Automotive control systems and vehicles : Intelligent unmanned systems

Intelligent Systems, Control and Automation:

Science and Engineering

Autonomous

Control Systems

and Vehicles

Kenzo Nonami

Muljowidodo Kartidjo

Kwang-Joon Yoon

Agus Budiyono Editors

Intelligent Unmanned Systems

Autonomous Control Systems and Vehicles

International Series on

INTELLIGENT SYSTEMS, CONTROL AND AUTOMATION:

SCIENCE AND ENGINEERING

VOLUME 65

Editor

Professor S. G. Tzafestas, National Technical University of Athens, Greece

Editorial Advisory Board

Professor P. Antsaklis, University of Notre Dame, Notre Dame, IN, USA

Professor P. Borne, Ecole Centrale de Lille, Lille, France

Professor D.G. Caldwell, University of Salford, Salford, UK

Professor C.S. Chen, University of Akron, Akron, Ohio, USA

Professor T. Fukuda, Nagoya University, Nagoya, Japan

Professor S. Monaco, University La Sapienza, Rome, Italy

Professor G. Schmidt, Technical University of Munich, Munich, Germany

Professor S.G. Tzafestas, National Technical University of Athens, Athens, Greece

Professor F. Harashima, University of Tokyo, Tokyo, Japan

Professor N.K. Sinha, McMaster University, Hamilton, Ontario, Canada

Professor D. Tabak, George Mason University, Fairfax, Virginia, USA

Professor K. Valavanis, University of Denver, Denver, USA

For further volumes:

http://www.springer.com/series/6259

Kenzo Nonami • Muljowidodo Kartidjo

Kwang-Joon Yoon • Agus Budiyono

Editors

Autonomous Control

Systems and Vehicles

Intelligent Unmanned Systems

Editors

Kenzo Nonami

Graduate School and Faculty

of Engineering

Chiba University

Chiba, Japan

Muljowidodo Kartidjo

Center for Unmanned

System Studies

Institute of Technology Bandung

Bandung, Indonesia

Kwang-Joon Yoon

Konkuk University

Seoul, Korea, Republic

of (South Korea)

Agus Budiyono

Department of Aerospace and Information

Engineering, Smart Robot Center

Konkuk University

Seoul, Korea, Republic of (South Korea)

ISBN 978-4-431-54275-9 ISBN 978-4-431-54276-6 (eBook)

DOI 10.1007/978-4-431-54276-6

Springer Tokyo Heidelberg New York Dordrecht London

Library of Congress Control Number: 2013936546

# Springer Japan 2013

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Preface

The International Conference on Intelligent Unmanned Systems (ICIUS) 2011 was

organized by the International Society of Intelligent Unmanned Systems (ISIUS)

and locally by the Center for Bio-Micro Robotics Research at Chiba University,

Japan. The event was the 7th conference continuing from previous conferences held

in Seoul, Korea (2005, 2006), Bali, Indonesia (2007), Nanjing, China (2008), Jeju,

Korea (2009), and Bali, Indonesia (2010). ICIUS2011 focused on both theory and

application, primarily covering the topics of robotics, autonomous vehicles, intelli￾gent unmanned technologies, and biomimetics. We invited seven keynote speakers

who dealt with related state-of-the-art technologies including unmanned aerial

vehicles (UAVs) and micro air vehicles (MAVs), flapping wings (FWs), unmanned

ground vehicles (UGVs), underwater vehicles (UVs), bio-inspired robotics,

advanced control, and intelligent systems, among others. Simultaneously, the

exhibition and demonstrations and the panel discussion were arranged to cover

advanced relevant technologies in this field. The aim of the conference was to

stimulate interactions among researchers active in the areas pertinent to intelligent

unmanned systems.

This special-interest conference successfully attracted 113 papers internation￾ally, covering the following topics:

• Unmanned systems: UAVs, MAVs, unmanned marine vehicles (UMVs), under￾water vehicles (UVs), multi-agent systems, UGVs, blimps, swarm intelligence,

autonomous flying robots (AFRs), and flapping robots (FRs)

• Robotics and biomimetics: smart sensors, design and applications of MEMS/

NEMS, intelligent robot systems, evolutionary algorithms, control of biological

systems, biological learning control systems, neural networks, and bioinformatics

• Control and computation: distributed and embedded systems, embedded intelli￾gent control, complex systems, pervasive computing, discrete event systems,

hybrid systems, networked control systems, delay systems, identification

and estimation, nonlinear systems, precision motion control, control applic￾ations, computer architecture and VLSI, signal/image and multimedia

v

processing, software-enabled control, real-time operating systems, architecture

for autonomous systems, software engineering for real-time systems, and

real-time data communications

• Context-aware computing intelligent systems: soft computing, ubiquitous com￾puting, distributed intelligence, and distributed/decentralized intelligent control

ICIUS2011 was strongly supported by IEEE, the Japan Society of Mechanical

Engineers (JSME), the Society of Instrument and Control Engineers (SICE), the

Institute of Systems, Control and Information Engineers (ISCIE), Robotics Society

of Japan (RSJ), the Japan Society for Aeronautical and Space Sciences (JSASS),

Japan UAV Association (JUAV), the Chiba Convention Bureau and International

Center, and the NSK Mechatronics Technology Advancement Foundation, Chiba

University. On behalf of the organization committee, we would like to express our

appreciation for the support provided by those organizations. We would also like to

use this opportunity to thank all individuals and organizations who contributed to

making ICIUS2011 successful and memorable.

This book is a collection of excellent papers that were updated after presentation

at ICIUS2011. The evaluation committee of ICIUS2011 finally decided to select a

total of 21 of those papers including the keynote papers. All papers that form the

chapters of this book were reviewed and revised from the perspective of advanced

relevant technologies in the field. The book is organized into four parts, which

reflect the research topics of the conference themes:

Part 1: Trends in Intelligent and Autonomous Unmanned Systems

Part 2: Trends in Research Activities of UAVs and MAVs

Part 3: Trends in Research Activities of UGVs

Part 4: TrendsinResearch Activities of Underwater Vehicles,Micro Robots, and Others

One aim of this book is to stimulate interactions among researchers in the areas

pertinent to intelligent unmanned systems of UAV, MAV, UGV, USV, and UV,

namely, autonomous control systems and vehicles. Another aim is to share new

ideas, new challenges, and the authors’ expertise on critical and emerging technologies.

The book covers multifaceted aspects of intelligent unmanned systems.

The editors hope that readers will find this book not only stimulating but also

useful and usable in whatever aspect of unmanned system design in which they may

be involved or interested. The editors would like to express their sincere appreciation

to all the contributors for their cooperation in producing this book. The contribution

from the keynote speakers is gratefully acknowledged, and all authors are to be

congratulated for their efforts in preparing such excellent chapters. Finally, the

publisher, Springer, and most importantly Ms. Y. Sumino and Ms. T. Sato have

been extremely supportive in the publication of this book. We especially want to

thank Ms. Sumino and Ms. Sato for their contribution.

Chiba, Japan Kenzo Nonami

Bandung, Indonesia Muljowidodo Kartidjo

Seoul Korea, Republic of (South Korea) Kwang-Joon Yoon

Seoul Korea, Republic of (South Korea) Agus Budiyono

vi Preface

Contents

Part I Trends of Intelligent and Autonomous Unmanned Systems

1 Flight Demonstrations of Fault Tolerant Flight

Control Using Small UAVs ............................... 3

Shinji Suzuki, Yuka Yoshimatsu, and Koichi Miyaji

2 Unmanned Aerial and Ground Vehicle Teams:

Recent Work and Open Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Steven L. Waslander

3 Cognitive Developmental Robotics: from Physical

Interaction to Social One . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Minoru Asada

Part II Trends on Research Activities of UAVs and MAVs

4 Towards a Unified Framework for UAS Autonomy

and Technology Readiness Assessment (ATRA) . . . . . . . . . . . . . . . 55

Farid Kendoul

5 Control Scheme for Automatic Takeoff

and Landing of Small Electric Helicopter . . . . . . . . . . . . . . . . . . . . 73

Satoshi Suzuki

6 Evaluation of an Easy Operation System

for Unmanned Helicopter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Masafumi Miwa, Shouta Nakamatsu, and Kentaro Kinoshita

7 Control of Ducted Fan Flying Object Using

Thrust Vectoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Masafumi Miwa, Yuki Shigematsu, and Takashi Yamashita

vii

8 Circular Formation Control of Multiple Quadrotor

Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

M. Fadhil Abas, Dwi Pebrianti, Syaril Azrad, D. Iwakura,

Yuze Song, and K. Nonami

9 Decentralised Formation Control of Unmanned

Aerial Vehicles Using Virtual Leaders . . . . . . . . . . . . . . . . . . . . . . 133

Takuma Hino and Takeshi Tsuchiya

10 Aerodynamics and Flight Stability of Bio-inspired,

Flapping-Wing Micro Air Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . 145

Hao Liu, Xiaolan Wang, Toshiyuki Nakata, and

Kazuyuki Yoshida

11 Development and Operational Experiences of

UAVs for Scientific Research in Antarctica . . . . . . . . . . . . . . . . . . 159

S. Higashino, M. Funaki, N. Hirasawa, M. Hayashi

and S. Nagasaki

12 Circularly Polarized Synthetic Aperture Radar

Onboard Unmanned Aerial Vehicle (CP-SAR UAV) . . . . . . . . . . . 175

Josaphat Tetuko Sri Sumantyo

Part III Trends on Research Activities of UGVs

13 Modeling and Control of Wheeled Mobile Robots:

From Kinematics to Dynamics with Slipping and Skidding . . . . . . 195

Makoto Yokoyama

14 Consideration of Mounted Position of Grousers

on Flexible Wheels for Lunar Exploration Rovers

to Traverse Loose Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Kojiro Iizuka

15 Optimal Impedance Control with TSK-Type FLC

for Hard Shaking Reduction on Hydraulically Driven

Hexapod Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Addie Irawan, Kenzo Nonami, and Mohd Razali Daud

16 LRF Assisted Autonomous Walking in Rough Terrain

for Hexapod Robot COMET-IV . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

M.R. Daud, K. Nonami, and A. Irawan

17 Walking Directional Control of Six-Legged Robot

by Time-Varying Feedback System . . . . . . . . . . . . . . . . . . . . . . . . . 251

H. Uchida and N. Shiina

viii Contents

Part IV Trends on Research Activities of Underwater

Vehicle, Micro Robot and Others

18 Design and Operation Analysis of Hybrid AUV . . . . . . . . . . . . . . . 267

K. Muljowidodo, Sapto Adi Nugroho, and Nico Prayogo

19 Ultrasound Energy Transmission for WaFLES-Support

Intra-abdominal Micro Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279

Takuya Akagi, David Gomez, Jose Gonzalez,

Tatsuo Igarashi, and Wenwei Yu

20 Simulation of Supercavitating Flow Accelerated by Shock . . . . . . . 291

B.C. Khoo and J.G. Zheng

21 Dynamics of Vortices Shed from an Elastic Heaving

Thin Film by Fluid–Structure Interaction Simulation . . . . . . . . . . 299

Tetsushi Nagata, Masaki Fuchiwaki, and Kazuhiro Tanaka

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Contents ix

Part I

Trends of Intelligent and Autonomous

Unmanned Systems

Chapter 1

Flight Demonstrations of Fault Tolerant Flight

Control Using Small UAVs

Shinji Suzuki, Yuka Yoshimatsu, and Koichi Miyaji

Abstract Fault tolerant flight control (FTFC) has been investigated to increase

aviation safety. FTFC adaptively changes control devices, control algorithms, or

control parameters in order to regulate the change of dynamic characteristics of

aircraft when malfunction of control system or airframe structure failure occurs

during the flight. It is difficult to carry out the flight demonstration of FTFC for an

aircraft with pilot operation since the airworthiness should be satisfied even in the

failure cases. The use of unmanned air vehicles (UAVs) is suitable for this purpose.

The authors’ team has been demonstrating the FTFC based on neural networks

(NNs) using a small UAVs.

Keywords Fault tolerant flight control • Flight test • Unmanned aircraft

1.1 Introduction

The steady increase of air traffic flow over the next 20 years is expected due to the

economic growth and demographic changes in the emerging economic nations.

Since aviation safety has been highly guaranteed throughout the process of design,

manufacturing, and operation of an aircraft, the fatal accident rate has been kept low

for the past 20 years. However, it is concerned that increasing flights will lead to

more accidents unless steps are taken to drastically reduce accident rates [1]. As for

the guidance and control technologies, flight automation such as autopilot,

autothrottle, and flight management system (FMS) has greatly contributed to

increase the flight safety. However, these functions cannot adapt themselves to

maintain their performance in the case of unexpected failures. In such cases, pilots

S. Suzuki (*) • Y. Yoshimatsu • K. Miyaji

Department of Aeronautics and Astronautics, The University of Tokyo, Tokyo, Japan

e-mail: [email protected]

K. Nonami et al. (eds.), Autonomous Control Systems and Vehicles,

Intelligent Systems, Control and Automation: Science and Engineering 65,

DOI 10.1007/978-4-431-54276-6_1, # Springer Japan 2013

3

have to manage the difficult situations which may increase their workloads and may

cause accidents. It is expected that a fault tolerant control or a resilient control can

extend the limit of conventional automatic control systems [2, 3].

While many kinds of fault tolerant controller designs have been investigated,

the authors’ team has been developing an adaptive controller using neural

networks (NNs). NN is a mathematical model of biological neural networks.

It is recognized that NNs have a high capability to model complex nonlinear

system, and they are used in a nonlinear adaptive controller [4]. Among the

various control architectures based on NNs, we are using a feedback error

learning (FEL) method which adds NNs parallel to the conventional feedback

controller [5]. Adapting the parameters in NNs, the approximate inverse model

can be obtained during the flight, and required signals can be generated at the

same time. These characteristics are required for FTFC, since the change of

dynamics should be captured, and the control command should be adaptively

generated immediately when failures occur during the flight.

Although actual flight tests are necessary to increase the technical readiness

level (TRD) of developed FTFC, it is very difficult to demonstrate the control

performance of the developed FTFC for serious failures such as airframe structure

failures since the airworthiness should be satisfied for experimental aircraft with

human pilots. The use of a scaled model as unmanned air vehicle (UAV) is an

available way for the flight demonstration with failures. Actually, the flight test of a

fighter-type model plane with wing structure failure was presented [6], and the

authors’ team carried out the flight test of a business jet-type model plane with wing

tip failure [7]. A business jet scale mode with wing tip separation mechanism was

designed and constructed. Additionally, an autonomous flight control system with

FTFC was also developed. The flight tests were successfully carried out to demon￾strate the effectiveness of the FTTC when the wing tip was separated during the

flight. Before this wing failure case, preliminary flight tests were practiced by using

a small UAV with malfunctions of control surfaces [8, 9].

1.2 Fault Tolerant Control Design Based on NNs

A feedback controller or a closed-loop controller has been widely used in many

fields since it can reduce the error between the reference and the output, compensate

the change of the system characteristics, and reduce the effect of disturbances to

the system. In the fields of flight dynamics, autopilot and autothrottle systems are

designed based on the feedback control theory in order to maintain the specified

flight path, flight altitude, and flight speed. The feedback controller is usually

designed for linearized system; thus, the design parameters such as feedback

gains must be prepared for various flight conditions since the dynamic

characteristics of aircraft wildly change according to the flight speed, the flight

speed, and the change of configurations. While this gain scheduling method has

been widely used, it cannot adapt itself to maintain its proper functions in the

4 S. Suzuki et al.

case of unexpected changes in the system dynamics. Typically, when airframe

structure failures or flight control system damages occur during the automated

flight, the automatic flight system will be disengaged, and the pilots have to control

the damaged aircraft. When the pilots are not familiar with the damages or failures,

the pilot’s workload will increase, and serious accidents will be unavoidable.

This is because that a fault tolerant control or a resilient control is explored, since

it has a possibility to adaptively compensate the unexpected changes [2, 3].

The authors’ team has been investigating the application of a linear neural network

controller using a FEL algorithm [8, 9]. FEL was originally developed in robot control

applications [5]. Neural networks (NNs) are added parallel on a conventional feedback

controller. By minimizing the feedback signals in a learning process, NNs can obtain

the inverse feedforward dynamic model of a plant. Although nonlinear NNs are used

in the original FEL, linear NNs are selected in order to obtain quick adaptation for

aircraft control applications. It should be noted that FEL with linear NNs is suitable for

an aircraft controller since the conventional feedback controller is working during an

online leaning period to guarantee the minimum flight performance.

Figure 1.1 shows the block diagram of the FEL controller. The evaluation

function to be minimized is defined as follows:

E ¼ 1

2

u2

fb (1.1)

where ufb is the feedback signal. The updating equation can be described as follows:

Δwi ¼ ε @E

@wi

¼ ε

@ufb

@wi

ufb (1.2)

where wi is a tuning parameter in neural networks and ε is a learning rate which

influences the convergence process. The control input to a plant is the sum of the

feedback and feedforward signal; thus,

u ¼ ufb þ unn (1.3)

@u

@wi

¼ @ufb

@wi

þ

@unn

@wi

(1.4)

Fig. 1.1 Block diagram of feedback error learning and linear neuron

1 Flight Demonstrations of Fault Tolerant Flight Control Using Small UAVs 5

When the convergence is obtained, the left side of Eq. (1.4) becomes zero.

Consequently, we obtain the following by combining Eqs. (1.2) and (1.4):

Δwi ¼ ε

@unn

@wi

ufb (1.5)

If linear neural networks are introduced as

unn ¼ w0 þ w1xc þ w2x_c þ w3x€c (1.6)

we can obtain the derivatives in Eq. (1.5) easily. It should be noted the command

signal xc and its time derivative should be normalized from the viewpoint of

numerical optimization. When the feedback command becomes to zero, the NNs

can capture the inverse model of the plant.

1.3 Aircraft Dynamics and Guidance and Control Law

A nonlinear 6 DOF model of an aircraft (Fig. 1.2) is used in our research. The

nonlinear aircraft equations can be written as [9]

x_ ¼ fðx; uÞ

x ¼ ½V; α; β; ϕ; θ; ψ; p; q;r

T

u ¼ ½δT; δa; δe; δr

T

(1.7)

where the state vector x consists of the velocity, the angle of attack, the sideslip

angle, the roll angle, the pitch angle, the yaw angle, the roll rate, the pitch rate, and

the yaw rate, respectively. The control vector u consists of the thrust command, the

aileron angle, the elevator angle, and the rudder angle, respectively.

In the automatic flight test of UAVs, a waypoint tracking method is applied.

Waypoints are specified as a set of target points. A UAV is guided by changing the

Fig. 1.2 Coordinate system

6 S. Suzuki et al.