scholarly journals High Order Feedback-Feedforward Iterative Learning Control Scheme with a Variable Forgetting Factor

10.5772/63936 ◽  
2016 ◽  
Vol 13 (3) ◽  
pp. 95 ◽  
Author(s):  
Hongbin Wang ◽  
Jian Dong ◽  
Yueling Wang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Forgetting Factor ◽  
Control Scheme ◽  
Variable Forgetting Factor

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.

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.

scholarly journals High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbin Wang ◽  
Jian Dong ◽  
Yueling Wang
Keyword(s):  
Nonlinear System ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Forgetting Factor ◽  
Trajectory Simulation ◽  
Number Of Iterations

A novel iterative learning control (ILC) algorithm is proposed to produce output curves that pass close to the desired trajectory. The key advantage of the proposed algorithm is introducing forgetting factor, which is a function of the number of iterations. Due to the forgetting factor characteristic of ILC, the proposed scheme not only stabilizes the nonlinear system with uncertainties but also weakens interference on the tracking desired trajectory. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed algorithm.

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