Experimental evaluation of some classical and adaptive iterative learning control schemes on a 5DOF robot manipulator
Abstract
In many process industries (e.g., VLSI production lines, Automotive industries, IC
welding process, inspections, manipulations), robot manipulators are used to perform
the same tasks repeatedly over a finite time interval. The ultimate goal of robotic
research is to design intelligent and autonomous robot control systems to perform
repetitive tasks that are dull, hazardous, or require skill beyond the capability of
humans. The nonlinear nature of the robot dynamics has made this problem a challenging
one in robotics research. This highly demanding control problem of driving an
industrial robot to follow a desired trajectory perfectly under constrained or unconstrained
environment has led to the application of sophisticated control techniques.
From the classical or modern control view point, it is a very difficult task to design
an intelligent robot control system that can achieve perfect tracking over a finite time
interval due to the effect of highly coupled robot dynamics and the presence of the
unmodeled dynamics such as friction and backlash that are usually exhibited in the
robot system during actual operation.
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