Path tracking controller of an autonomous armoured vehicle using modified Stanley controller optimized with particle swarm optimization

  • Noor Hafizah Amer
  • Hairi Zamzuri
  • Khisbullah Hudha
  • Vimal Rau Aparow
  • Zulkiffli Abd Kadir
  • Amar Faiz Zainal Abidin
Technical Paper
  • 21 Downloads

Abstract

This study presents the development and optimization of a proposed path tracking controller for an autonomous armoured vehicle. A path tracking control is developed based on an established Stanley controller for autonomous vehicles. The basic controller is modified and applied on a non-linear, 7degree-of-freedom armoured vehicle model, and consists of various modules such as handling model, tire model, engine, and transmission model. The controller is then optimized using particle swarm optimization algorithm to select the optimum set of controller parameters. The main motivation of this study is that implementation of path tracking control on an autonomous armoured vehicle is still very limited and it is important to have a specific study on this field due to the different dynamics and properties of the armoured vehicle compared to normal passenger vehicles. Several road courses are considered and the performance of the developed controller in guiding the vehicle along these courses was compared against the original Stanley Controller. It was found that the optimized controller managed to improve the overall lateral error throughout the courses with 24–96% reduction in lateral error. Also, the optimization for the proposed controller was found to converge faster than its counterpart with up to 93% better solution.

Keywords

Particle swarm optimization Armoured Vehicle Autonomous path tracking Path tracking Stanley controller 

Nomenclature

CG

Vehicle’s centre of gravity

X

Global vehicle position in X axis (m)

Y

Global vehicle position in Y axis (m)

x

Local vehicle position in x axis (m)

y

Local vehicle position in y axis (m)

Ax

Lateral acceleration in vehicle local coordinates (ms−2)

Ay

Longitudinal acceleration in vehicle local coordinates (ms−2)

Fx, Fy, Fz

Forces in vehicle local coordinates direction (N)

θ
Vehicle’s inclination w.r.t ground as shown in Fig. 1 (rad)
Fig. 1

Armoured vehicle model [9, 30, 31]

CR

Distance between gun and vehicle’s CG (m)

φ

Firing angle w.r.t. vehicle’s longitudinal axis (rad)

β

Vehicle’s moving direction w.r.t. vehicle’s longitudinal axis (rad)

R

Radius of wheel (m)

Alpha, α

Lateral slip angle (rad)

Ta

Engine acceleration torque (Nm)

Omega, ω

Wheel rotational speed (rad/s)

Vx

Longitudinal vehicle velocity(m/s)

δ

Steered wheel angle (rad)

Ytraj

Y-position of a point on path nearest to the vehicle (m)

ψ

Vehicle’s yaw angle (rad)

\( \dot{\psi } \)

Vehicle’s yaw rate (rads−1)

\( \psi_{traj} \)

Path yaw angle (rad)

\( \dot{\psi }_{traj} \)

Path’s yaw rate (rads−1)

ϕ

Heading error, ψ − ψ traj (rad)

ϕ

Heading error, ψ − ψ traj (rad)

e

Lateral error, Y traj   Y (m)

kϕ

Tuned controller gain

k1

Gain for ϕ

k

Tuned controller gain (s−1)

kψ

Tuned controller gain (s)

v

Instantaneous vehicle velocity (m/s)

Notes

Acknowledgements

The authors would like to thank the Malaysian Ministry of Education for their financial supports and technical advises for this research through research grant RAGS (RAGS/1/2014/TK01/UPNM/1).

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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversiti Pertahanan Nasional MalaysiaKuala LumpurMalaysia
  2. 2.Malaysia-Japan International Institute of TechnologyUniversity Technology MalaysiaKuala LumpurMalaysia
  3. 3.Faculty of Engineering TechnologyUniversiti Teknikal Malaysia MelakaMelakaMalaysia

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