Neuro-fuzzy Control of a Mobile Robot

Reguii, Islem; Hassani, Imen; Rekik, Chokri

doi:10.1109/sta56120.2022.10018999

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Fuzzy inference systems have been used as adaptive controllers for robots [32][33][34][35][36], showing one of the most successful applications of fuzzy logic systems [37][38][39][40]. Naturally, neural networks have been developed in various ways to deal with the uncertainty components [41][42][43][44][45][46]. However, the limitation of the tuning parameters leads to a degradation of the controller performance [46].…”

Section: Introductionmentioning

confidence: 99%

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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Section: Introductionmentioning

confidence: 99%

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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“…The state parameters and set of rules in an FLC that an MR employs to move through dynamic obstacles are scaled up in this research using genetic algorithms. [57] Neuro-fuzzy Controller…”

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confidence: 99%

Discussion on different controllers used for the navigation of mobile robot

Raj,

Kos

2024

International Journal of Electronics and Telecommunications

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Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.

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Fault-tolerant Tracking Control for Mobile Robots Based on the Framework of Intermediate Estimator and MPC

Ying

Zhu

2023

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

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Neuro-fuzzy Control of a Mobile Robot

Cited by 4 publications

References 8 publications

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Discussion on different controllers used for the navigation of mobile robot

Fault-tolerant Tracking Control for Mobile Robots Based on the Framework of Intermediate Estimator and MPC

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