title: Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.
abstract: This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. 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, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.
authors.forename: Luis A
authors.initials: LA
authors.lastname: Vazquez
authors.forename: Francisco
authors.initials: F
authors.lastname: Jurado
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Castaneda
authors.forename: Victor
authors.initials: V
authors.lastname: Santibanez