A Class of PType Iterative Learning Control Schemes for DiscreteTime Systems with Multiple Time Delays

2006 ◽  
Author(s):  
Hansheng Wu ◽  
Kimihisa Kawabata ◽  
Hiroko Kawabata
Keyword(s):  
Iterative Learning Control ◽  
Time Delays ◽  
Learning Control ◽  
Iterative Learning ◽  
Multiple Time ◽  
Multiple Time Delays ◽  
Control Schemes

PD-Type ILC Algorithm Research with Forgetting Factor for a Class of Linear Systems with Multiple Time Delays

2012 ◽  
Vol 220-223 ◽  
pp. 1125-1130 ◽  
Author(s):  
Hong Yang ◽  
Sheng Ming Li
Keyword(s):  
Linear Systems ◽  
Iterative Learning Control ◽  
Time Delays ◽  
Learning Control ◽  
Iterative Learning ◽  
Multiple Time ◽  
Forgetting Factor ◽  
Convergence Conditions ◽  
Multiple Time Delays ◽  
Study Results

Based on iterative learning control (ILC) algorithm with forgetting factor, the thought that the forgetting factor is a function of iteration numbers is proposed in this paper, which has simplified the convergence conditions. And the convergence analysis is given. Then, the study results of this paper are applied to a class of linear systems with multiple time delays and simulation results show that, under the improvements of the convergence conditions and the reasonable choice of forgetting factor function, the PD-type iterative learning control algorithm with forgetting factor applied to the linear systems with multiple time delays in this paper has effectiveness and superiority.

scholarly journals Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Zhuoyan Gao ◽  
JinRong Wang ◽  
Yong Zhou
Keyword(s):  
Iterative Learning Control ◽  
Time Delays ◽  
Iterative Learning ◽  
Multiple Time ◽  
Weighted Norm ◽  
Differential Systems ◽  
Numerical Examples ◽  
Multiple Time Delays ◽  
Two Parameters ◽  
Theoretical Results

We address existence and Ulam-Hyers and Ulam-Hyers-Mittag-Leffler stability of fractional nonlinear multiple time-delays systems with respect to two parameters’ weighted norm, which provides a foundation to study iterative learning control problem for this system. Secondly, we design PID-type learning laws to generate sequences of output trajectories to tracking the desired trajectory. Two numerical examples are used to illustrate the theoretical results.

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

2020 ◽  
Vol 42 (12) ◽  
pp. 2166-2177
Author(s):  
Gaoyang Jiang ◽  
Zhongsheng Hou
Keyword(s):  
Control Problem ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Of Arrival ◽  
Multiple Time ◽  
Data Driven Approach ◽  
Along Track

Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

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