Integration of an Empirical Mode Decomposition Algorithm With Iterative Learning Control for High-Precision Machining

Tsai, Meng-Shiun; Yen, Chung-Liang; Yau, Hong-Tzong

doi:10.1109/tmech.2012.2194162

Cited by 28 publications

(7 citation statements)

References 25 publications

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“…The error signals at every learning repetition were analyzed by looking the IMFs through the EMD [10]. The correlation coefficient for investigating the contribution or correlation of each IMF to the error history rejection was studied by [11] and [12]. Learnable error signals through bandwidth tuning mechanism will be adaptively injected into learning control law and reduce the tracking error.…”

Section: Conventionalmentioning

confidence: 99%

Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion

Huang

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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This paper utilized the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, Iterative Learning Control (ILC) and the bandwidth of zero-phase Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and try to learn the error signals. However the learnable and unlearnable error signals should be separated for bettering control process as repetition goes. Producing high frequency error condition should be avoided before the phase margin caused any trouble. The filter bandwidth should be changed at every repetition. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as repetition goes. The correlation coefficient validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in next repetition. The learnable error signals of the Intrinsic Mode Functions (IMFs) through the Empirical Mode Decomposition (EMD) correlate efficiently with reduced tracking error as repetition goes. Simulation results validate the application for positioning of a robot arm system for high precision motion control.

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Section: Conventionalmentioning

confidence: 99%

Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion

Huang

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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“…The physical model-driven forecasting methods need the specific structure of the physical model and corresponding parameters to be set in advance, while it is always difficult for complex control systems to establish accurate physical models. Consequently, physical model driven methods such as iterative control method [1]and its improvement [2] are subject to many restrictions in practical applications. The other type of methods to solve the above problems are the data-driven prediction methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Prediction of Feed System Time Series

Shen

Wang

Liu

2021

Proceedings of CECNet 2021

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In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

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“…In order to further improve the contour tracking of CCC, especially in mass production, the strategy of cross iteration learning coordination control (CCILC) was developed, which combines iterative learning control (ILC) and CCC [22]. A novel algorithm that integrates ILC with empirical mode decomposition (EMD) was proposed to improve learning process [23].…”

Section: Introductionmentioning

confidence: 99%

Reference Trajectory Modification Based on Spatial Iterative Learning for Contour Control of Two-Axis NC Systems

Wang

et al. 2020

IEEE/ASME Trans. Mechatron.

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Integration of an Empirical Mode Decomposition Algorithm With Iterative Learning Control for High-Precision Machining

Cited by 28 publications

References 25 publications

Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion

Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion

Multi-Stage Prediction of Feed System Time Series

Reference Trajectory Modification Based on Spatial Iterative Learning for Contour Control of Two-Axis NC Systems

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