| 论文简介 |
In this paper, conventional first- and second-order proportional-derivative-type (PD-type) iterative learning
control (ILC) updating algorithms are discussed for a type of nonlinear time-invariant system. On the
basis of the Bellman–Gronwall inequality, the convergence is derived in the sense that the tracking error
is measured in the form of the Lebesgue-p norm. This analysis shows that, under an appropriate condition,
the first-order PD-type ILC updating law is monotonically convergent while the second-order law
is convergent and the monotonicity is guaranteed after finite iterations. Further, by analysing the characteristic
polynomial of the second-order PD-type ILC updating law, an argument about the comparison of
convergence speed in terms of Qp-factor is made. The argument clarifies that the second-order PD-type
ILC law can be Qp faster, equivalent or slower than the first-order law, depending upon different sets of
the learning gains. Numerical simulations are conducted to show their validity and effectiveness. |
(创新港)
