TY  - EJOU
AU  - Selma, Ben Attia 
AU  - Eddine, Ouerfelli Houssem 
AU  - Salah, Salhi 

TI  - Robustness Convergence for Iterative Learning Tracking Control Applied to Repetitfs Systems
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 32
IS  - 2
SN  - 2326-005X

AB  - This study addressed sufficient conditions for the robust monotonic convergence of repetitive discrete-time linear parameter varying systems, with the parameter variation rate bound. The learning law under consideration is an anticipatory iterative learning control. Of particular interest in this study is that the iterations can eliminate the influence of disturbances. Based on a simple quadratic performance function, a sufficient condition for the proposed learning algorithm is presented in terms of linear matrix inequality (LMI) by imposing a polytopic structure on the Lyapunov matrix. The set of LMIs to be determined considers the bounds on the rate of variation of the scheduling parameter. The control law designs polynomial ILC by constructing a sequence of control inputs to a discrete-time R-LPV system, producing an iterative dynamic for the R-LPV system with respect to the polytopic structure for uncertain parameters. Numerical simulations were performed to demonstrate the benefits of the proposed technique.
KW  - ILC control; quadratic approach; LMI; repetitive LPV systems; output disturbance; robust control

DO  - 10.32604/iasc.2022.020435