iterative learning control for robot manipulators
abstract
when a system is performing the same task repeatedly it is, from an engineering
perspective, advantageous to use the knowledge from the previous iterations of the
same task in order to reduce the error on successive trials.
in control systems, the aim is to force the system output to follow a desired
trajectory as closely as possible. specific norms and measures of optimality are used
to determine how close the output is to the desired trajectory. although control
theory provides many different possible solutions for such problem, it is not always
possible to achieve a desired set of performance requirements. this may be due to
the presence of unmodeled dynamics or parametric uncertainties exhibited during
the system operation, or due to the lack of suitable design techniques for particular
class of systems. iterative learning control (ilc) is a relatively new addition to these
techniques that, for a particular class of problems, can be used to overcome some of
the difficulties associated with performance design of control systems.
collections
- retrospective theses [1604]