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

Yovchev, Kaloyan

doi:10.1515/jtam-2017-0016

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…This new solution will be based over the BEA method, where the learning update law will be altered in order to allow the executed output trajectory to be into the whole area defined by the state space constraints. According to the conclusion of our previous research [28] this will lead to a faster convergence rate.…”

Section: Introductionmentioning

confidence: 86%

“…Our previous research [28] investigates how the BEA parameters influence the convergence rate of the ILC procedure. This research proposes how the BEA parameters should be selected for achieving optimal convergence rate and confirmed the following statement: higher value of BEA parameter ε leads to a faster convergence rate (see Fig.…”

Section: Bounded-error Algorithm (Bea) For Ilcmentioning

confidence: 99%

“…δ * is selected to satisfy (11) based on the desired output trajectory y d . If y d is planned to pass closely to the output constraints in (10) the set Y in (12) will be highly limited, δ * defined from (11) will tend to zero and according to [28] this will increase the required number of iterations and will result in an inefficient ILC process. The overall efficiency of this method depends on the selected desired trajectory.…”

Section: Applying Ilc To Non-linear System With Constrained Outputmentioning

confidence: 99%

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Archives of Control Sciences

Yovchev

Delchev

Krastev

2020

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Iterative Learning Control (ILC) is a well-known method for control of systems performing repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for non-linear state space constrained systems. In the existing literature there is no general solution for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm (BEA) and resolves the transient growth error problem, which is a major obstacle in applying ILC to non-linear systems. Another advantage of COILC is that this method can be applied to constrained output systems. Unlike other ILC methods the COILC method employs an algorithm that stops the iteration before the occurrence of a violation in any of the state space constraints. This way COILC resolves both the hard constraints in the non-linear state space and the transient growth problem. The convergence of the proposed numerical procedure is proved in this paper. The performance of the method is evaluated through a computer simulation and the obtained results are compared to the BEA method for controlling non-linear systems. The numerical experiments demonstrate that COILC is more computationally effective and provides better overall performance. The robustness and convergence of the method make it suitable for solving constrained state space problems of non-linear systems in robotics.

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Section: Introductionmentioning

confidence: 86%

Section: Bounded-error Algorithm (Bea) For Ilcmentioning

confidence: 99%

Section: Applying Ilc To Non-linear System With Constrained Outputmentioning

confidence: 99%

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Archives of Control Sciences

Yovchev

Delchev

Krastev

2020

Self Cite

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“…As suggested in [ [13] and [14], parameters are chosen as following diagonal matrices: Applying the BEA algorithm with (16) parameters, the desired accuracy was obtained after 21 iterations, with maximum tracking errors: From Figures 6, 7 and 8 it can be seen that the iterations were interrupted by the violation of generalized coordinatesconstraints, while on Figure9 duration of iterations can be seen individually. As the iteration duration time increases, the control system successfully decreases the tracking error and incrementally obtains more learning information as the iteration interruptions occur later in the tracking process.…”

Section: The First Set Of Parameters -Bea and Comentioning

confidence: 99%

State space constrained iterative learning control for 3DOF robotic manipulator

Dubonjac¹,

Lazarević²

2021

FME Transactions

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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.

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Iterative Learning Control for Precise Trajectory Tracking Within a Constrained Workspace

Yovchev

2018

Advances in Service and Industrial Robotics

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

Cited by 4 publications

References 7 publications

Archives of Control Sciences

Archives of Control Sciences

State space constrained iterative learning control for 3DOF robotic manipulator

Iterative Learning Control for Precise Trajectory Tracking Within a Constrained Workspace

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