Abstract
In this paper, we provide uniform design and analysis framework for iterative learning control of a class of impulsive first-order distributed parameter systems in the time domain. In particular, P-type and D-type iterative learning controls with initial state learning are considered. We present convergence results for open-loop iterative learning schemes in the sense of the $L^{p}$ -norm and λ-norm, respectively. Finally, an example is given to illustrate our theoretical results.
Highlights
In, Arimoto et al [ ] propose basic theories and algorithms on iterative learning control (ILC) and point out that ILC is a useful practical control approach for systems which perform tasks repetitively over a finite time interval
For more details on the contributions for linear and nonlinear ordinary differential equations, the reader is referred to the monographs [ – ], and [ – ]
In [ ], Liu et al explore P-type iterative learning control law with initial state learning for impulsive ordinary differential equations to tracking the discontinuous output desired trajectory
Summary
In , Arimoto et al [ ] propose basic theories and algorithms on iterative learning control (ILC) and point out that ILC is a useful practical control approach for systems which perform tasks repetitively over a finite time interval. In [ ], Liu et al explore P-type iterative learning control law with initial state learning for impulsive ordinary differential equations to tracking the discontinuous output desired trajectory.
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