computational modeling of cerebellar model articulation controller alireza jalali faculty of computer

218 Pages·2015·4.75 MB·English

COMPUTATIONAL MODELING OF CEREBELLAR MODEL
ARTICULATION CONTROLLER

ALIREZA JALALI

FACULTY OF COMPUTER SCIENCE AND INFORMATION
TECHNOLOGY
UNIVERSITY OF MALAYA
KUALA LUMPUR

2015
COMPUTATIONAL MODELING OF CEREBELLAR MODEL
ARTICULATION CONTROLLER

ALIREZA JALALI

THESIS SUBMITTED IN FULFILMENT OF
THE REQUIREMENT FOR THE DEGREE
OF DOCTOR OF PHILOSOPHY

DEPARTMENT OF ARTIFICIAL INTELLIGENCE
FACULTY OF COMPUTER SCIENCE AND INFORMATION
TECHNOLOGY
UNIVERSITY OF MALAYA
KUALA LUMPUR

2015
UNIVERSITI MALAYA

ORIGINAL LITERARY WORK DECLARATION

Name of Candidate: Alireza Jalali (I.C/Passport No: Old: R12477893)
New: P95425769
Registration/Matric No: WHA070021
Name of Degree: PhD
Title of Project Paper/Research Report/Dissertation/Thesis (“this Work”):

Computational Modeling of Cerebellar Model Articulation Controller

Field of Study: Artificial Intelligence

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(1) I am the sole author/writer of this Work;

(2) This Work is original;

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for permitted purposes and any excerpt or extract from, or reference to or
reproduction of any copyright work has been disclosed expressly and sufficiently
and the title of the Work and its authorship have been acknowledged in this Work;

(4) I do not have any actual knowledge nor do I ought reasonably to know that the
making of this work constitutes an infringement of any copyright work;

(5) I hereby assign all and every rights in the copyright to this Work to the
University of Malaya (“UM”), who henceforth shall be owner of the copyright in this
Work and that any reproduction or use in any form or by any means whatsoever
is prohibited without the written consent of UM having been first had and obtained;

(6) I am fully aware that if in the course of making this Work I have infringed any
copyright whether intentionally or otherwise, I may be subject to legal action or any
other action as may be determined by UM.

Candidate’s Signature Date

Subscribed and solemnly declared before,

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ABSTRACT
The cerebellum has major role in the human motor control to coordinate the motion.
The cerebellar model articulation controller is a computational model of the human
cerebellum. This research is towards the study of cerebellar model articulation
controller (CMAC) and its application to non-linear systems. This model of the CMAC
is developed to explore its potential for predictive control of movement.
The main limitation of Cerebellar Model Articulation Controller is memory size in
application for non-linear systems. The size of memory which used by CMAC depends
on input space dimension and input signal quantification step. Therefore, the efficient
utilization of the CMAC memory is a crucial issue. Our main aim is to develop an
optimal CMAC model which decrease memory size and increase the learning accuracy.
To solve the memory size problem of CMAC a model namely Hierarchically Clustered
Fuzzy Cerebellar Model Articulation Controller (HCFCMAC) is proposed. The
performance of the proposed model is simulate and tested to control robotic arm. The
presented simulation results show that proposed model is able to obtain a minimal
modelling error and increase the learning accuracy.
This study is an examination of the HCFCMAC in biped robot control. It addresses
simulations of the cerebellum to control robot swing leg. The proposed method includes
a new concept of footstep planning strategy based on the Semi Online Fuzzy Q-learning
concept for biped robot control in dynamic environments.
The main advantages of proposed approach are that, the computing time is very short
and the footstep planning for both predictable and unpredictable obstacle in dynamic
environment is operational. It will allow the controller to increase the strength. Another
main contribution is on obstacle avoidance strategy for robot in dynamic environment.
In this research the mathematical model of kinematics and dynamic of biped robot are
described. Our approach is on gait pattern planning and control strategy for biped robot
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stepping over dynamic obstacles. The high–level control used to predict the motion of
the robot and the low-level control applied to compute the trajectory of swing leg with
operation of HCFCMAC.

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ABSTRAK (MALAY)
Otak kecil ‘cerebellum’ mempunyai peranan utama dalam kawalan motor manusia
untuk mengawal pergerakan. Pengawal Artikulasi Model Cerebellar adalah model
komputer bagi otak kecil manusia. Dalam penyelidikan ini kita memberi tumpuan
kepada kajian pengawal artikulasi model cerebellar (CMAC) dan aplikasinya pada
sistem bukan linear. Model CMAC Ini dibangunkan untuk meneroka potensinya bagi
kawalan ramalan dan membolehkan siasatan proses ramalan yang berkaitan dengan
otak kecil dalam kawalan pergerakan.
Pengawal Artikulasi Model Cerebellar mempunyai had utama kepada saiz memori
dalam aplikasi untuk sistem bukan linear. Memori yang digunakan oleh CMAC
bergantung kepada dimensi ruang input dan langkah kuantifikasi isyarat input. Oleh itu,
kecekapan penggunaan memori CMAC adalah isu penting. Matlamat kami adalah untuk
mencari CMAC optimum yang membolehkan pengurangan dalam saiz memori dan
masa pengkomputeran. Untuk menyelesaikan masalah saiz memori kami
membentangkan model seni bina CMAC iaitu Pengawal Artikulasi Model Cerebellar
Kabur Berkelompok Hierarki (HCFCMAC). Prestasi rangkaian yang dicadangkan diuji
untuk mengawal lengan robot. Keputusan simulasi yang dibentangkan menunjukkan
bahawa model kami boleh mendapatkan satu ralat model yang minimum.
Kajian ini merupakan pemeriksaan model HCFCMAC dalam kawalan robot. Ia
menangani simulasi otak kecil untuk mengawal ayunan kaki robot dalam persekitaran
dengan halangan, dalam sistem bukan linear. Kaedah ini termasuk strategi perancangan
jejak langkah yang berdasarkan konsep Q-pembelajaran Kabur Semi Online untuk
mengawal robot berkaki dua dalam persekitaran yang dinamik. Keberkesanan kaedah
penyelesaian masalah utama dalam penyelidikan teknologi robot kawalan adalah juga
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merupakan tumpuan utama. Halangan dinamik yang boleh dan tidak boleh diramalkan
yang dihadapi dalam sistem dibincangkan.
Dalam kajian ini juga kami membentangkan satu konsep baru daripada strategi
perancangan jejak langkah yang berdasarkan kepada konsep Q-pembelajaran untuk
robot dalam persekitaran dinamik. Kelebihan utama pendekatan kami adalah tentang
masa pengkomputeran yang sangat pendek dan perancangan jejak langkah itu
beroperasi untuk kedua-dua halangan dinamik yang boleh dan tidak boleh diramalkan,
membolehkan sistem kawalan dalam meningkatkan kekuatan. Satu lagi sumbangan
utama ialah tentang strategi untuk mengelakkan halangan bagi robot dalam persekitaran
yang dinamik. Strategi kawalan bagi pergerakan robot berkaki dua perlu dibahagikan
terutamanya kepada dua kategori. Dalam kajian ini kita memberi tumpuan kepada
model mekanikal kinematik dan dinamik bagi model berkaki dua. Di sini kita mengkaji
model pergerakan bagi robot lima-link berkaki dua. Pendekatan kami adalah mengenai
perancangan corak gaya berjalan dan strategi kawalan bagi robot berkaki dua
melangkah halangan dinamik. Ia boleh dibuat untuk berfungsi secara berasingan untuk
kawalan tahap tinggi dan kawalan tahap rendah. Kami memberi tumpuan kepada
kawalan peringkat tinggi untuk meramalkan pergerakan robot. Untuk kawalan dalam
talian masa pengkomputeran bagi proses pembelajaran adalah satu parameter kritikal.
Untuk mengurangkan masa pengiraan, peringkat pembelajaran bagi perancangan
langkah kaki digunakan untuk mereka bentuk strategi kawalan peringkat tinggi. Kita
mengkaji kawalan peringkat rendah untuk mengira trajektori ayunan kaki dari output
HCFCMAC.

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Acknowledgements
I would like to begin with my sincere gratitude to the current Dean of the faculty,
former Deans and the staff members for providing the support and assistance during this
research. I would also like to express my gratitude to the former Deputy Dean Assoc.
Prof. Dr. Diljit Singh for his invaluable advice and encouragement.
Most importantly and dearest to my heart, I am indebted to my supervisor Prof. Dr.
Roziati Zainuddin and co-supervisor Dr. Woo Chaw Seng for their invaluable support,
encouragement, guidance, corrections, reviews and motivations from initial stage to the
final thesis compilation to ensure the success of this study.
I am also taking this opportunity to thank both the past and present members of the
Artificial Intelligence Laboratory University of Malaya for their generous support, and
the individuals who have contributed their voice for this research.
Finally I like to thank my beloved wife Marzieh Yaeghoobi, She was always there
cheering me up and stood by me through the good times and bad as well all of my
family members, who have made enumerable sacrifices to allow me to pursue my
research goals.
May the Almighty God richly bless all of you.

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TABLE OF CONTENTS

ABSTRACT ...................................................................................................................... ii
ABSTRAK (MALAY) .................................................................................................... iv
ACKNOWLEDGEMENTS ............................................................................................. vi
TABLE OF CONTENTS ................................................................................................ vii
LIST OF FIGURES ........................................................................................................ xii
LIST OF TABLES ......................................................................................................... xvi
LIST OF ABBREVIATIONS AND ACRONYMS ..................................................... xvii

1.0. INTRODUCTION .................................................................................................. 1
1.1. Research Inspiration and Background ................................................................ 1
1.1.1 Intelligent Control ............................................................................................ 1
1.1.2. The Cedrebellum ............................................................................................ 3
1.1.2.1. Inputs ................................................................................................. 4
1.1.2.2. Outputs .............................................................................................. 5
1.1.2.3. Mossy Fibers and Purkinje Cells ....................................................... 6
1.1.2.4. Cerebellar Cortex............................................................................... 7
1.1.2.5. Cerebellum Role ................................................................................ 9
1.1.2.6. Cerebellar Model ............................................................................. 10
1.1.2.7. Cerebellar Learning ......................................................................... 10
1.1.3. Neural Network .............................................................................................. 12
1.1.4. Reinforcement Learning ................................................................................ 14
1.2. Focus and Scope ............................................................................................... 16
1.3. Research Problem ............................................................................................. 16
1.3.1 Issues On Cerebellar Model Articulation Controller .................................... 16
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1.3.2 Issues On Path Planning and Q-learning ...................................................... 16
1.4. Research Main Aim and Objectives ................................................................ 17
1.5. Foundamental Research Questions .................................................................. 18
1.6. Research Contribution ...................................................................................... 19
1.7. Thesis Outline .................................................................................................. 20

2.0. TIMELINE OF ROBOTIC CONTROL ...................................................... 22
2.1. Timeline of Development of Robot Control .................................................... 22
2.2. Human Thought and Behavior Processes ......................................................... 24
2.3. Artificial Inteligence ........................................................................................ 27
2.4. Cerebellar Model Articulation Controller (CMAC) and Q-learning Theory ... 30
2.5. Reinforcement Learning Theory ...................................................................... 31
2.6. Dynamic Enviroments ...................................................................................... 38
2.7. Summary .......................................................................................................... 39

3.0. STRUCTURE OF CEREBELLAR MODEL ARTICULATION
CONTROLLER (CMAC) AND Q-LEARNING ......................................... 40
3.1. Introduction ...................................................................................................... 40
3.2. Cerebellar Model Articulation Controller as a Model of the Human
Cerebellum ....................................................................................................... 42
3.2.1. Important Basics of CMAC ......................................................................... 44
3.3. Q-learning ......................................................................................................... 47
3.4. Summary .......................................................................................................... 52

4.0. DESIGN OF HIERARCHICALLY CLUSTERED FUZZY CMAC AND
ADAPTIVE CONTROL OF ROBOTIC ARM .............................................. 53
4.1 Introduction ...................................................................................................... 53
4.2 Design of Fuzzy cerebellum model articulation controller .............................. 55
4.3. The Hierarchically Clustered Fuzzy Cerebellum Model Articulation Controller algorithm ... 59
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