ALSINA, P. J.; http://lattes.cnpq.br/3653597363789712; ALSINA, Pablo Javier.
Resumo:
In this work, robot manipulator control techniques based on
artificial neural networks are developed. A theoretical review of Neural Networks and
neurocontrollers is presented. A Block Diagram representation of the robot dynamics
equation is developed. Based on this representation, two recursive algorithms for
efficient computation of the inertia matrix and dynamics equation are proposed: the
Inverse Path Method and the Central Path Method. A factored form of the inertia matrix
is derived. A Linear-ln-Parameter Robot Model is developed and a recursive method for
the computation of regressor matrix is proposed. The concept of modular adaptive
control of robotic manipulators is presented. In the modular scheme, control and
adaptation modules are assigned to each link, allowing the control and adaptation of
each link dynamic parameters independent of the other links. A Linear-In-Kinematic-
Terms robot model is developed. Based on this model, the concept of Modular
Ncuroconlrollcr for robotic manipulators is presented. In the proposed scheme, Neural
Modules (Standardized Neural Networks) are assigned to each link. The modules are
joined in a global control system that can be applied to any serial manipulator. A direct
inverse training method for the modular ncurocontroller is proposed and, based on
adaptive control techniques, a direct adaptive training scheme is developed. In order to
test the proposed control schemes, the ROBOTLAB software tool was developed. This
tool allows the robot dynamics simulation, including 3D graphics output.