The design
and implementation of adaptive control for nonlinear unknown systems is
extremely difficult. The nonlinear adaptive control for assembly robots performing
a peg-in-hole insertion is one such an example. The recently
intensively studied neural networks brings a new stage in the
development of adaptive control, particularly for unknown nonlinear systems. The
aim of this paper is to propose a new approach
of hybrid force position control of an assembly robot based
on artificial neural networks systems. An appropriate neural network is
used to model the plant and is updated online. An
artificial neural network controller is then directly evaluated using the
updated neuro model. Two control structures are proposed and the
stability analysis of the closed-loop system is investigated using the
Lyapunov method. Experimental results demonstrate that the identification and control
schemes suggested in this paper are efficient in practice.
In this paper we present a combination between a speed up robust features (SURF) and the image based visual servoing control law; in order to achieve a robust tracking mission. The validation of the proposed approach insured by the control of turret Pan-Tilt system using the visual features acquired by a webcam, and these visual features are extracted by the SURF algorithm; in the constraints of the variation of the scale and the rotation of the image; so that the use of the visual features in the image based visual servoing control law to compute the kinematics screw of the camera in the image frame. For tracking an object we need to introduce the jacobian of the robot to transfer the values of velocities from the camera frame to the robot articulation joints.
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