Dissipative Stabilization of Nonlinear Repetitive Processes with an Iterative Learning Control Application

2019 ◽  
Author(s):  
Julia Emelianova ◽  
Pavel Pakshin ◽  
Krzvsztof Galkowski ◽  
Eric Rogers
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Repetitive Processes ◽  
Control Application

Passivity based stabilization of repetitive processes and iterative learning control design

2018 ◽  
Vol 122 ◽  
pp. 101-108 ◽  
Author(s):  
Pavel Pakshin ◽  
Julia Emelianova ◽  
Mikhail Emelianov ◽  
Krzysztof Galkowski ◽  
Eric Rogers
Keyword(s):  
Iterative Learning Control ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Repetitive Processes

scholarly journals Robust PD-Type Iterative Learning Control of Discrete Linear Repetitive Processes in the Finite Frequency Domain

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1004
Author(s):  
Lei Wang ◽  
Mu Li ◽  
Huizhong Yang
Keyword(s):  
Frequency Domain ◽  
Iterative Learning Control ◽  
State Feedback ◽  
Learning Control ◽  
Iterative Learning ◽  
Injection Velocity ◽  
Linear Matrix ◽  
Finite Frequency ◽  
Repetitive Processes ◽  
Linear Repetitive Processes

This paper studies a robust iterative learning control design for discrete linear repetitive processes in the finite frequency domain. Firstly, the state-space model of the iterative learning process is deduced. Then the dynamic performance condition of the control system in the finite frequency domain is derived by combining it with the stability theory of discrete linear repetitive processes. The system performances in the finite frequency domain are then transformed into the corresponding solutions of the linear matrix inequality by using the generalised KYP lemma. Finally, an integrated state feedback PD-type iterative learning control strategy is proposed. The robust control problem with norm-bounded uncertainty and convex polyhedral uncertainty are also considered in this paper. The simulation of the injection velocity in injection molding verified that the proposed methods in this paper are more effective than the P-type state feedback iterative learning control algorithm.

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