Abstract
Conventional iterative learning control algorithms are designed to minimize the norm of output-error functions. But boundedness of the input-functional sequence is not assured. Since convergence of the output function does not imply boundedness of the input-functional sequence and the energy of the input function is limited at real systems, it is necessary to guarantee the iterative learning control algorithm generates bounded input-functional sequences. This paper proposed a method to design iterative learning control algorithms which minimize the norm of output-error functions and decreases the norm of input-error functions monotonically in order to ensure the boundedness.
The design method consists of three steps of procedure. The first one is to confirm existence of input functions that realize the desired output function. The second one is to determine a linear operator the ratio of which with the one of the object system is within a certain limit. And the last one is to construct an algorithm with a linear differential system corresponding to the adjoint operator. The last two steps are also stated in terms of transfer function matrix.
The illustrated design procedures and results of numerical simulations are presented in the final part of the paper.
