Optimised sliding mode control of a hexacopter: Simulation and experiments

• Main Authors: Sun, Chaoran, Agha, Stephen A., Mohamed, Zaharuddin, Shaheed, M. Hasan
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/103559/1/ZaharuddinBMohamed2022_OptimisedSlidingModeControlofaHexacopter.pdf; http://eprints.utm.my/103559/; http://dx.doi.org/10.3390/electronics11162519

Hexacopters are a kind of unmanned aerial vehicle (UAV) with six actuators and six degrees-of-freedom motions. The control of a hexacopter drone is a critical challenge. This paper presents a nonlinear dynamical model for a hexacopter and complete control approaches based on sliding mode control theory. Furthermore, this study proposed an effective control tuning method based on an optimisation algorithm. The controller has been improved by the grey wolf optimisation (GWO) algorithm, an iteration algorithm inspired by the social hierarchy and hunting behaviour of grey wolves. The improvement of the controller has been verified both experimentally and in simulations. The performance of the sub-controller for an attitude angle was tested in a test bench, and the whole flight controller was tested in simulation hexacopters, which are highly manoeuvrable, nonlinear aerial vehicles with six independent rotors and capacity for vertical take-off and landing. This article presents a derivation of the nonlinear dynamical model for a hexacopter, which includes the aerodynamic drag, and inertia counter torques. Flight control based on sliding mode control theory, which generally shows good performance on nonlinear systems, is developed and implemented. Given the need to simultaneously tune controller parameters, two nature inspired optimisation routines (GWO and PSO) are applied and compared for effectiveness in controller tuning. Results indicate that GWO-based tuning produces superior outcomes in terms of controller performance. This is in addition to the fact that PSO parameters require tuning rather than random selection of algorithm parameters. A reduced-order physical prototype is presented for the validation of the tuning routine on the roll/pitch control. The results indicate good agreement between simulation and experimental outcomes, with about 10.4% improvement in the tracking performance of roll DOF when GWO is applied to tune the controller.