Online Neuro-Fuzzy Controller: Design for Robust Stability

Oliveira, Everthon Souza de; Takahashi, Ricardo H. C.; Caminhas, Walmir M.

doi:10.1109/access.2020.3033496

“…Meanwhile, in [27] is presented the design of an adaptive-fuzzy control compensation strategy for direct adaptive control. Finally, in [28] is presented an Online Neuro-Fuzzy Controller (ONFC) that uses a simple structure displaying low computational cost and can control nonlinear, time-varying, and uncertain systems. A brief review of different adaptive control applications using neuro-fuzzy systems is presented below, where Fig.…”

Section: A Applications Of Adaptive Neuro-fuzzy Controlmentioning

confidence: 99%

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

Espitia

¹

,

Machón-González

²

,

Garcı́a

³

2021

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This document describes the implementation of a neuro-fuzzy adaptive system MIMO (Multiple Input Multiple Output), using two neuro-fuzzy MIMO systems: one for control and the other for identifying the plant. Under this approach, the controller is optimized, employing the model obtained during the identification of the plant that utilizes data generated from the controller's operation. In this way, the plant identification and the controller optimization is performed iteratively. The application case consists of controlling a MIMO non-linear hydraulic system fed by a pump and a three-way valve. In order to observe the controller performance various experimental configurations are considered.

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“…Yang examined Fuzzy PI based on EKF sensor speed control, optimal control of power from direct drive power system [8]. Oliveira designed online neuro-fuzzy controller robust stability [9]. Tran researched adaptive fuzzy control method for single tilt tricopter [10].…”

Section: Introductionmentioning

confidence: 99%

Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Iswanto

¹

,

Ahmad

²

2021

jrc

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Fuzzy logic is a logic with a degree of vulnerability ranging from 0 to 1. Fuzzy logic is used to convert a quantity into language. It is used as a control system because it is a versatile and simple control process that does not require complex mathematical models. The paper aimed to present a fuzzy control system implemented in a rocket tracking control system as the controller because it could work well on non-linear systems and offered convenience in program design. The fuzzy control system worked to keep the rocket on track and travel at a fixed speed. The signal from the fuzzy logic control system served to control the rocket thrust. However, the process of the fuzzy logic control system is slow and time-consuming, not proper for the one that required rapid control, such as rockets, and is not applicable for tracking ramp and parabolic signals. The fuzzy logic, therefore, was modified by adding second-order integral control. The proposed algorithm showed that, by adding second-order integral control, the rocket glided 12.78m at 12 seconds with a steady-state error of 0.78 according to the setpoint of ramp path 12 m; 10.68m at 10 seconds with a steady-state error of 0.68 according to the setpoint of ramp path 10m; and 4.689m at 4 seconds with a steady-state error of 0.689 according to the setpoint of ramp path 4m. In accordance with the parabolic path, the rocket glided 15.47m at the 4th minute with 0 steady-state error.

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Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Iswanto

¹

,

Ahmad

²

2021

jrc

View full text Add to dashboard Cite

Fuzzy logic is a logic with a degree of vulnerability ranging from 0 to 1. Fuzzy logic is used to convert a quantity into language. It is used as a control system because it is a versatile and simple control process that does not require complex mathematical models. The paper aimed to present a fuzzy control system implemented in a rocket tracking control system as the controller because it could work well on non-linear systems and offered convenience in program design. The fuzzy control system worked to keep the rocket on track and travel at a fixed speed. The signal from the fuzzy logic control system served to control the rocket thrust. However, the process of the fuzzy logic control system is slow and time-consuming, not proper for the one that required rapid control, such as rockets, and is not applicable for tracking ramp and parabolic signals. The fuzzy logic, therefore, was modified by adding second-order integral control. The proposed algorithm showed that, by adding second-order integral control, the rocket glided 12.78m at 12 seconds with a steady-state error of 0.78 according to the setpoint of ramp path 12 m; 10.68m at 10 seconds with a steady-state error of 0.68 according to the setpoint of ramp path 10m; and 4.689m at 4 seconds with a steady-state error of 0.689 according to the setpoint of ramp path 4m. In accordance with the parabolic path, the rocket glided 15.47m at the 4th minute with 0 steady-state error.

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Online Neuro-Fuzzy Controller: Design for Robust Stability

Cited by 3 publications

References 14 publications

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

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