Research on dual‐mode switching fuzzy PID servo algorithm based on micro‐linear motor

Sun, Hongchang; Ma, Ruijun; Deng, Sangpeng; Li, Lina; Zhang, Jian; Qi, Yuming; Feng, Yuanjing; Zhang, Tianjiang

doi:10.1049/joe.2018.9150

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…The adaptive fuzzy PID control algorithm adopted in this system establishes control rules through the fuzzy relationship between the system temperature deviation e , deviation change rate ec and three related parameters K p , K i and K d of PID control algorithm [ 34 , 35 ].

where the △ K p is the correction of the proportional coefficient; △ K p is the correction of the integral coefficient; △ K p is the correction of the differential coefficient.…”

Section: Monitoring and Control Principlementioning

confidence: 99%

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Zhang

Min

2022

Computational Intelligence and Neuroscience

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Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.

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where the △ K p is the correction of the proportional coefficient; △ K p is the correction of the integral coefficient; △ K p is the correction of the differential coefficient.…”

Section: Monitoring and Control Principlementioning

confidence: 99%

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Zhang

Min

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…With the ability to adjust the control according to the changing environment, enhance the robustness of the control system, anti-interference, etc. [11][12][13][14][15][16][17]. But its complex calculation will increase the amount of calculation of the control system, affect the stability of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi‐Algorithm Switching Hybrid Control of Permanent Magnet Synchronous Motor for Electric Special Vehicles

Wu,

Liu

2023

IEEJ Transactions Elec Engng

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In order to improve the smoothness of the electric special vehicle (ESV) under each operational condition, a multi‐algorithm switching hybrid control (MASHC) algorithm is proposed for the permanent magnet synchronous motor (PMSM) of ESV. For a steady–state operation state, the transfer function structure of the motor control system is optimized, avoiding its output variables from varying with the integral and differential changes of the input quantity to enhance the stability and response speed of the control system. For transient operation states, it is considered to avoid the torque ripple that can be generated due to the increased computational effort of introducing an intelligent algorithm. The fuzzy proportional (FP) control algorithm that can replicate the transient performance of the fuzzy control algorithm is designed to improve the control accuracy of the control system. In addition, a switching function is designed to blend the two algorithms to improve the overall vehicle operation performance. To verify the effectiveness of the control algorithm, a simulation model and an experimental platform are developed. Both simulation and experimental results show that the proposed control algorithm has the advantages of no overshoot and oscillation in output, which can improve the smoothness of the ESV drive system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…it fails to realize the best controller, nonlinearity of the system such as saturation are overall not deal well. Author [13] presented the effectiveness of fuzzy PID controller to control the speed and dynamic of the framework, the planned controller utilized algorithm (dual-mode switching) which is capable of adjusting to the effect of interference. Fractional order PID (FOPID) controller is used instead of PID controller in [14][15][16], where integral-derivative order integer turns to fractional.…”

Section: Introductionmentioning

confidence: 99%

Speed control of an SPMSM using a tracking differentiator-PID controller scheme with a genetic algorithm

Hadi

Ibraheem

2021

IJECE

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In this paper, a tracking differentiator-proportional integral and derivative (TD-PID) control scheme is proposed to control the speed of a surface mount permanent magnet synchronous motor (SPMSM). The TD is used to generate the necessary transient profile for both the reference and the output speed, which are compared with each other to produce the error signals that feed into the PID controller. In addition to the TD unit parameters, the PID controller’s parameters are tuned to achieve the optimum new multi-objective performance index, comprised of the integral of the time absolute error (ITAE), the absolute square of the control energy signal (USQR), and the absolute value of the control energy signal (UABS) and utilizing a genetic algorithm (GA). A nonlinear model of the SPMSM is considered in the design and the performance of the proposed TD-PID scheme was validated by comparing its performance with that of a traditional PI controller in a MATLAB environment. Different case studies were tested to show the effectiveness of the proposed scheme, results including peak overshoot, energy consumption, control signal chatter, and 30% improvement in the OPI, with variable reference speeds, load torque, and parameters uncertainties. Illustrate the proposed scheme's success compared with PI controller.

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Research on dual‐mode switching fuzzy PID servo algorithm based on micro‐linear motor

Cited by 9 publications

References 9 publications

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Research on Multi‐Algorithm Switching Hybrid Control of Permanent Magnet Synchronous Motor for Electric Special Vehicles

Speed control of an SPMSM using a tracking differentiator-PID controller scheme with a genetic algorithm

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