In this paper an adaptive iterative learning control scheme is presented for the output tracking of a
class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and
s-domain representation is constructed to derive an error model with relative degree one for our purpose. And
time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is
constructed by using a first-order filter, which removes the requirement of strict positive real (SPR) condition
and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,
the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing
Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation
example is presented to verify the effectiveness of the proposed approach.
WEI Jianming1* (韦建明), ZHANG Youan2 (张友安), LIU Jingmao3 (刘京茂)
. Observer-Based Adaptive Neural Iterative Learning Control for a Class of Time-Varying Nonlinear Systems[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(3)
: 303
-312
.
DOI: 10.1007/s12204-017-1836-2
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