A neural network fractional order PID controller for FOLPD process

Shan, Wenjuan; Tang, Wei

doi:10.1109/chicc.2016.7555013

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…where K p , K i and K d are the corresponding PID control parameters. Combined with fractional calculus, the PID can be extended to fractional-order PID (PI λ D µ ) [30,31]. For FNN, the update law of the weight w ji becomes…”

Section: Fractional-order Pid Control In Backpropagation Diagramsmentioning

confidence: 99%

A Survey of Fractional-Order Neural Networks

Zhang

Chen

2017

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

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In this paper, the literature of fractional-order neural networks is categorized and discussed, which includes a general introduction and overview of fractional-order neural networks. Various application areas of fractional-order neural networks have been found or used, and will be surveyed and summarized such as neuroscience, computational science, control and optimization. Recent trends in dynamics of fractional-order neural networks are presented and discussed. The results, especially the stability analysis of fractional-order neural networks, are reviewed and different analysis methods are compared. Furthermore, the challenges and conclusions of fractional-order neural networks are given.

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Section: Fractional-order Pid Control In Backpropagation Diagramsmentioning

confidence: 99%

A Survey of Fractional-Order Neural Networks

Zhang

Chen

2017

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

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“…Research activities are focused on optimizing FOPI controllers to achieve the desired performance specified in both time‐domain and frequency‐domain . Many efforts have been made to develop parameter tuning methods for the FOPI controller as an extension of classical control theory, including evolutionary algorithm , neural network algorithm , Bode shaping‐based design methods , model reference control method , and fuzzy approach . These tuning techniques require the system model information to update the control parameters, like dynamic linear models and transfer function models .…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fractional Order PI Controller Design for a Flexible Swing arm System Via Enhanced Virtual Reference Feedback Tuning

Xie

Tang

Zheng

et al. 2017

Asian Journal of Control

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Flexible swing arm system (FSAS) is one of the most important components in the LED packaging industry. The trajectory tracking performance of the FSAS will directly affect the efficiency and accuracy of the LED packaging equipment. In order to meet the high precision and high speed requirements, this paper proposes an adaptive fractional order proportional integral (AFOPI) control method based on enhanced virtual reference feedback tuning (EVRFT). In this method, the AFOPI controller is applied to handle the fractional order characteristics of the FSAS. EVRFT is used to tune the AFOPI controller in a real-time way to accommodate the time-varying operating conditions. The proposed method is facilitated with two advantages: 1) only input/output measured data are fully utilized during the recursive tuning process without using model information of the controlled FSAS; 2) an improved adaptive law is incorporated in EVRFT to reduce the computation burden and provide an unbiased estimate for the ideal controller simultaneously. Thus, the conventional VRFT is enhanced both in efficiency and accuracy. The stability of the proposed method is guaranteed by rigorous theoretical analysis. Finally, experimental results are presented to verify the effectiveness of the EVRFT-based AFOPI controller.

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