Title: Optimisation of motion cueing position based on adaptive chaos PSO algorithm

Authors: Xiang-Tong Kong; Yuan-Chang Zhu; Yan-Qiang Di; Hao-Hao Cui

Addresses: Department of Electrics and Optics Engineering, Mechanical Engineering College, Shijiazhuang, China ' Department of Electrics and Optics Engineering, Mechanical Engineering College, Shijiazhuang, China ' Department of Electrics and Optics Engineering, Mechanical Engineering College, Shijiazhuang, China ' Department of Electrics and Optics Engineering, Mechanical Engineering College, Shijiazhuang, China

Abstract: In most simulators, such as flight simulator, to keep the trainers in a high level sensation fidelity, the best motion cueing position needs to be solved firstly. We make analyses to the problem and proposed a model for the problem. Then we figure out the principle and target of the choosing of optimal motion cueing position. Considering the analyses above, we propose an improved adaptive chaos particle swarm optimisation (ACPSO) algorithm. By making the inertial factor adjust adaptively and combining the chaos algorithm with PSO algorithm, the searching range is more flexible and the particles' movement more simultaneous. The changes above realise the simultaneous progressing of global searching and local searching of solutions. Furthermore, local optimal is avoided by this means and the constraint information of the problem is utilised to improve the optimisation effect. Experiment results show that the ACPSO algorithm improves the overall optimisation effect of the optimal position problem, and the optimal motion cueing position make the simulator trainer have a more realistic sensation and maintain the safety of the platform.

Keywords: adaptive chaos PSO; particle swarm optimisation; ACPSO; simulation; motion cueing position; flight simulators.

DOI: 10.1504/IJRIS.2016.080059

International Journal of Reasoning-based Intelligent Systems, 2016 Vol.8 No.1/2, pp.15 - 22

Received: 01 Dec 2015
Accepted: 12 Jan 2016
Published online: 31 Oct 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article