Title :
Robust iterative learning control design is straightforward for uncertain LTI systems satisfying the robust performance condition
Author :
Tayebi, A. ; Zaremba, M.B.
Author_Institution :
Dept. of Electr. Eng., Lakehead Univ., Thunder Bay, Ont., Canada
fDate :
1/1/2003 12:00:00 AM
Abstract :
This note demonstrates that the design of a robust iterative learning control is straightforward for uncertain linear time-invariant systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well-known robust performance condition, a convergent updating rule involving the performance weighting function can be directly obtained. It is also shown that for a particular choice of this weighting function, one can achieve a perfect tracking. In the case where this choice is not allowable, a sufficient condition ensuring that the least upper bound of the L2-norm of the final tracking error is less than the least upper bound of the L2-norm of the initial tracking error is provided. This sufficient condition also guarantees that the infinity-norm of the final tracking error is less than the infinity-norm of the initial tracking error.
Keywords :
learning (artificial intelligence); linear systems; robust control; uncertain systems; controller; final tracking error; initial tracking error; iterative control; learning control; perfect tracking; robust control; robust iterative learning control; robust performance; uncertain linear time-invariant systems; Automatic control; Chebyshev approximation; Control design; Control systems; Estimation theory; Filtering algorithms; Gold; Robust control; System identification; Uncertainty;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2002.806659