scholarly journals Iterative Learning Control for AGV Drive Motor Based on Linear Extended State Observer

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 324
Author(s):  
Wei Jiang ◽  
Gang Zhu ◽  
Ying Zheng
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
State Observer ◽  
Iterative Learning ◽  
Extended State Observer ◽  
Extended State ◽  
Drive Motor ◽  
Linear Extended State Observer ◽  
Combined Algorithm ◽  
Control Accuracy

In order to solve the problems of repetitive and non-repetitive interference in the workflow of Automated Guided Vehicle (AGV), Iterative Learning Control (ILC) combined with linear extended state observer (LESO) is utilized to improve the control accuracy of AGV drive motor. Considering the working conditions of AGV, the load characteristics of the drive motor are analyzed with which the mathematical model of motor system is established. Then the third-order extended state space equations of the system approximate model is obtained, in which LESO is designed to estimate the system states and the total disturbance. For the repeatability of AGV workflow, ILC is designed to improve the control accuracy. As the goods mass transported each time is not same, the LESO is utilized to estimate the non-repetitive load disturbance in real time and compensate the disturbance of the system to improve the position precision. The convergence of the combined algorithm is also verified. Simulation and experimental results show that the proposed iterative learning control strategy based on LESO can reduce the positioning error in AGV workflow and improve the system performance.

scholarly journals Iterative Learning Control with Extended State Observer for Telescope System

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Huaxiang Cai ◽  
Yongmei Huang ◽  
Junfeng Du ◽  
Tao Tang ◽  
Dan Zuo ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Control Method ◽  
Tracking Error ◽  
Learning Control ◽  
State Observer ◽  
Iterative Learning ◽  
Extended State Observer ◽  
Extended State ◽  
Nonlinear Disturbances ◽  
Telescope System

An Iterative Learning Control (ILC) method with Extended State Observer (ESO) is proposed to enhance the tracking precision of telescope. Telescope systems usually suffer some uncertain nonlinear disturbances, such as nonlinear friction and unknown disturbances. Thereby, to ensure the tracking precision, the ESO which can estimate system states (including parts of uncertain nonlinear disturbances) is introduced. The nonlinear system is converted to an approximate linear system by making use of the ESO. Besides, to make further improvement on the tracking precision, we make use of the ILC method which can find an ideal control signal by the process of iterative learning. Furthermore, this control method theoretically guarantees a prescribed tracking performance and final tracking accuracy. Finally, a few comparative experimental results show that the proposed control method has excellent performance for reducing the tracking error of telescope system.

Sign in / Sign up

Export Citation Format

Share Document