scholarly journals Welding Process Tracking Control Based on Multiple Model Iterative Learning Control

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xiaoli Li ◽  
Jian Liu ◽  
Linkun Wang ◽  
Kang Wang ◽  
Yang Li
Keyword(s):  
Tracking Control ◽  
Iterative Learning Control ◽  
Welding Process ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Multiple Model ◽  
Gas Tungsten Arc ◽  
Multiple Model Adaptive Control ◽  
Precision Tracking

The welding of the same parts has same welding trajectory, so welding process has strong repeatability. In this paper, aiming at the repeatability of welding process, an iterative learning controller is designed to achieve the control of weld quality. Due to the extremely variable welding environment and the presence of noise interferences and load disturbances, it is easy to cause the jumping change in parameters and even the structure of the welding system. Therefore, the idea of multiple model adaptive control (MMAC) is introduced into iterative learning control (ILC), and a multiple model iterative learning control (MMILC) algorithm is designed according to model of weld pool dynamic process in gas tungsten arc welding (GTAW). Besides, the convergence of the algorithm is analyzed for two cases: fixed parameters and jumping parameters. It turns out that the MMILC can not only utilize the repetitive information effectively in the welding process to achieve high precision tracking control of weld seam in limited time interval, but also realize the multiple model switching according to different working conditions to improve the welding quality.

scholarly journals A PD-Type Iterative Learning Control for a Class of Switched Discrete-Time Systems with Model Uncertainties and External Noises

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Yang
Keyword(s):  
Discrete Time ◽  
Switched Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Monotone Convergence ◽  
Model Uncertainties ◽  
Time Systems

A PD-type iterative learning control algorithm is applied to a class of linear discrete-time switched systems for tracking desired trajectories. The application is based on assumption that the switched systems repetitively operate over a finite time interval and the switching rules are arbitrarily prespecified. By taking advantage of the super-vector approach, a sufficient condition of the monotone convergence of the algorithm is deduced when both the model uncertainties and the external noises are absent. Then the robust monotone convergence is analyzed when the model uncertainties are present and the robustness against the bounded external noises is discussed. The analysis manifests that the proposed PD-type iterative learning control algorithm is feasible and effective when it is imposed on the linear switched systems specified by the arbitrarily preset switching rules. The attached simulations support the feasibility and the effectiveness.

Analysis of iterative learning control for an oscillating control system with two delays

2017 ◽  
Vol 40 (6) ◽  
pp. 1757-1765 ◽  
Author(s):  
Chengbin Liang ◽  
JinRong Wang
Keyword(s):  
Control System ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Reference Trajectory ◽  
Output State ◽  
Control Functions ◽  
Input Control ◽  
Two Delays

In order to track the desired reference trajectory from an oscillating control system with two delays in a finite time interval, we design iterative learning control updating laws to generate a sequence of input control functions such that the error between the output and the desired reference trajectories tends to zero via a suitable norm in the sense of uniform convergence. Here, we adopt a delayed matrix function to characterize the output state, which can be easily solved in the simulation. As a result, convergence analysis results are given. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.

D-type iterative learning control without resetting condition for robot manipulators

Robotica ◽  
2011 ◽  
Vol 29 (7) ◽  
pp. 975-980 ◽  
Author(s):  
Farah Bouakrif
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Initial State ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Topological Measure

SUMMARYThis 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 D-type ILC is presented with an initial condition algorithm, which gives the initial state value in each iteration automatically. Thus, the resetting condition (the initial state error is equal to zero) is not required. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of this control scheme, over the whole finite time-interval, when the iteration number tends to infinity. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

Iterative learning control for a distributed cloud robot with payload delivery

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiehao Li ◽  
Shoukun Wang ◽  
Junzheng Wang ◽  
Jing Li ◽  
Jiangbo Zhao ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Robot System ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Motion Models ◽  
Distributed Cloud

Purpose When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios. Design/methodology/approach Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm. Findings Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm. Originality/value This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

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