title: Adaptive neural network motion control of manipulators with experimental evaluations.
abstract: A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.
authors.affiliation: Instituto Tecnológico de Tijuana, Boulevard. Industrial S/N, 22510 Tijuana, BC, Mexico ; Instituto Politécnico Nacional-CITEDI, Avenida del Parque 1310, Mesa de Otay, 22510 Tijuana, BC, Mexico.
authors.forename: S
authors.initials: S
authors.lastname: Puga-Guzmán
authors.affiliation: Instituto Politécnico Nacional-CITEDI, Avenida del Parque 1310, Mesa de Otay, 22510 Tijuana, BC, Mexico.
authors.forename: J
authors.initials: J
authors.lastname: Moreno-Valenzuela
authors.affiliation: Instituto Tecnológico de La Laguna, Boulevard Revolución y Cuahtémoc, 27000 Torreón, COAH, Mexico.
authors.forename: V
authors.initials: V
authors.lastname: Santibáñez
mesh_terms: D000222:Adaptation, Physiological; D016571:Neural Networks, Computer; D012371:Robotics / Q000379:methods*