Repetitive control mechanism of disturbance cancellation using a hybrid regression and genetic algorithm

Shen, Pu Fun; Wen, Hao-Ping

doi:10.1016/j.ymssp.2014.12.014

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…9 shows the transient response of the output. The response curve (data with 4 categories) is stably converged [28].…”

Section: A Reduced Periodic Errors Using a Repetitive Control Law With Fuzzy Regressionmentioning

confidence: 87%

“…The repetitive control laws are demonstrated using a real case example, wherein the focus of an electron beam subjected to periodic fluctuations is improved [28] via the following transfer function: . .…”

Section: System Model With a Linear Repetitive Control Processmentioning

confidence: 99%

“…so as to obtain the parameters , , and for the transfer function [28]. The operational flow chart for the system model with linear repetitive control laws is shown in Fig.…”

Section: System Model With a Linear Repetitive Control Processmentioning

confidence: 99%

“…It is observed that data classified into four categories shows the most effective convergence value, although the number of repetitions significantly exceeds that of the others. In comparison with the reference [28], the controller performance of this approach has more tracking error reduction and shorter transient response. However, the developed controller in this study has longer computational time used for dividing data for real time applications.…”

Section: A Reduced Periodic Errors Using a Repetitive Control Law With Fuzzy Regressionmentioning

confidence: 99%

“…Based on the aforementioned arguments and the previous work by [27,28] on disturbance rejection, as well as to highlight the importance of each design framework based on the problem statement and why the proposed method is necessary to be designed for this particular system, this study developed a repetitive control process using a fuzzy regression approach, with basis function feedback to decrease tracking errors or disturbances. First, a system model using the basis function was used to compute the matched basis functions and their associated coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Repetitive Control Process for Periodic Disturbance Cancellation Using Data Classification With a Fuzzy Regression Approach

Lin

2020

IEEE Access

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The utilization of a repetitive controller to cancel periodic disturbance or noise in mechanical systems has become increasingly important for industrial applications. In this study, a repetitive control process was developed using a data classification method, with a fuzzy regression approach and basis function, to reduce tracking errors in feedback controllers. First, a system model using the basis function is illustrated to compute the matched basis functions and their associated coefficients. A real case example of improving the focus of an electron beam subjected to periodic fluctuations has been described for verification and error analysis. Next, model algorithms containing pure and fuzzy regression are introduced into a repetitive feedback control system to reduce tracking errors caused by a periodic disturbance. System output data are categorized using fuzzy inference rules as similar data forming a single group are typically more reliable than the entire output data. The fuzzy theorem approach adopts a Gaussian membership function for system output variables owing to uncertainties that arise from modeling errors, environmental noise, etc. It is determined that the repetitive control process based on data classification with a fuzzy regression approach is more effective than using a pure regression approach. Increasing the number of data classifications initially improves accuracy; however, this decreases when the number of data classifications continues to increase. The optimal root-mean-square output tracking error convergence value was determined as 10 -14.09 when the system output data was classified into four categories, demonstrating the satisfactory reduction of a periodic disturbance. Similar results were obtained using the pure regression algorithm, where the lowest averaged verification error was 2.71% for the linear basis function model, with data classified into four categories, and this corresponded to an average prediction error of 3.86%.

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“…9 shows the transient response of the output. The response curve (data with 4 categories) is stably converged [28].…”

Section: A Reduced Periodic Errors Using a Repetitive Control Law With Fuzzy Regressionmentioning

confidence: 87%

Section: System Model With a Linear Repetitive Control Processmentioning

confidence: 99%

“…so as to obtain the parameters , , and for the transfer function [28]. The operational flow chart for the system model with linear repetitive control laws is shown in Fig.…”

Section: System Model With a Linear Repetitive Control Processmentioning

confidence: 99%

Section: A Reduced Periodic Errors Using a Repetitive Control Law With Fuzzy Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations