A fractional order predictive control for trajectory tracking of the ar.drone quadrotor

Abstract:

A fractional-order model predictive control with extended prediction self-adaptive control (FOMPC-EPSAC) strategy is proposed for the AR.Drone quadrotor system. The objective is to achieve an optimal trajectory tracking control for an AR.Drone quadrotor by using a fractional order integral cost function in the conventional MPC-EPSAC algorithm. In addition, a particle swarm optimization (PSO) algorithm is applied to find the optimal weighting matrices, which depend on the terms ($$\alpha $$, $$\beta $$) of the fractional order cost function. Some simulation results show the superiority of FOMPC-EPSAC over conventional MPC-EPSAC with respect to trajectory tracking and robustness under wind disturbances.

Año de publicación:

2021

Keywords:

• Particle swarm optimization (PSO) algorithm
• model pbkp_redictive control (MPC)
• unmanned aerial vehicles (UAVs)
• Fractional Calculus
• Fractional Order Control

Fuente:

google

scopus

orcid