Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm (SFLA) based tuning approach of an Integral Sliding Mode Controller (ISMC) for a quadrotor type of Unmanned Aerial Vehicles (UAV). Based on the Newton–Euler formalism, a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes. Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law, which are usually selected by repetitive and time-consuming trials-errors based procedures, a constrained optimization problem is formulated for the systematically tuning of these unknown variables. Under time-domain operating constraints, such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation- and swarm intelligence-based algorithms such as the Crow Search Algorithm (CSA), Fractional Particle Swarm Optimization Memetic Algorithm (FPSOMA), Ant Bee Colony (ABC) and Harmony Search Algorithm (HSA). Numerical experiments are carried out for various sets of algorithms’ parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization. Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence. Demonstrative results verified that the proposed metaheuristics-based approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.