Iterative learning control designs for autonomous driving applications

Iterative Learning Control Designs For

Autonomous Driving Applications

Submitted by: Nguyen Van Lanh

Master Thesis

Master of Control System Engineering

HAN University of Applied Science, the Netherlands

Academic supervisor:

Richarch Kaandorp

Company supervisor:

Dr. Son Tong

Siemens Industry Software, Belgium

8th April 2018

Acknowledgement

I would like to thank my dear teacher and academic supervisor, Richarch Kaan￾dorp, who gave me a professional supervision with invaluable lessons. Richard is a

warm-hearted person and he is always willing to answer my questions and discuss

with me in his classes. In order to finish my thesis, he gave me useful and insightful

comments helping me to follow the right track.

In addition, I would love to express my gratitude to my company supervisor,

Dr. Son Tong. He was always happy to help me solve confusions and led me to

obtaining the final results of the thesis. Besides, Son is an open-minded person who

has been good to me. Without his encouragement, I could not finish this final work

in my master study.

Furthermore, I am grateful to Thai Nguyen University of Technology (TNUT) in

Vietnam, where I studied and have been working for. Additionally, I would like to

acknowledge Prof. Cuong Duy Nguyen at TNUT in Vietnam. He always supports

and motivates me in my academic research. I also would like to thank Vietnamese

Government for sponsoring my Master study at HAN University of Applied Science,

Netherlands in form of Project 599 scholarship.

Finally, I wish to thank my family. Your love and belief have brought me up and

gone further.

Thank you!

Leuven, March 2018

Lanh Nguyen

I

Abstract

In this master thesis, iterative learning control (ILC) is introduced to deal with

the problem of designing the most optimal control signal in autonomous driv￾ing applications that require tracking a fixed reference trajectory. By exploiting

data/information from the previous iterations, the learning control algorithm can

obtain better tracking control performance for the next iteration, and hence out￾performs conventional control approaches such as feedback control. In addition, the

control design is based on optimization, where kinematic and dynamic constraints of

the vehicle, such as acceleration and steering, are taken into account. The learning

algorithms can also be used in combination with other traditional control techniques,

for example, the conventional feedback control is designed in the first iteration, then

learning control is applied to improve performance in the subsequent iterations. In

this thesis, we use RoFaLT, a nonlinear optimization-based learning control tool, to

implement the ILC controllers. Finally, the learning control designs are simulated in

a co-simulation fashion of LMS Amesim and Prescan software in two different sce￾narios: autonomous valet parking and racing car. The results show the advantages

of ILC controllers in improving tracking performance while guaranteeing system

constraints.

Keywords:

Advanced Driver Assistance Systems (ADAS), Iterative Learning Control (ILC),

Optimal Control.

II

Contents

Nomenclature IV

List of Figures VII

List of Tables VIII

1 Introduction 1

1.1 Siemens Industry Software NV . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Autonomous Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Goal of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.4 Simulation with LMS Imagine.Lab Amesim . . . . . . . . . . . . . . . 2

1.5 Demostration with PreScan . . . . . . . . . . . . . . . . . . . . . . . 4

2 Vehicle dynamics 5

2.1 Vehicle model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Tire model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Slip-free bicycle model . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4 Valiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Feedback controller 14

4 Iterative Learning Control (ILC) 17

4.1 Overview of ILC controller . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1.1 PD-type design . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.1.2 Plant Inversion Methods . . . . . . . . . . . . . . . . . . . . . 18

4.1.3 Quadratically Optimal Design (Q-ILC) . . . . . . . . . . . . . 19

4.1.4 Current-Iterative Learning Control . . . . . . . . . . . . . . . 19

4.2 RoFaLT tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.1 Model correction step . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.2 Control step . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Autonomous Applications and Simulation Results 24

5.1 Application 1: Valet Parking . . . . . . . . . . . . . . . . . . . . . . . 24

5.2 Application 2: Racing . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6 Conclusion and recommendation 41

6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.2 Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

References 43

III

Nomenclature

Symbols

X Global X coordinate [m]

Y Global Y coordinate [m]

x Local x coordinate [m]

y Local y coordinate [m]

ϕ Global orientation [rad]

v Longitudinal velocity [m/s]

δ Steering angle [rad]

a Commanded acceleration [m/s2

]

g Acceleration of gravity [m/s2

]

κ Path curvature [−]

α Slip angle [rad]

ω Yaw rate [rad/s]

F Force [N]

L Length of wheelbase [m]

l Length of wheelbase [m]

I Moment of inertia [kg.m2

]

C1 Geometrical [l/L] [−]

C2 Geometrical [1/L] [m−1

]

Cr0 Zero order friction parameter [−]

Cr2 Second order friction parameter [−]

B Stiffness factor [−]

C Shape factor [−]

D Peak factor [−]

Subscripts

f front wheel x x axis

r rear wheel y y axis

l left z z axis

r right y nominal

IV