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Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

1
Agricultural School of Jundiaí, Federal University of Rio Grande do Norte, UFRN, Macaíba, RN 59280-000, Brazil
2
INESC TEC—INESC Technology and Science, Campus da FEUP, 4200-465 Porto, Portugal
3
University of Trás-os-Montes and Alto Douro, School of Sciences and Technology, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Robotics 2020, 9(2), 22; https://doi.org/10.3390/robotics9020022
Received: 30 January 2020 / Revised: 29 March 2020 / Accepted: 2 April 2020 / Published: 7 April 2020
The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use. View Full-Text
Keywords: ant lion optimizer; grey wolf optimizer; sliding mode control; particle swarm optimization; quadrotor; unmanned aerial vehicle ant lion optimizer; grey wolf optimizer; sliding mode control; particle swarm optimization; quadrotor; unmanned aerial vehicle
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Oliveira, J.; Oliveira, P.M.; Boaventura-Cunha, J.; Pinho, T. Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller. Robotics 2020, 9, 22.

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