Noncausal finite time interval iterative learning control law design

2014 ◽  
Author(s):  
Xuan Wang ◽  
Eric Rogers
Keyword(s):  
Finite Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Time Interval ◽  
Finite Time Interval

scholarly journals Stability Analysis of High-Order Iterative Learning Control for a Class of Nonlinear Switched Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xuhui Bu ◽  
Fashan Yu ◽  
Ziyi Fu ◽  
Fuzhong Wang
Keyword(s):  
Switched Systems ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Control Law ◽  
Time Interval ◽  
Nonlinear Switched Systems ◽  
The Stability

This paper considers the stability of high-order PID-type iterative learning control law for a class of nonlinear switched systems with state delays and arbitrary switched rules, which perform a given task repeatedly. The stability condition for the proposed high-order learning control law is first established, and then the stability is analyzed based on contraction mapping approach in the sense ofλnorm. It is shown that the proposed iterative learning control law can guarantee the asymptotic convergence of the tracking error for the entire time interval through the iterative learning process. Two examples are given to illustrate the effectiveness of the proposed approach.

Decentralized iterative learning control for switched large-scale systems

2018 ◽  
Vol 41 (4) ◽  
pp. 1045-1056
Author(s):  
Panpan Gu ◽  
Senping Tian ◽  
Qian Liu
Keyword(s):  
Iterative Learning Control ◽  
Large Scale ◽  
Contraction Mapping ◽  
Learning Control ◽  
Mapping Method ◽  
Iterative Learning ◽  
Control Law ◽  
Time Interval ◽  
Large Scale Systems ◽  
Numerical Examples

This paper is concerned with the iterative learning control problem for switched large-scale systems. According to the characteristics of the systems, a decentralized D-type iterative learning control law is proposed for such switched large-scale systems. The proposed controller of each subsystem relies only on local output variables, without any information exchanges with other subsystems. By using the contraction mapping method, it is shown that the algorithm can guarantee that the output of each subsystem converges to the desired trajectory over the whole time interval along the iteration axis. Finally, three numerical examples are given to illustrate the effectiveness of the proposed algorithm.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

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.

Lateral robust iterative learning control for unmanned driving robot vehicle

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.

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document