Automatic Steering Control Algorithm Based on Compound Fuzzy PID for Rice Transplanter

Yin, Junnan; Zhu, Dan; Liao, Juan; Zhu, Guangyue; Wang, Yao; Zhang, Shun

doi:10.3390/app9132666

Cited by 19 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Salah satu pengembangan tersebut dengan metode tree physiology optimization (TPO) yang diimp lementasikan [1] pada Proportional-Integral (PI). Tekn ik tuning lainnya adalah selftuning Fuzzy Logic Controller (FLC) on-line dengan mengatur fuzzy control rules [2], [3]. PID controller juga dapat di-tuning oleh FLC adaptive dengan pengaturan bentuk Membership Function (MF) [4].…”

Section: Pendahuluanunclassified

“…Sementara Operating Range (OR) dari MF input/output hanya dibatasi pada angka tertentu saja. Sehingga menjad i sebuah pemahaman yang mutlak bahwa OR dari MF input/output tidak bisa dikembangkan [3], [6], [13], [14].…”

Section: Pendahuluanunclassified

“…Hal ini dimaksudkan untuk melihat kinerja controller pada obyek yang sama. Penggabungan FLC dan PID controller [3], [19] menghasilkan performa sesuai dengan target respon transien, tetapi strukturnya berbeda. Struktur Fu zzy PID controller dalam seri susunannya masterslave [20].…”

Section: Pendahuluanunclassified

“…Fu zzy PID controller ini merupakan FLC dengan tiga input dan satu output. Struktur Fu zzy PID controller [24] leb ih sederhana dibanding model Fuzzy PID controller [3], [19] - [23]. Strukturnya memiliki input yang sama seperti PID controller.…”

Section: Pendahuluanunclassified

See 3 more Smart Citations

Metode Tuning Operating Range Fuzzy PID Controller pada Sistem Orde Tiga

Sutarna

Purwanti

2020

Jurnal Teknik Elektro

View full text Add to dashboard Cite

This research discusses the development of a model Fuzzy Proportional, Integral, Derivative (Fuzzy PID) controller models and self-tuning. This method has been implemented in the third-order system of Differential Equations, as a sample implementation of a PID controller. The methods self-tuning known are fuzzy rules, membership function (MF), and scaling factor. The focus of the discussion in this research is to introduce self-tuning to the operating range (OR) setting of MF. Previous research has succeeded in converting a PID controller to a Fuzzy Logic Controller (FLC) which is in accordance with the PID structure. The FLC has three inputs and one output as in the PID controller, hereinafter referred to as Fuzzy PID controller. Knowledgebase on the FLC structure of three inputs one output is expressed in the form of cubic fuzzy associative memory (FAM). The conversion was done by mapping errors, integrals error, derivative errors and outputs of PID controller into the OR of MF Fuzzy input/output. The size of the OR conversion value on the MF fuzzy input was set, so the response transient is set-to-point. While the OR value of the MF fuzzy output was fixed as a limitation. Improved settling time was reached up to 75.3% and percent overshoot was reduced by 57.7% in Fuzzy PID with PID controller. The output signal from the Fuzzy PID controller showed the smallest amplitude of 24.12, while the PID controller was 32.53. The amplitude unit depends on the Fuzzy PID controller parameters when it applied the real plant. The third-order Fuzzy PID controller has been successfully simulated in Simulink/Matlab.

show abstract

Section: Pendahuluanunclassified

See 2 more Smart Citations

Metode Tuning Operating Range Fuzzy PID Controller pada Sistem Orde Tiga

Sutarna

Purwanti

2020

Jurnal Teknik Elektro

View full text Add to dashboard Cite

show abstract

“…The lateral controller controls the driverless bus to track a desired path. Some model-free methods, such as proportional-integral-derivative (PID) [7][8][9], fuzzy logic control (FLC) [10,11], and sliding model control (SMC) [12][13][14], are applied. Those algorithms perform well even when the model of the driverless bus is vague.…”

Section: Introductionmentioning

confidence: 99%

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yan

Kuang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

show abstract