Iterative Learning Control With Switching Gain Feedback for Nonlinear Systems

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
P. R. Ouyang ◽  
B. A. Petz ◽  
F. F. Xi
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Trajectory Tracking Control ◽  
Control Scheme

Iterative learning control (ILC) is a simple and effective technique of tracking control aiming at improving system tracking performance from trial to trial in a repetitive mode. In this paper, we propose a new ILC called switching gain PD-PD (SPD-PD)-type ILC for trajectory tracking control of time-varying nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with switching gains in the iteration domain and a PD-type ILC based on the previous iteration combine together into one updating law. The proposed SPD-PD ILC takes the advantages of feedback control and classical ILC and can also be viewed as online-offline ILC. It is theoretically proven that the boundednesses of the state error and the final tracking error are guaranteed in the presence of uncertainty, disturbance, and initialization error of the nonlinear systems. The convergence rate is adjustable by the adoption of the switching gains in the iteration domain. Simulation experiments are conducted for trajectory tracking control of a nonlinear system and a robotic system. The results show that fast convergence and small tracking error bounds can be observed by using the SPD-PD-type ILC.

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.

PD-PD Type Learning Control for Uncertain Nonlinear Systems

2009 ◽  
Author(s):  
P. R. Ouyang
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Gains ◽  
Tracking Errors ◽  
Initial Errors

In this paper, a new learning control, called PD-PD type learning control, is proposed for trajectory tracking of nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with the current tracking errors and a PD type iterative learning control using the previous tracking errors are combined in the updating law. Explicit expressions have been developed for choosing the feedback control gains and the iterative learning gains, and an initial updating scheme is proposed to reduce and eliminate initial errors from iteration to iteration. It is proven that the final tracking error is guaranteed to converge toward the desired trajectory in the presence of varying uncertainty, disturbance, and initial errors. Comparing with the traditional iterative learning control, the new algorithm has potential benefits that include: fast convergence rate, more flexible choices of the learning gains, and monotonic convergence of the tracking error. The effectiveness of the proposed learning control method is demonstrated by simulation experiments. Due to the straightforward implementation and very good trajectory performance of the proposed control algorithm, it should be highly applicable to industrial systems.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

Adaptive robust iterative learning control with application to a Delta robot

2020 ◽  
pp. 095965182093853
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Derivative Type ◽  
Delta Robot ◽  
Proportional Derivative Controller

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

scholarly journals Fractional-order iterative learning control for robotic Arm-PD2Dα type

Filomat ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 1-10
Author(s):  
Bosko Cvetkovic ◽  
Mihailo Lazarevic
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Sufficient Conditions ◽  
Learning Control ◽  
Joint Space ◽  
Open Loop ◽  
Iterative Learning ◽  
Robotic Arm ◽  
Trajectory Tracking Control

In this paper, a new open-loop PD2D? type a fractional order iterative learning control (ILC) is studied for joint space trajectory tracking control of a linearized uncertain robotic arm. The robust convergent analysis of the tracking errors has been done in time domain where it is theoretically proven that the boundednesses of the tracking error are guaranteed in the presence of model uncertainty. The convergence of the proposed open-loop ILC law is proven mathematically using Gronwall integral inequality for a linearized robotic system and sufficient conditions for convergence and robustness are obtained.

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