Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Abstract

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicle (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) controller and Improved Particle Swarm Optimization algorithm (IPSO) model based on functional inertia weight. The controller is implemented in a three degrees of freedom (3 DOF) quadrotor symbolized with its non-linear dynamical mathematical model. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular ANFIS controller to guarantee fast convergence rapidity and IPSO aims to facilitate convergence to the ANFIS’s optimal parameters to accurately reproduce a desired reference trajectory. Simulation results are given to confirm the advantages of the proposed intelligent control, compared with ANFIS and PID control methods.