Study on Iterative Learning Control of Mobile Robot

2015 ◽  
Vol 775 ◽  
pp. 319-323
Author(s):  
Li Ping Qu ◽  
Yong Yin Qu ◽  
Hao Han Zhou
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Problem ◽  
Forgetting Factor ◽  
Robot Trajectory ◽  
Tracking Process

In order to solve the mobile robot trajectory tracking problem better, an iterative learning control (ILC) was applied. And the efficiency of mobile robot trajectory tracking was improved. From the simulation result, ILC with forgetting factor has very good performance for solving mobile robot trajectory tracking problem, and the smooth of trajectory tracking process also improved well.

scholarly journals Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Hongbin Wang ◽  
Jian Dong ◽  
Yueling Wang
Keyword(s):  
Mobile Robot ◽  
Iterative Learning Control ◽  
Closed Loop ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Learning Control ◽  
Iterative Learning ◽  
Forgetting Factor ◽  
Output Measurement ◽  
Variable Forgetting Factor

We propose an iterative learning control algorithm (ILC) that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

Trajectory tracking of wheeled mobile robot by adopting iterative learning control with predictive, current, and past learning items

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1393-1414 ◽  
Author(s):  
Chong Yu ◽  
Xiong Chen
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Mathematical Proof ◽  
Learning Control ◽  
Iterative Learning ◽  
Wheeled Mobile Robot ◽  
State Variables ◽  
Wheeled Mobile Robots

SUMMARYIn this paper, an iterative learning control algorithm is adopted to solve the high-precision trajectory tracking issue of a wheeled mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties. The designed iterative learning control law adopts predictive, current and past learning items to drive the state variables, and input variables, and outputs variables converge to the bounded scope of their desired values. The algorithm can enhance the control performance, stability and robust characteristics. The rigorous mathematical proof of the convergence character of the proposed iterative learning control algorithm is given. The feasibility, effectiveness, and robustness of the proposed algorithm are illustrated by quantitative experiments and comparative analysis. The experimental results show that the proposed iterative learning control algorithm has an outstanding control effect on the trajectory tracking issue of wheeled mobile robots.

High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

2018 ◽  
Vol 40 (15) ◽  
pp. 4105-4114 ◽  
Author(s):  
Farah Bouakrif ◽  
Michel Zasadzinski
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Lyapunov Theory ◽  
Time Interval ◽  
Tracking Problem ◽  
Definite Sequence

This paper deals with Iterative Learning Control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A high order ILC scheme is synthetized; this controller contains the information (errors) of several iterations and not only of one iteration. It has been shown that the closed loop system (robot plus controller) is asymptotically stable, over the whole finite time interval, when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed controller scheme. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.

scholarly journals Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Dong ◽  
Bin He ◽  
Chenghong Zhang ◽  
Gang Li
Keyword(s):  
Mobile Robot ◽  
Iterative Learning Control ◽  
Closed Loop ◽  
Learning Control ◽  
Iterative Learning ◽  
State Variables ◽  
Forgetting Factor ◽  
Coupling Dynamics ◽  
Variable Forgetting Factor ◽  
Input Variables

A novel iterative learning control (ILC) algorithm for a two-wheeled self-balancing mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties is presented to resolve the trajectory tracking problem in this research. A kinematics model and dynamic model of a two-wheeled self-balancing mobile robot are deduced in this paper, and the combination of an open-closed-loop PD-ILC law and a variable forgetting factor is presented. The open-closed-loop PD-ILC algorithm adopts current and past learning items to drive the state variables and input variables, and the output variables converge to the bounded scope of their desired values. In addition, introducing a variable forgetting factor can enhance the robustness and stability of ILC. Numerous simulation and experimental data demonstrate that the proposed control scheme has better feasibility and effectiveness than the traditional control algorithm.

Trajectory Tracking of a One-Link Flexible Arm via Iterative Learning Control

2020 ◽  
Author(s):  
Michele Pierallini ◽  
Franco Angelini ◽  
Riccardo Mengacci ◽  
Alessandro Palleschi ◽  
Antonio Bicchi ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Flexible Arm
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