After motivating examples, this monograph gives substantial new
results on the analysis and control of linear repetitive processes.
These include further applications of the abstract model based
stability theory which, in particular, shows the critical
importance to the dynamics developed of the structure of the
initial conditions at the start of each new pass, the development
of stability tests and performance bounds in terms of so-called 1D
and 2D Lyapunov equations. It presents the development of a major
bank of results on the structure and design of control laws,
including the case when there is uncertainty in the process model
description, together with numerically reliable computational
algorithms. Finally, the application of some of these results in
the area of iterative learning control is treated --- including
experimental results from a chain conveyor system and a gantry
robot system.
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