Interval-Type 3 Fuzzy Differential Evolution for Designing an Interval-Type 3 Fuzzy Controller of a Unicycle Mobile Robot

Peraza, Cinthia; Ochoa, Patricia; Castillo, Oscar; Geem, Zong Woo

doi:10.3390/math10193533

Cited by 18 publications

(9 citation statements)

References 36 publications

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“…Recently, one of the most used control cases to test the efficiency of some method has been that of trajectory-following of a mobile robot [37]. The mathematical model of this robot can be found in [38,39].…”

Section: General Type 2 Fuzzy Controllermentioning

confidence: 99%

Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

Ochoa

Peraza

Castillo

et al. 2023

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The papers using fuzzy logic have generated continuous improvements for applications, and an example of this is the use of generalized type 2 fuzzy systems in real-world problems. The key idea of this paper is to present a generalized type 2 fuzzy system for augmenting differential evolution with dynamic parameter variation in order to enhance its performance and convergence. A generalized type 2 Sugeno controller was implemented with the aim of enhancing the trajectory of a robot, and an analysis of the execution time and the errors obtained by ITAE, IAE, ITSE, ISEV, RMSE, and MSE is presented. In addition, a comparison with different levels of disturbance applied to the controller was performed with the goal of demonstrating the efficiency of a type 2 fuzzy system.

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Section: General Type 2 Fuzzy Controllermentioning

confidence: 99%

Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

Ochoa

Peraza

Castillo

et al. 2023

Axioms

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“…In [26], an observer is suggested to identify the modeling error of T3-FLSs, and then fault-tolerant controller is designed. In [27] a methodology is suggested for improving the accuracy of the differential evolution approach in designing an optimal T3-FLS for controlling a MR. The methodology is tested on a type-3 Sugeno controller with different noise levels, and the efficacy is compared to other studies in the literature.…”

Section: A Reviewmentioning

confidence: 99%

Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model

Wu,

Huang,

Wang

et al. 2023

IEEE Access

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The paper studies the control of wheeled land mobile robots (MRs) using nonlinear equations and non-holonomic dynamic constraints. Due to the complex and unpredictable nature of the environments in which these robots operate, designing a controller for them is a challenging task. Uncertainties in the system further compound the problem. To tackle these challenges, this paper proposes a novel approach based on type-3 (T3) fuzzy logic systems (FLSs) for system identification and parameter estimation. The T3-FLSs are used to create an online model of the MRs dynamics, which is then used to design a modelbased control system. To account for the approximation error of T3-FLSs and the effect of un-modeled dynamics and constraints, an optimal supervisor is designed. The supervisor compensates for any error in the model and ensures that the control system remains stable under symmetrical constraints. A Lyapunov analysis is conducted to verify the stability of the system. The simulations demonstrate that the proposed controller yields excellent results even in the presence of non-holonomic constraints and fully unknown dynamics. The findings of this study offer significant insights into the challenges associated with controlling MRs and provide a promising solution to address these issues.

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“…The algorithm can be applied, as we previously mentioned, to robots, microsystems, sensors, devices, etc., in the optimization of the parameters of their models that are being experimented upon. The proposed algorithm can be used in the optimization of the architecture of a neural network or in the parameters of the membership functions of a fuzzy logic system; as we have seen in other articles [ 24 , 25 , 26 , 27 ], this type of experimentation with the DMOA is the subject of a future work that we plan to undertake in due course…”

Section: Introductionmentioning

confidence: 99%

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

et al. 2023

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Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics, piezoelectricity, motors, biosensors, 3D printing, etc.

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Interval-Type 3 Fuzzy Differential Evolution for Designing an Interval-Type 3 Fuzzy Controller of a Unicycle Mobile Robot

Cited by 18 publications

References 36 publications

Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

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