Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

Sánchez, Martínez; Castro, Juan R.; Ocegueda-Miramontes, Violeta; Cervantes, Leticia

doi:10.3390/a10030099

Cited by 17 publications

(3 citation statements)

References 31 publications

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“…Find the maximum value of the 𝑛𝑡ℎ output corresponding to the smallest component using the equation (52).…”

Section: 𝑘 ̂= 𝑎𝑟𝑔 Minmentioning

confidence: 99%

“…For the second problem, a Type 2 Fuzzy Inference System (T2FIS) was introduced into the neural network. The Type 2 fuzzy structure provides greater flexibility because it contains more tunable parameters to handle uncertain components [51][52]. Notably, the Type 2 fuzzy sets can reduce the number of samples to be computed in T2FIS [53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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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.

show abstract

“…Find the maximum value of the 𝑛𝑡ℎ output corresponding to the smallest component using the equation (52).…”

Section: 𝑘 ̂= 𝑎𝑟𝑔 Minmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The second challenge is dealt with by integrating a Type-2 fuzzy inference system (T2FIS) into the proposed neural network. In contrast to conventional Type-1 fuzzy sets, Type-2 fuzzy sets provide further flexibility in handling uncertainties as they contain more adjustable parameters that help to minimize the difficulty in uncertainty representation [24], [29]- [42]. As such, the employment of Type-2 fuzzy sets introduces more degrees-of-freedom into system modeling [9], [43]- [50].…”

Section: Introductionmentioning

confidence: 99%