Title: Attitude control of an unmanned patrol helicopter based on an optimised spiking neural membrane system for use in coal mines
Authors: Jiachang Xu; Yourui Huang; Ruijuan Zhao; Yu Liu
Addresses: School of Computer Science and Engineering, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province, China ' School of Electrical and Information Engineering, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province, China ' Department of Information Engineering, PLA Army Academy of Artillery and Air Defense, Hefei 230031, China ' State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province, China
Abstract: For the attitude control of unmanned helicopters used in the intelligent patrolling of coal mines, a spiking neural membrane system is introduced for attitude optimisation control. First, a geometry-based attitude dynamics model suitable for coal mine scenarios is constructed. Second, in accordance with the attitude dynamics model, an extended spiking neural membrane system (ESNMS) is constructed, and an optimised spiking neural membrane system (OSNMS) and accompanying algorithm are designed to optimise the ESNMS. Then, the attitude control performance of the developed system is theoretically analysed. Finally, through the simulation of a semiphysical experimental platform, trajectory tracking is effectively realised. Under normal and wind disturbance conditions, comparisons with the traditional synovium controller (TSC) and linear feedback controller (LFC) show that the performance of the designed OSNMS is greatly improved, and the experimental results show that the OSNMS has good stability and effectiveness.
Keywords: intelligent coal mining; unmanned helicopter; attitude control; membrane computing; spiking neural membrane system.
International Journal of Computational Science and Engineering, 2021 Vol.24 No.5, pp.538 - 549
Received: 12 Nov 2020
Accepted: 18 Mar 2021
Published online: 12 Oct 2021 *