Abstract
This chapter describes the design of a continuous-time decentralized neural control scheme for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV). A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, given in the form of a strict-feedback system, a local neural controller is derived via the backstepping approach. The performance of the overall neural identification and control scheme is validated via simulation results.
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