Robot learning and supervisory control of a human powered

ROBOT LEARNING AND SUPERVISORY CONTROL OF A HUMAN-POWERED AUGMENTATION LOWER EXOSKELETON HUALEX A Doctoral Dissertation Submitted to University of Electronic Science and Technology of

'41 UDC

10),

h

-T=

Robot Learning and Supervisory Control of a Human-Powered Augmentation Lower Exoskeleton

(HUALEX)

TRAN HUU TOAN

(11T-4.41Z, )

( eP 1ST f`J- )

1I-11± \11/ Eti -f ft '1 173"43 71)c- Te.:1,1 EI

H

1117-1RJ A

P-1, -1-43YkLA UDC))

Công

toàn

lòng

liên hệ

phòng

Dịch

vụ thông

tin - Th

ư vi ện

ROBOT LEARNING AND SUPERVISORY CONTROL

OF A HUMAN-POWERED AUGMENTATION LOWER EXOSKELETON (HUALEX)

A Doctoral Dissertation Submitted to

University of Electronic Science and Technology of China

Major:

Author:

Advisor:

School :

Electronic Science and Technology

Tran Huu Toan

Professor Cheng Hong

School of Automation

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

*Ar-i•kPfi-T Zntifc_At*At-±-WIA-UT344-in4JFYstii

fFalfRf4R4 RE9-MT MVMH, Rflc_11:41-?31.1M1=WifriRiA tnith h*, Ttt.1=1,4-thAER-tiA/A'411-nwlimu., -L1/02*

ts) 140

TrAft:

Fin]: 201 F 03A 30 H

*-1-4S-LikScfrt5t4-714 Tf4RA*A XNM

A *MTjIA111*-AX-A3r19-14nfzig3see_ii6cMkrPiftfriEt,

AAjaidEltaffitt, II=AffltFP

qffifg#, UN4.4-1PLikk

(AWAIff-v.'-V6cAfriMoitz1V f-f-LAI)

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

11- L :14i A rri flP A L 10.1 Id V IP fli.Y i'J5 II 7-1KSctl-A - 1W.0:01-A-M.1)1M A E"):-,:13ji4-,_,7.ifi

r(E*30i TOM r-11 4.4-911k

111‘i I ) • MA! JIJ lJ ft.110.7.1131,1c'A ( Fuzzy-based Impedance Control strategy, FV IC ; 2) h" j 12_.< -M RIK ( Model-Learn i ng- based

Partitioned Control strategy, M L PC )

71K•Vi?, Ei-i Ifj

IttjA,IV-iilelk:NAY12.1,'J.J11=f-l'fM A ±6, 1 0.1111.13Z11'

IMaisi,if.fi IA kit lYjIJJt, ,N-gklEffcrellillj-8 ,t72:'31t-rt)537:Fil M 1:1:112:)] -.5 A 11 L rrim,113.wflYL-N: A LE 7 ft g .911.I 11<i It TA :157._ 'dill

-mitgurn iitFvk-s-h

fiL Afg

IN*.fila A ;1917;._'±ii:i

fl-r-rtiVil)LMAMAITLIFLf:R Wfr A 1LIrJJ,i.L.411111,313z1-d1)=11.0.42,:gsf A VizZ_:

31.(141 WA' 733' IX I'S-ffif A=A- K-811i r-P ;01 A 174i, fg ,/fR 5f7 tVgarg

fitfirkitha7371-191:i U.1 f:g1ErIJ *-$11.TSI:

tt.t*O.R41MAii).03*-41;9.111:4TitT

Mfil'IVii1W/VE 71-17-1

r

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

ABSTRACT

ABSTRACT

Developing wearable devices for human-powered support has been a long-standing dream in the intelligent robotic field These wearable robots bring a numerous practical

applications to human assistance and augmentation in daily life, yet pose many open

problems relating to anthropomorphic design and advanced control Inspired by these

challenges, the context of this dissertation is the emergence of powered lower

exoskeletons capable of augmenting the human muscle force and supporting

load-carrying, where exoskeletons can be worn by human-operators and need adaptive

capabilities to work in compliance with human Two advanced control strategies have

been developed to provide the exoskeletons with the ability to be more flexible, easier

to integrate into users, and more intelligent to situational awareness The first approach

is a newly fuzzy-based impedance control strategy proposed to provide assistive torques

by regulating the desired impedance between the exoskeleton and a wearer's lower limb

according to a specific motion speed The effect of human behaviors on impedance

parameters changes is adopted for the fuzzy rules designed to increase the exoskeleton's

adaptation ability over a specific range of different walking speeds

Before introducing the second control strategy, we present our investigations into the relationship between the physical interaction torques and the dynamic factors of the

human-exoskeleton systems using state-of-the-art learning techniques Consequently, a

novel model-learning-based partitioned control of the exoskeleton is proposed in which

the dynamics of the combined human-exoskeleton system along with the corresponding

resulting interaction torques are learned based on nonparametric regression technique

and then incorporated into the control system Compared to state-of-the-art control

methods of the exoskeletons, this combination of incremental model learning and

partitioned control scheme can provide the robot with the ability to adapt various

dynamics of human operators, to reduce the physical interaction between the operator

and exoskeleton, and to minimize the sensory system used in the system simultaneously

As a contribution to robotic exoskeletons field, we present an original prototype of Human-powered Augmentation Lower Exoskeleton (HUALEX) through the analysis of

human factors engineering, biomechanics, and dynamics properties The above control

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

ABSTRACT

strategies are evaluated on this platform through several proposed performance indexes

as fundamental criteria in the lower exoskeleton field for hinnan augmentation A

comparison of these strategies is discussed to present our future work The long-term

objective of this dissertation is to develop powered wearable lower exoskeletons such

that they will be low cost, simplified and portable intelligent robots capable of

augmenting the human muscle force and adapting to various individuals flexibly

Keywords: Lower Limb Exoskeleton, Physical Human-Robot Interaction, Impedance

Control, Fuzzy Logic, Model Learning, Non-parametric Regression

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

Contents

Contents

Chapter L Introduction 1.1 Motivation

1.2 Research background and dissertation approach 4

2.2 Kinematics and Dynamics of Lower Exoskeleton 17 2.3 Fuzzy Logic

Chapter 3 The Human-powered Augmentation Lower Exoskeleton System 26

3.2 Human-powered Augmentation Lower Exoskeleton (HUALEX) 29

Chapter 4 Fuzzy-based Impedance Regulation for Control of the Coupled

4.2.1 Model of the coupled human-exoskeleton system 39 4.2.2 Estimation of physical human-exoskeleton parameters 40

4.3.1 Impedance control of the exoskeleton while interacting with human 42 4.3.2 Frequency analysis according to impedance parameters 44 4.3.3 Fuzzy-based variable impedance control 45

IV

Công

toàn

lòng

liên hệ

phòng

Dịch

vụ thông

tin - Th

ư vi ện

Contents

Chapter 5 Evaluation of a Fuzzy-based Impedance Control Strategy on a Powered

5.1.2 Impedance control of an exoskeleton robot 54 5.2 Fuzzy-based variable impedance control of the coupled human-exoskeleton system

57 5.2.1 Biomechanics characteristics of human legs at different walking speeds 57 5.2.2 Impedance control of the exoskeleton under interacting with human 60 5.3 Fuzzy-based impedance regulation according to walking phases 62

5.4.4 Load transfer through the lower exoskeleton 81

Chapter 6 The Relationship between Physical Human- Exoskeleton Interaction

and Dynamic Factors: Using a Learning Approach for Control Applications 84

6.3.1 Model of the combined human-exoskeleton system 87 6.3.2 Representation of the interaction resulting from the human 88 6.4 Supervised learning with nonparametric regression techniques 90

6.4.1 Statistical learning of nonlinear mapping of the RPIT 90

6.4.3 Locally Weighted Projection Regression (LWPR) 91

6.5.2 Experiments with a simple master-slave control 95 6.6 Control applications based on the learned models 96

6.6.1 Variable impedance control with the learned interaction model 97 6.6.2 Partitioned control with learned dynamics-interaction model 97

Chapter 7 Learning Dynamic Model of a Lower Exoskeleton Interacting with

7.2 Model-learning-based partitioned control strategy (MLPC) 103

V

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

Contents

7.2.1 Control of lower exoskeletons with model-based approach 104 7.2.2 Key features of human-robot model learning 106

7.4 Evaluation of the proposed algorithm in computer simulations Ill

8.3.3 Motion modes and transitions recognition of a hybrid human-exoskeleton

Research Results Obtained During the Study for PhD Degree 146

APPENDIX A Control loop flowchart of fuzy-based variable impedance control

APPENDIX B Control loop flowchart of online model-learning based partitioned

Công

toàn

lòng liên

Dịch

vụ thông

tin - Th

ư vi ện

List of Figures

List of Figures

Figure 3-7 Custom-built force sensors for measuring the resulting interaction force from

Figure 4-2 One of the trials for parameter estimation of lower thigh of

Figure 4-3 Principle of the frizzy-based regulated impedance control for the lower

Figure 5-1 Control of motion and human-robot interaction for lower exoskeletons 55

Figure 5-2 The resulting interaction forces Fhl; Flo from hip and knee torques at the

vu

Công

toàn

lòng

liên hệ

phòng

Dịch

vụ thông

tin - Th

ư vi ện