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of the European Council for Modelling and Simulation



The Use Of Artificial Intelligence In Controlling A 6DOF Motion Platform


Webjørn Rekdalsbakken

Published in:


ECMS 2007 Proceedings

Edited by: Ivan Zelinka, Zuzana Oplatkova, Alessandra Orsoni


ISBN: 978-0-9553018-2-7

Doi: 10.7148/2007


21st European Conference on Modelling and Simulation,

Prague, June 4-6, 2007


Citation format:

Rekdalsbakken, W. (2007). The Use Of Artificial Intelligence In Controlling A 6DOF Motion Platform. ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni (pp. 249-254). European Council for Modeling and Simulation. doi:10.7148/2007-0249.



Aalesund University College (AUC) has long and broad experience in the design of nautical simulators. An important part of this work is the design and control of motion platforms in three or six Degrees of Freedom (DOF). A small-scale model of a 6DOF motion platform has been designed to investigate the control possibilities of such a device. The platform deck is moved by six actuator rods connected to DC servo motors, controlled by Pulse Width Modulated (PWM) signals. The servo motors are placed in pairs at the corners of an equilateral triangle at the base of the platform, and the actuators are similarly connected in pairs to the platform deck. A solution for the inverse kinematics from platform coordinates to motor shaft angles is derived in this paper. The mathematical transformations are verified through simulations and measurements of the platform movements. To explore the control capabilities of the motion platform, a ball has been controlled with the aid of a camera to follow different kinds of paths on the deck. The ball follows reference paths like a circle or an 8-curve with the platform in a stationary position relative to the camera, as well as while the platform is moving in a plane at a certain height. The control is based on a state space model, and different kinds of adaptive algorithms have been tested, including a Neural Network (NN) and a Genetic Algorithm (GA). The training of the NN controller with a back propagation algorithm was very time demanding, and the real-time implementation was not successful. The GA controller, however, functioned very well and adapted to the different reference conditions in real-time.

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