Disturbance observer-based discrete-time neural control for unmanned aerial vehicles with uncertainties and disturbances *. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Shao, Shuyi; Chen, Mou; Mei, Rong
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 15289
• Page End: 15294
• Publication Date: 2017-07
• Subjects: UAV -- disturbance observer -- neural network -- backstepping -- tracking control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.2439
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, a disturbance observer-based discrete-time neural control problem is studied for unmanned aerial vehicle (UAV) in the presence of external disturbances and system uncertainties. To estimate the external disturbance, a nonlinear discrete-time disturbance observer (DTDO) is designed. Furthermore, the system uncertainties are approximated by employing neural network (NN). Then, a discrete-time neural tracking control scheme is proposed based on the designed DTDO, the discrete-time tracking differentiator and the backstepping technique. Under the discrete-time Lyapunov analysis, the boundness of all the closed-loop system signals are proven. Finally, numerical simulation results are shown to demonstrate the effectiveness of the proposed control scheme.