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

Abstract

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 control of SCARA parallel robot based on fuzzy adaptive iterative learning algorithm

“…In order to suppress aperiodic external disturbance, an ILC algorithm with forgetting factor is proposed, in which the designed forgetting factor can be regarded as a filter, thus effectively reducing the accumulation of disturbance on the iteration axis. However, it also leads to the error can only converge to a certain range [7][8] . The controller parameters are adjusted in real time through fuzzy rules to resist external disturbances, which requires less parameter adjustment than the classic PD-ILC [9] .Combined with neural network, a high-order ILC is proposed.…”

Discrete open-closed-loop PID-type iterative learning control for trajectory tracking of tracked mobile robots

“…1. Most of related works regarding the combination of PID idea and ILC algorithm only considered the combination of some parts of each 16,17,19 , and focused on the continuous time system rather than discrete-time system. 18 2.…”

“…Kang et al 16 presented an open-closed-loop P-type ILC algorithm to enhance the path-tracking precision of nonholonomic mobile robots with state disturbances, but the convergence speed and relative stability of the algorithm can be improved further. Wang et al 17 extended the work by adding a variable forgetting factor to improve the robustness and stability of the algorithm. However, the value of the variable forgetting factor must be small enough for the system to obtain a better tracking performance.…”

“…Through the analysis of relevant literature, the technical challenges for trajectory tracking of TMRs can be summarized as follows: Most of related works regarding the combination of PID idea and ILC algorithm only considered the combination of some parts of each 16,17,19 , and focused on the continuous time system rather than discrete-time system. 18 Most of the literature focuses on the trajectory tracking issues of wheeled mobile robots (WMRs), 20,21 autonomous underwater vehicles (AUVs), 22,23 and unmanned air vehicles (UAVs).…”

Design Hybrid Iterative Learning Controller for Directly Driving the Wheels of Mobile Platform against Uncertain Parameters and Initial Errors

“…Then, researchers [36] added the PID coefficient in the iterative learning controller to improve the effectiveness of arc-shaped trajectory tracking. At the same time, researchers [37] changed the forgetting factor to reduce the correction of the error parameters at the initial moment of each iteration process.…”