, requires the signi cant use of mathematical
modeling and system identi cation methods. In this thesis, we focus on
developing new system identi cation methods for understanding legged locomotion
models towards building better legged robot platforms that can locomote
e ectively as their animal counterparts do in nature.
In the rst part of this thesis, we present our e orts 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 identi cation 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. Experiments with di erent leg
springs and cross validation of results yield that our approximate analytical solutions
provide a su ciently accurate representation of the physical robot platform.
In the second part, we adopt a data-driven approach to obtain an input{output
representation of legged locomotion models around a stable periodic orbit (a.k.a.
limit cycle). To this end, we rst linearize the hybrid dynamics of legged locomotor
systems around a limit cycle to obtain a linear time periodic (LTP) system representation. Hence, we utilize the frequency domain analysis and identi cation
methods for LTP systems towards the identi cation of input{output models
(harmonic transfer functions) of legged locomotion. We propose simulation experiments
on simple legged locomotion models to illustrate the prediction performance
of the estimated input{output models.
Finally, the third part considers estimating state space models of legged locomotion
using input{output data. To accomplish this, we rst propose a state
space identi cation 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 identi cation 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 di erent LTP system models. Our results provide a basis towards
identi cation of state space models for legged locomotion.
Keywords
System identi cationLegged locomotion
Mathematical models
Spring-loaded inverted pendulum (SLIP) model
Linear time periodic systems
Harmonic transfer functions
Subspace identi cation