Model reference adaptive iterative learning control for linear systems

2006 ◽  
Vol 20 (9) ◽  
pp. 475-489 ◽  
Author(s):  
A. Tayebi
Keyword(s):  
Linear Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Adaptive Iterative Learning Control ◽  
Model Reference ◽  
Model Reference Adaptive

Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise

2019 ◽  
Vol 42 (2) ◽  
pp. 259-271
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Measurement Noise ◽  
System Matrix ◽  
Adaptive Iterative Learning Control ◽  
Matrix Estimation

This paper investigates an adaptive iterative learning control (AILC) scheme for a class of switched discrete-time linear systems with stochastic measurement noise. For the case when the subsystems dynamics are unknown and the switching rule is arbitrarily fixed, the iteration-wise input-output data-based system lower triangular matrix estimation is derived by means of minimizing an objective function with a gradient-type technique. Then, the AILC is constructed in an interactive form with system matrix estimation for the switched linear systems to track the desired trajectory. Based on the derivation of the boundedness of the estimation error of system matrix, by virtue of norm theory and statistics technique, the tracking error and the covariance matrix of the tracking error are derived to be bounded, respectively. Finally, the AILC concept is extended to nonlinear systems by utilizing linearization techniques. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

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