Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delays

2010 ◽  
Vol 8 (2) ◽  
pp. 177-186 ◽  
Author(s):  
Weisheng Chen ◽  
Li Zhang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Varying ◽  
Adaptive Iterative Learning Control ◽  
Parameterized Systems ◽  
Nonlinearly Parameterized Systems ◽  
Nonlinearly Parameterized

scholarly journals Observer-Based Adaptive Iterative Learning Control for a Class of Nonlinear Time Delay Systems with Input Saturation

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Jian-ming Wei ◽  
Yun-an Hu ◽  
Mei-mei Sun
Keyword(s):  
Iterative Learning Control ◽  
Input Saturation ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Varying ◽  
Hyperbolic Tangent ◽  
Adaptive Iterative Learning Control ◽  
Hyperbolic Tangent Function ◽  
Tangent Function ◽  
Composite Energy

This paper presents an adaptive iterative learning control scheme for the output tracking of a class of nonlinear systems with unknown time-varying delays and input saturation nonlinearity. An observer is presented to estimate the states and linear matrix inequality (LMI) method is employed for observer design. The assumption of identical initial condition for ILC is relaxed by introducing boundary layer function. The possible singularity problem is avoided by introducing hyperbolic tangent function. The uncertainties with time-varying delays are compensated for by the combination of appropriate Lyapunov-Krasovskii functional and Young’s inequality. Both time-varying and time-invariant radial basis function neural networks are employed to deal with system uncertainties. On the basis of a property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

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