Repetitive process based higher-order iterative learning control law design

For a class of single-input single-output (SISO) dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D) repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs) technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method.

Download Full-text

Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

MATEC Web of Conferences ◽

10.1051/matecconf/201929201010 ◽

2019 ◽

Vol 292 ◽

pp. 01010

Author(s):

Mihailo Lazarević ◽

Nikola Živković ◽

Darko Radojević

Keyword(s):

Iterative Learning Control ◽

Control Algorithm ◽

Closed Loop ◽

Learning Control ◽

Open Loop ◽

Iterative Learning ◽

Control Law ◽

Exact Linearization ◽

Outer Loop ◽

Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

Download Full-text

Monotonic convergence and robustness of higher‐order gain‐adaptive iterative learning control

International Journal of Robust and Nonlinear Control ◽

10.1002/rnc.4978 ◽

2020 ◽

Vol 30 (10) ◽

pp. 3960-3981

Author(s):

Xiaohui Li ◽

Xiaoe Ruan

Keyword(s):

Iterative Learning Control ◽

Learning Control ◽

Higher Order ◽

Iterative Learning ◽

Adaptive Iterative Learning Control ◽

Monotonic Convergence

Download Full-text

Lateral robust iterative learning control for unmanned driving robot vehicle

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820904834 ◽

2020 ◽

Vol 234 (7) ◽

pp. 792-808

Author(s):

Shuhua Su ◽

Gang Chen

Keyword(s):

Dynamic Model ◽

Iterative Learning Control ◽

Tracking Error ◽

Learning Control ◽

Steering System ◽

Path Tracking ◽

Iterative Learning ◽

Control Law ◽

Vehicle Dynamic ◽

Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

Download Full-text