Control System for Quadcopter UAV based SMC-RBFNN with External Disturbance

Abstract

Unmanned aerial vehicles (UAVs) can either be flown autonomously or remotely by a pilot. Due to its many benefits, including the capacity to take off and land vertically and the ability to take off and land in a small space, this form of UAV quadcopter is currently the subject of extensive research. An autonomous UAV is being developed to reduce the likelihood of pilot operating errors when managing the UAV. The quadcopter dynamic system in this study was controlled primarily by a radial basis function neural network (RBFNN), and its performance was evaluated using simulation on a test track with outside disturbances. One test track is used for the simulation, and there are no outside disturbances. Input of external noise occurs concurrently for x, y, and z coordinates. The average of error for the control system SMC and SMC-RBFNN without disturbance is 0 according to the simulation results. Additionally, the SMC control system’s of error with external disturbances is 0.74, whereas it is 0.54 for the SMC-RBFNN control system. This is demonstrated by the system’s ability to return to the test track at the present within 9 seconds while employing the SMC-RBFNN controller. In contrast, the system can reach the test track in 18 seconds while using the SMC. The SMC- RBFNN is one of the suitable control strategies for flight missions with external disturbances, it may be inferred.