An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

He, Changjiang; Mahfouf, Mahdi; Torres-Salomao, L. A.

doi:10.1109/thms.2020.3027531

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…In addition, uncertainty analysis has become crucially important in recent years, as the quantification of model uncertainty brings insights of model interpretability and robustness. We will explore the solutions of integrating the proposed HIP-ML model and uncertainty analysis methods, for example, the cloud-NARX model [40], fuzzy model [49].…”

Section: Discussionmentioning

confidence: 99%

Hybrid interpretable predictive machine learning model for air pollution prediction

Meng

2022

Neurocomputing

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Section: Discussionmentioning

confidence: 99%

Hybrid interpretable predictive machine learning model for air pollution prediction

Meng

2022

Neurocomputing

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“…Moreover, the Gaussian-based shape for primary MF with uncertain values of mean and standard deviation, which have an interval type-2 secondary MF, could be named an interval type-2 Gaussian MF [35][36][37][38]. The mathematical expression of an interval type-2 Gaussian MF with the uncertainties in mean 𝑚 and standard deviation 𝜎 is performed as…”

Section: Fundamentals Of Interval Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Ngo

Nguyеn

2022

Journal of Robotics and Control

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Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation.

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“…Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87]. Of course, the application of interval type-2 fuzzy logic in the domain of control has recently attracted a lot of attention due to its better performance under uncertain conditions.…”

Section: Number Of Output Fuzzy Membership Functionsmentioning

confidence: 99%

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

2022

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This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

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An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Abstract: This is a repository copy of An adaptive general type-2 fuzzy logic approach for psychophysiological state modeling in real-time human-machine interfaces.

Cited by 7 publications

References 34 publications

Hybrid interpretable predictive machine learning model for air pollution prediction

Hybrid interpretable predictive machine learning model for air pollution prediction

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

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