Abstract
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.
Highlights
QuadRotor (QR) is a small rotorcraft which can be remotely controlled or fly autonomously throughGPS-based embedded flight plans
At least one hunting method outperforms Particle Swarm Optimization (PSO) in terms of general tracking (ISET ): both Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO) when the mean controller parameters (Table 6) are used (Table 9 ) and ALO when the choice is the controller parameter set related to best fitness values (Table 10)
When the curves almost coincide with the set point, it is due to the Sliding Mode Control (SMC) transient response, which is very fast and non-oscillatory, achieving the objectives of this work of getting a more precise tracking with equivalent and feasible control signal effort and chattering, when compared to PSO
Summary
QuadRotor (QR) is a small rotorcraft which can be remotely controlled or fly autonomously through. Some statistic-based [10] and bio-inspired [3,11,12], optimization algorithms have been used for PID and backstepping based flight control systems, a solution for the SMC-based approach is still an open and necessary research issue [13] This has motivated the novel work reported here on the proposal and evaluation of hunting-based search algorithms as optimization tools, compared to the known and classical bio-inspired Particle Swarm Optimization (PSO) [14]. GWO mimics how a population of wolves chase a prey, inspired in its social hierarchy Both have been evaluated for different applications, such as power systems control [24,25,26,27], process planning [28], and data gathering [29], as well as their multi-objective approaches [30,31]. Novel extension of such optimization approaches to SMC controller tuning, usually applied to PID control
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
