Fuzzy logic in auto-tuning of fractional PID and backstepping tracking control of a differential mobile robot

Bernardes, Nemerson D.; Castro, Felipe A.; Cuadros, Marco Antônio de Souza Leite; Salarolli, Pablo F.; Almeida, Gustavo Maia de; Munaro, Celso J.

doi:10.3233/jifs-181431

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Fuzzy logic is usually used to achieve the self-tuning property, such a FO-PI selftuned controller is presented in [60], in a differential mobile robot. Three different types of controllers are evaluated and compared to a classical controller, with its parameters being acquired through traditional methods.…”

Section: Applications and Self-tuned Fo-pidsmentioning

confidence: 99%

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

et al. 2022

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The scientific community has recently seen a fast-growing number of publications tackling the topic of fractional-order controllers in general, with a focus on the fractional order PID. Several versions of this controller have been proposed, including different tuning methods and implementation possibilities. Quite a few recent papers discuss the practical use of such controllers. However, the industrial acceptance of these controllers is still far from being reached. Autotuning methods for such fractional order PIDs could possibly make them more appealing to industrial applications, as well. In this paper, the current autotuning methods for fractional order PIDs are reviewed. The focus is on the most recent findings. A comparison between several autotuning approaches is considered for various types of processes. Numerical examples are given to highlight the practicality of the methods that could be extended to simple industrial processes.

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Section: Applications and Self-tuned Fo-pidsmentioning

confidence: 99%

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

et al. 2022

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“…The reason behind this high popularity belongs to the facility of design and hardware implementation, with an acceptable performance obtained from this controller [24]. The appearance of the FOPID controller made a remarkable attention from many investigators, due to its over performance compared to the traditional PID controller [25][26][27][28][29][30]. In comparison to the integer-order PID controller, which has just three parameters to be tuned, five parameters, namely Kp, Ki, Kd, λ, and µ, can give better designed controllers, in terms of faster in time response and less overshoot [25,30].…”

Section: Introductionmentioning

confidence: 99%

Improved Path Tracking Control in Mobile Robots Using a Hybrid FOPID Controller with Backstepping Technique: An Experimental Study

Euldji¹,

Rebhi²,

Alkhafaji³

et al. 2023

JESA

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This study aims to address the challenge of low-cost hardware implementation of a combined backstepping with fractional order PID (FOPID) controller for mobile robots in real-time applications. Moreover, this work proposes a self-designed mobile robot prototype that is easy to realize, low in cost, spares time, and reduces human effort. This robot platform was equipped with two DC motors with quadratic encoders and two passive wheels, controlled by an Arduino mega, where the software code was developed in the Matlab-Simulink environment, using Simulink support package for Arduino. Four case studies were conducted to demonstrate the effectiveness of the suggested methodology. Experimental results demonstrate improved trajectory tracking performance with less tracking error and smooth control efforts, and is capable of handling trajectories with continuous and non-continuous gradients.

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“…In recognizing the above-mentioned limitation of traditional PID controllers, many researchers have provided methods for auto-tuning PID parameters (Amoura et al, 2016 ; De Keyser et al, 2016 ; Concha et al, 2017 ; Live et al, 2017 ; Mendes et al, 2017 ; Wang, 2017 ; Bernardes et al, 2019 ). In which, using the fuzzy neural network to tune PID parameter controllers has attracted the attention of many researchers.…”

Section: Introductionmentioning

confidence: 99%

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Park

Quynh

et al. 2021

Front. Neurorobot.

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This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

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Fuzzy logic in auto-tuning of fractional PID and backstepping tracking control of a differential mobile robot

Cited by 7 publications

References 18 publications

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

Improved Path Tracking Control in Mobile Robots Using a Hybrid FOPID Controller with Backstepping Technique: An Experimental Study

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

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