scholarly journals State space constrained iterative learning control for 3DOF robotic manipulator

2021 ◽  
Vol 49 (2) ◽  
pp. 429-436
Author(s):  
Aleksandar Dubonjac ◽  
Mihailo Lazarević
Keyword(s):  
State Space ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Tracking Accuracy ◽  
Trajectory Error ◽  
System Parameters ◽  
Bounded Error ◽  
Learning Parameter ◽  
Parameter Values ◽  
Number Of Iterations

In this paper, the trajectory tracking problem of a nonlinear robotic system with 3DOFs under the control signal obtained through nonlinearly constrained state spaceIterative Learning Control (ILC) methods is considered. The focus of this paper is the analysis of different control system parameters on the convergence rate of two constrained state space ILCalgorithms: Bounded Error Algorithm (BEAILC) and Constrained Output algorithm (COILC), as well as the comparison between these two algorithms through simulations. The obtained results have shown that COILC algorithm converges faster than BEAILC algorithm when compared with the same learning and feedback parameters, due to lower trajectory restrictions. Also, it has been shown that an increase in feedback gains can decrease the number of iteration terminations during the learning process, thus allowing for more of the trajectory error information to be learned from during the single iteration. Moreover, simulations have shown that the decrease in learning parameter values will increase the number of iterations required to obtain the desired tracking accuracy.

scholarly journals A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Xuewei Fu ◽  
Xiaofeng Yang ◽  
Zhenyu Chen
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Learning Control ◽  
Inverse Model ◽  
Iterative Learning ◽  
Compensation Method ◽  
Tracking Accuracy ◽  
Linear Motors ◽  
Learning Speed ◽  
Force Ripple

Permanent magnet linear motors (PMLMs) are gaining increasing interest in ultra-precision and long stroke motion stage, such as reticle and wafer stage of scanner for semiconductor lithography. However, the performances of PMLM are greatly affected by inherent force ripple. A number of compensation methods have been studied to solve its influence to the system precision. However, aiming at some application, the system characteristics limit the design of controller. In this paper, a new compensation strategy based on the inverse model iterative learning control and robust disturbance observer is proposed to suppress the influence of force ripple. The proposed compensation method makes fully use of not only achievable high tracking accuracy of the inverse model iterative learning control but also the higher robustness and better iterative learning speed by using robust disturbance observer. Simulation and experiments verify effectiveness and superiority of the proposed method.

State Space Constrained Iterative Learning Control for Robotic Manipulators

2017 ◽  
Vol 20 (3) ◽  
pp. 1145-1150 ◽  
Author(s):  
Kaloyan Yovchev ◽  
Kamen Delchev ◽  
Evgeniy Krastev
Keyword(s):  
State Space ◽  
Iterative Learning Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning

Adaptive Iterative Learning Control for Robot Manipulators with Initial Resetting Errors

2011 ◽  
Vol 130-134 ◽  
pp. 265-269 ◽  
Author(s):  
Jian Ming Wei ◽  
Yun An Hu
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Error Function ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Unknown Parameters ◽  
Adaptive Iterative Learning Control ◽  
Suitable Parameter ◽  
Parameter Values

In this paper, an adaptive iterative learning control is presented for robot manipulators with unknown parameters, performing repetitive tasks. In order to overcome the initial resetting errors, an auxiliary tracking error function is introduced. The adaptive algorithm is derived along the iteration axis to search for suitable parameter values. The technical analysis shows convergence of the tracking errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed controller.

scholarly journals Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

2017 ◽  
Vol 47 (4) ◽  
pp. 3-11 ◽  
Author(s):  
Kaloyan Yovchev
Keyword(s):  
Computer Simulation ◽  
Iterative Learning Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Robotic Manipulator ◽  
Iterative Learning ◽  
Optimal Parameters ◽  
Manipulator Arm ◽  
Bounded Error ◽  
Optimal Values

Abstract This paper continues previous research of the Bounded Error Algorithm (BEA) for Iterative Learning Control (ILC) and its application into the control of robotic manipulators. It focuses on investigation of the influence of the parameters of BEA over the convergence rate of the ILC process. This is performed first through a computer simulation. This simulation suggests optimal values for the parameters. Afterwards, the estimated results are validated on a physical robotic manipulator arm. Also, this is one of the first reports of applying BEA into robots control.

Iterative Learning Controller Based on Particle Swarm Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 593-600
Author(s):  
Xiu Lan Wen ◽  
Hong Sheng Li ◽  
Dong Xia Wang ◽  
Jia Cai Huang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Improved Genetic Algorithm ◽  
Tracking Accuracy ◽  
Swarm Optimization

Iterative Learning Control (ILC) has recently emerged as a powerful control strategy that iteratively achieves a higher accuracy for systems with repetitive tasks. The basic idea of ILC is to construct a compensation signal based on the tracking error in each repetition so as to reduce the tracking error in the next repetition. In this paper, particle swarm optimization (PSO) is proposed to optimize the input of iterative learning controller. The experimental results confirm that the proposed method not only has higher tracking accuracy than that of Improved Genetic Algorithm (IGA) and traditional Genetic Algorithm based elisit strategy (EGA), but also has the advantages of simple algorithm and good flexibility. And compared with conventional iterative learning control methods, it is easy to solve the optimal input for non-linear plant models.

Iterative Learning Control of a Piezo-Actuated Positioning Stage for Micro/Nano Manipulation

2013 ◽  
Vol 284-287 ◽  
pp. 2080-2084
Author(s):  
Chih Jer Lin ◽  
Chih Keng Chen ◽  
Chun Ta Chen
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Experimental Result ◽  
Tracking Accuracy ◽  
Positional Accuracy ◽  
Nonlinear Hysteresis ◽  
Positioning Stage ◽  
Better Than

The main objective of this investigation is to improve the tracking accuracy of a piezo-actuated positioning stage using an iterative learning control. First, to compensate for the tracking error of the piezo-actuated positioning stage that is caused by nonlinear hysteresis, the dynamics of the hysteresis is modeled using the Bouc-Wen model. The particle swarm optimization (PSO) is used to determine the parameters of the inverse-hysteresis model. Second, the design of an iterative learning control is presented. Based on the simulation, the appropriate value of the learning rate is determined. Finally, the efficacy of the approach is demonstrated to achieve high accuracy positioning via the real-time experiments. The experimental result of the piezo-actuated positioning stage is measured by the laser interferometer (HP-5529A). The experimental results show that the iterative learning control can compensate the hysteresis-caused tracking error and the positional accuracy of better than 100 nano-meter is readily achieved.

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