scholarly journals Load Disturbance Conditions for Current Error Feedback and Past Error Feedforward State-Feedback Iterative Learning Control

2021 ◽  
Vol 12 (02) ◽  
pp. 65-72
Author(s):  
Athari Alotaibi ◽  
Asmaa Alkandri ◽  
Muhammad Alsubaie
Keyword(s):  
Iterative Learning Control ◽  
State Feedback ◽  
Learning Control ◽  
Iterative Learning ◽  
Error Feedback ◽  
Load Disturbance ◽  
Current Error

scholarly journals Robust Conditions for Iterative Learning Control in State Feedback and Output Injection Paradigm

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Muhammad A. Alsubaie ◽  
Mubarak KH. Alhajri ◽  
Tarek S. Altowaim ◽  
Salem H. Salamah
Keyword(s):  
Iterative Learning Control ◽  
State Feedback ◽  
Linear Time ◽  
Stability Margin ◽  
Learning Control ◽  
Iterative Learning ◽  
Delay Model ◽  
Linear Time Invariant ◽  
System Input ◽  
Linear Time Invariant Systems

A robust Iterative Learning Control (ILC) design that uses state feedback and output injection for linear time-invariant systems is reintroduced. ILC is a control tool that is used to overcome periodic disturbances in repetitive systems acting on the system input. The design basically depends on the small gain theorem, which suggests isolating a modeled disturbance system and finding the overall transfer function around the delay model. This assures disturbance accommodation if stability conditions are achieved. The reported design has a lack in terms of the uncertainty issue. This study considered the robustness issue by investigating and setting conditions to improve the system performance in the ILC design against a system’s unmodeled dynamics. The simulation results obtained for two different systems showed an improvement in the stability margin in the case of system perturbation.

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.

scholarly journals An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saleem Riaz ◽  
Hui Lin ◽  
Muhammad Waqas ◽  
Farkhanda Afzal ◽  
Kai Wang ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Position Control ◽  
Tracking Error ◽  
Learning Control ◽  
Convergence Condition ◽  
Design Criterion ◽  
Iterative Learning ◽  
Current Error ◽  
P Type

Traditional and typical iterative learning control algorithm shows that the convergence rate of error is very low for a class of regular linear systems. A fast iterative learning control algorithm is designed to deal with this problem in this paper. The algorithm is based on the traditional P-type iterative learning control law, which increases the composition of adjacent two overlapping quantities, the tracking error of previous cycle difference signals, and the current error difference. Using convolution to promote Young inequalities proved strictly that, in terms of Lebesgue-p norm, when the number of iterations tends to infinity, the tracking error converges to zero in the system and presents the convergence condition of the algorithm. Compared with the traditional P-type iterative learning control algorithm, the proposed algorithm improves convergence speed and evades the defect using the norm metric’s tracking error. Finally, the validation of the effectiveness of the proposed algorithm is further proved by simulation results.

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