Observer-Based Adaptive Neural Iterative Learning Control for a Class of Time-Varying Nonlinear Systems

• Published in: Shanghai jiao tong da xue xue bao Vol. 22; no. 3; pp. 303 - 312
• Main Author: 韦建明 张友安 刘京茂
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.06.2017; Springer Nature B.V
• Subjects: Adaptive control; Architecture; Computer Science; Computer simulation; Dynamical systems; Electrical Engineering; Engineering; Infinity; Iterative methods; Learning; Life Sciences; Materials Science; Neural networks; Nonlinear dynamics; Nonlinear systems; Radial basis function; Time domain analysis; Tracking errors; filter; observer; time-varying nonlinear systems; output tracking; adaptive iterative learning control (AILC); TP 273
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-017-1836-2

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.